· . .. I LI0T
HIERDIE EKSEMPLAAR MAG ONDER
GEEN OMSTANDIGHEDE UIT DIE
~~BIBLIOTEEK VERWYDER WORD NIE,
University Free State
11111111111111111111111111111111111111111111111111111111111111111111111111111111
34300001922156
Universiteit Vrystaat
MORPHO-AGRONOMICAL AND MOLECULAR MARKER BASED GENETIC
DIVERSITY ANALYSES AND QUALITY EVALUATION OF SORGHUM
[Sorghum bicotor (L.) Moeneh] GENOTYPES
by
Nemera Geleta Shargie
A dissertation submitted in accordance with
the academic requirements for the degree
of
Philosophiae Doctor
in the
Department of Plant Sciences (Plant Breeding)
Faculty of Natural and Agricultural Sciences at the
University of the Free State
Bloemfontein, South Africa
Supervisor: Professor M.T. Labuschagne (Ph.D.)
Co-Supervisors: Dr. C.D. Viljoen (Ph.D.) and Professor G. Osthoff (Ph.D.)
May 2003
2 2 J 2004
Dedicated to
My Father
11
DECLARATION
I declare that the dissertation submitted hereby for the degree of Philosophiae
Doctor in Agriculture in the University of the Free State is an original work and
has not been previously submitted by me to another University.
I further concede copy right of the dissertation in favour of the University of the
Free State.
Nemera Geleta Shargie May 2003
III
ACKNOWLEDGEMENTS
I would like to thank the following individuals, organizations and/or institutions
for their contribution towards the success of this dissertation:
o Professor Maryke T. Labuschagne for her efficient supervision and
continued support, motivation and enthusiasm.
o Dr. Chris D. Viljoen for his excellent co-supervision and vital theoretical
and practical input.
o Professor G. Osthoff for his interest, consistent encouragement and
guidance in the food quality evaluation.
o The Agricultural Research & Training Project (ARTP) from the World
Bank through the Alemaya University (AU), Ethiopia, for the financial
support of my study and the research. The Alemaya University for
giving me the study opportunity.
o The sorghum growers of the study area for their willingness and
cooperation during research sample collection.
o The Institute of Biodiversity Conservation and Research of Ethiopia for
giving me the permission to take the sorghum accessions, included in
the research, to South Africa.
o Ms Elizma Koen of the Genetics and Molecular Biology Laboratory for
her excellent assistance, knowledge and help when required.
o Mrs C. Bothma for her professional assistance in the sensory
evaluation of sorghum injera.
o Dr. A. Hugo, Miss Maryna de Wit, Miss Eileen Roodt and Mr. W.
Combriek for their technical assistance during the chemical composition
determination.
o Mrs. Christina Matla of the Small Grains Institute, Bethlehem, for her
help in milling the samples included in the sensory evaluation.
IV
o Mr. Shimelis Woldehawariat, the coordinator of the ARTP/AU for all his
support throughout my study and the rest of the staff for their
assistance in facilitating the research materials and logistics.
o Personnel of the Plant Breeding division especially Mrs. Sadie
Geldenhuys for providing excellent support, encouragement and the
creation of a good working environment.
o All personnel and students of the Molecular and Genetics Laboratory at
the previous Dept. of Botany and Genetics (the current Dept. of Plant
Sciences) for their technical support and assistance.
o Personnel of the Sorghum Improvement Program of the Alemaya
University, especially Miss Birtukan Yimam, for the technical support
during the field experiment.
o Mrs. Heleni Girma and my wife Shashitu Barkessa for their help in
preparation of the injera for the sensory evaluation. In addition, my wife,
for her care, encouragement, support and patience during the study.
o All colleagues and friends that directly or indirectly have influenced my
studies.
o God, who made all these possible.
v
TABLE OF CONTENTS
PAGE
DECLARATION ii
ACKNOWLEDGEMENTS iii
LIST OF TABLES x
LIST OF FIGURES xiii
SYMBOLS AND ABBREVIATIONS xv
APPENDICES xvii
CHAPTER 1 GENERAL INTRODUCTION ------------------------------------------1
REFER ENCES -------------------------------------------------------------------------------- 4
CHAPTE R 2 LITERATURE REV lEW ----------------------------------------------- 6
2.1 Introd uction --:------------------------------------------------------------------- 6
2.2 Morpho-agronomic traits as markers --------------------------------------- 8
2.2.1 Qualitative traits -------------------------------------------------------- 8
2.2.2 Quantitati ve tra its ------------------------------------------------------ 8
2.3 DNA-based molecular marker systems (DNA fingerprinting) ------- 9
2.3.1 Restriction fragment length polymorph isms (RFLPs)--------12
2.3.2 Polymerase chain reaction (PCR) - based techniques ----13
2.3.2.1 Random amplified polymorphic DNA (RAPD)
markers -------------------------------------------------------- 14
2.3.2.2 The amplified fragment length
polymorphisms (AFLP's ) ----------------------------- 14
2.3.2.3 Microsatellites or simple sequence
repeats (SSRs) ------------------------------------------ 17
2.4 Genetic distance analysis --------------------------------------------------- 18
2.5 Comparison of major marker systems ----------------------------------- 20
2.6 Food quaIity cha racteri sties ------------------------------------------------ 22
2.6.1 Physical properties and chemical composition -------------- 23
2.6.1.1 Physical properties -------------------------------------- 23
VI
2.6.1.2 Chemical composition --------------------------------- 23
2.6.1.2.1 Protein content ----------------------------- 23
2.6.1.2.2 Lipid content ------------------------------- 24
2.6.1.2.3 Carbohydrate content --------------------- 25
2.6.1.2.4 Polyphenol / Tannins --------------------- 27
2.6.2 Food quality / Sensory evaluation ------------------------------- 28
REFERENCES -------------------------------------------------------------------_________31
CHAPTER 3 PHENOTYPIC DIVERSITY FOR MORPHO-
AGRONOMICAL TRAITS IN SORGHUM ------------------- 50
Abs tract ---- -------------------------------- ----------------- ------ -------- -------______50
3.1 Introd uction -------------------------------------------------------------- 50
3.2 Materi aIsan d methods ----------------------------------------------------- __52
3.2.1 Oualitative traits------------------------------ ---- 52
3.1.1.1 Plant material --------------------------------------------- 52
3.1.1.2 Methods ----------------------------------------------------- 53
3.1.1.3 Data analysis ---------------------------------------------- 53
3.2.2 0uantitative traits----------------------------------------------------- 54
3.2.2.1 Location of the study----------------------------------- __54
3.2.2.2 PIant materials--------------------------------------______ 54
3.2.2.3 Parameters measured --------------------------------- 54
3.2.2.4 Stati stical analysis --------------------------------------- 59
3.3 Results and discussion ------------------------------------------------------ 59
3.3.1 Oualitative tra its -------------------------------------------------- 59
3.3.2 0uantitative traits --------------------------------------------------- 61
3.3.2. 1 Clustering -------------------------------------------------- 61
3.3.2.2 Principal component analysis ------------------------- 64
3.4 Conclusions --------------------------------------------------------------------- 70
REFERENCES -----------------------------------------------------------------------------_ 71
CHAPTER 4 ANALYSIS OF GENETIC DIVERSITY BASED ON
DNA MARKERS IN SORGHUM ------------------------------------- 74
Abs tract ------------------------------------------------------------------------------- __74
Vl!
4.1 Introd uction ------------------------------------------------------ 74
4.2 Materi aIsand methods -------------------------------------------- 76
4.2. 1 Plant materia1---------------------------------------------- 76
4.2.2 DNA extra ction ------------------------------------------ 76
4.2.3 DNA concentration determination -------------------- 76
4.2.4 AFLPs ------------------------------------------------------ 77
4.2.4.1 Restriction endonuclease digestion and
ligation of adaptors --------------------------------- 77
4.2.4.2 PCR amplification reactions ---------------------- 77
4.2.4.3 AF LPan aIysis ------------------------------------------- __79
4.2.4.4 Stati stieaI anaIysis ------------------------------------ 79
4.2.5 Microsateil ites (SSRs) -------------------------------------- 79
4.2.5.1 SSR primers -----------:----------------------------- 79
4.2.5.2 SSR amplification ------------------------------------ 80
4.2.5.3 SSR locus visualization / gel analysis -------------- 80
4.2.5.4 Data collection and statistical analyses ------------ 80
4.3 Results and discussion ---------------------------------------- 82
4.3.1 AFLP markers ------------------------------------------ 82
4.3.1.1 Level of AFLP polymorphism ----------------------- __82
4.3.1.2 Genetic distance and cluster analysis -------------- 84
4.3.1.3 Principal component analysis ----------------------- __87
4.3.2 Microsateil ite markers --------------------------------------- 88
4.3.2.1 Polymorphism of SSRs in sorghum accessions - 88
4.3.2.2 Genetic diversity -------------------------------------___ 89
4.3.3 Comparison of AFLP and SSR markers ------------------------ 93
4.4 Conclusions ---------------------------------------------------------;;;. 95
REFERENCES ----------------------------------------------------------- 96
CHAPTER 5 COMPARISON OF AFLP, SSR AND MORPHO-
AGRONOMICAL TRAITS MARKERS FOR
ESTIMATING GENETIC DIVERSITY IN SORGHUM:----------99
Abs tract ---------------------- ----- --------- --------- ------ -------------- -- 99
vin
5.1 Introd uction --------------------------------------------------------------- 100
5.2 Materials and methods ---------------------------------------------------- 101
5.2.1 Plant material ---------------------------------------------------- 101
5.2.2 Methods --------------------------------------------------------- 101
5.2.2.1 Morpho-agronomical traits ---------------------------- 101
5.2.2.2 DNA markers -------------------------------------------- 102
5.2.2.2.1 AFLP's -------------------------------------- 102
5.2.2.2.2 Microsatellites (SSR's ) ------------------ 102
5.2.3 Data anaIysis ------------------------------------------------------_ 102
5.3 Results and discussion ---------------------------------------------------- 103
5.3.1 Level of polymorphism detection ------------------------------- 103
5.3.2 Genetic diversity estimation ------------------------------------- 103
5.3.3 Clustering based on AFLP, SSR and morpho-
agronom icaI markers ---------------------------------------------- 105
5.4 Conclusions ---------------------------------------------------------------_____ 111
REFERENCES ----------------------------------------------------------------------______ 112
CHAPTER 6 PHYSICO-CHEMICAL ANALYSIS OF SORGHUM
AND SENSORY EVALUATION OF INJERA ---------------- 115
Abs tract ---------------------------------------------------------------------------____ 115
6. 1 Introd uction ------------------------------------------------------------------- 116
6.2 Materials and methods ---------------------------------------------------_ 118
6.2.1 Selection of sorghum accessions ----------------------------- 118
6.2.2 Methods --------------------------------------------------------- 119
6.2.3 Chemical composition -------------------------------------------- 119
6.2.3.1 Moisture content ---------------------------------------- 119
6.2.3.2 Protein content ------------------------------------------ 119
6.2.3.3 Lipid extraction and methylation ------------------- 119
6.2.3.4 Fatty acid analysis ------------------------------------- 120
-
6.2.3.5 Starch content -------------------------------------------- 120
6.2.3.6 Amylose content ---------------------------------------- 122
6.2.3.7 Polyphenol / Tannin content ------------------------ 122
IX
6.2.4 Sensory evaluation of injera ------------------------------------ 123
6.2.4.1 Sorghum samples -------------------------------------- 123
6.2.4.2 Methods of sensory evaluation---------------------- 124
6.2.5 Statistical analyses ------------------------------------------------_ 125
6.3 Results and discussion ---------------------------------------------------_ 126
6.3.1 Chemical composition -------------------------------------------- 126
6.3.2 Sensory evaluation of injera ------------------------------------- 132
6.3.3 Relationships between physiochemical properties
and injera quality ---------------------------------------------------- 137
6.4 Conclusions -------------------------------------------------------------------_ 139
REFERENCES -------------------------------------------------------------------_________140
CHAPTER 7 GENERAL CONCLUSIONS -------------------------------------- __144
CHAPTE R 8 SUMMARY ------------------------------------------------------ 146
OPSOMMING ------------------------------------------------------------- 148
x
LIST OF TABLES
TABLE PAGE
3.1 Local / cultivar name, collection site, altitude and status of
sorghum samples used in the study -------------------------------------------- 55
3.2 Character, descriptor and codes used for characterisation of
sorghum access ions--------------------------------------------------------- 57
3.3 List, code and descriptions of the quantitative characters
record ed in the study -------------------------------------------------------------- 58
3.4 Estimates of H', partitioning into within and between collection
sites for 10 qualitative characters in sorghum accessions ----------------- 60
3.5 Estimates of the Shannon-Weaver diversity index, H', for 10
qualitative characters in sorghum accessions by location of
eoIIecti0n----------------------------------------------------------------------- 61
3.6 Distribution of the 45 sorghum accessions into seven clusters by
location of origin using average values of quantitative characters ------- 64
3.7 Cluster means for 16 quantitative characters in 45 sorghum
accessions ------------------------------------------------------------------------ 66
3.8 Correlation coefficients (n = 45) between .quantitative traits and
grain yield per panicle, head weight and grain number per
paniclein sorghum -------------------------------------------------------------- 67
3.9 Principal component (PC) analysis of 16 quantitative traits in 45
sorghum accessions showing eigenvectors, eigenvalues and
proportion of variations explained with the first eight PC axes ------------ 68
4.1 Adaptors and primers used for pre-selective and selective AFLP
ampiification reacti ons ----------------------------------------------.:.---------------- 78
4.2 Summary of the SSR-primer pairs used in this study------------------------ 81
Xl
4.3 The number of AFLP fragments and degree of polymorphism
determined for 45 sorghum accessions using eight AFLP primer
combinati0ns----------------------- ----------------- --------------------- 83
4.4 Number of alleles, size range (in base pairs), and PlC values for
SSR loci found in 45 accessions of sorghum --------------------------- 90
4.5 Level of polymorphism and comparison of the amount of
information obtained with AFLP and SSR markers in 45
sorghum accessio ns----------------------------------------------------- 95
5.1 Number of polymorphic bands, average and range of pair-wise
genetic distance (GO) estimates among 45 sorghum accessions
based on AFLP, SSR, and morpho-agronomical traits data ------------- 104
5.2 Correlation coefficients between genetic distance values
estimated for the three techniques (AFLP, SSR and morpho-
agronomical traits), with sample size of 990 --------------------------------- 105
6.1 List of sorghum accessions included in the chemical
compositi0n determ inati0n ----------------------------------------------------____ 118
6.2 Average values and mean separation of the chemical
compositions for 13 sorghum accessions-------------------- 127
6.3 Fatty acid compositions (%) of sorghum accessions ---------------------_ 128
6.4 Average tannin content and mean separation for six sorghum
accessions --------------------------------------------------------------- 129
6.5 Correlation coefficients (n = 39) between chemical and physical
properties for 13 sorghum accessions ------------------------------------- 130
6.6 Principal component (PC) analysis of eight physiochemical
parameters in 13 sorghum accessions with eigenvectors,
eigenvalues and proportion of variations explained by the first
three PC axes ---------------------------------------------------------------- 131
6.7 Rank sums and significance tests for injera colour ------------------------ 133
XII
6.8 The rank sums and differences between products along with the
significance levels for 'eye' quality of injera ---------------------------------- 134
6.9 Summarised results for under side appearance of injera from six
sorgh um samples -------------------------------------------------------- 135
6.10 Summarised rank sums and significance tests for texture of injera -- 135
6.11 Rank sums and significance tests for injera taste results --------------- 136
6.12 Correlation coefficients between injera quality and some
physico-chemical parameters in sorghum ---------------------------------- 138
Xlll
LIST OF FIGURES
FIGURE
PAGE
3.1 Geographic locations where the sorghum landrace accessions
used in the study were collected. ----------------------------- 56
3.2 Dendrogram showing cluster groups among the 45 sorghum
accessions based on 16 quantitative traits data ----------------------- 63
3.3 Principal component plot of the 45 sorghum accessions,
estimated using 16 quantitative traits data ------------------------------ 69
4.1 Genescan electropherograms showing part of the AFLP runs of
bulked DNA of two accessions (ETS 721 and ETS 2752) using
EcoRI/ACA and Msel/CTA primers in the present study. ---------------- 84
4.2 Frequency distribution of pair-wise genetic distance coefficients
obtained for 45 sorghum accessions using AFLP data.--------------------- 84
4.3 Dendrogram constructed based on AFLP data, showing genetic
distance and cluster groups among 45 sorghum accessions -------- 86
4.4 Plot of the 45 sorghum accessions against the first two principal
components (PCi and PC2) based on AFLP data ----------------------- 87
4.5 Agarose gel electrophoresis of SSR-PCR products amplified
using primer sb6-36 (AG)1g----------------------------------------- 89
4.6 Frequency distribution of pair-wise genetic distance coefficients
among 45 sorghum accessions based on SSR data -------------------- 91
4.7 Dendrogram constructed for 45 accessions of sorghum based
on data from 43 ploymorphic SSR alleles ----------------------------- 92
4.8 Plot of the 45 sorghum accessions against the first two principal
components analysis (PCi and PC2) computed using the SSR
data -------------------------------------------------------------------- 93
XIV
5.1 Dendrograms of 45 sorghum accessions constructed using
dissimilarity matrix from (a) morpho-agronomic, (b) AFLP, (c)
SSR, (d) AFLP + SSR, and (e) combined data. --------------------------- __110
6.1 Standard graph for glucose content determined by duplicate
data points------------------------ -------- 121
6.2 Standard amylose curve determined by duplicate data points ---------- 122
6.3 PC plot of 13 sorghum accessions analysed using eight
physicochem icaI parameters. ------------------------------------------ 132
6.4 Injera / bidena prepared from two sorghum accessions,
Ambajeette (A) and ETS 1005 (B) showing the colour variation
and the distribution of the 'eyes' ---------------------------------------- 134
6.5 The six sorghum injera / bidena samples evaluated by the
paneil ists. ---------------------,-----------------------:------------------ 136
6.6 Bar graph showing the relationship between tannin content,
injera quality, endosperm texture, grain colour and 1aaa-kernel
weight in six sorghum samples --------------------------------------- 138
xv
SYMBOLS AND ABBREVIATIONS
A = absorbance
AFLP = amplified fragment length polymorphism
AU = Alemaya University
bp = base pair
°C = degree Celsius
% = percent
CTAB = cetyltrimethyl ammonium bromide
cm = centimeter
DMSO = dimethyl sulphoxide
DNA = deoxyribonueclic acid
DNS = dinitrosalicylic acid
DNTP = deoxynucleoside triphosphate
EDTA = ethylenediamin tetra acetic acid
et al = 'et alii / alia' (and others)
g = gram
GD = genetic distance
h = hour
min = minute
H2O = water
HCI = hydrochloric acid
KCI = potassium chloride
kg = kilograms
LSD = least significant difference
m = meter
M = molar
mol = mole
mg = milligram
MgCI2 = magnesium chloride
ml = milliliter
XV1
mM = millimolar
NaCI = sodium chloride
NaOH = sodium hydroxide
NCSS = number cruncher statistical system
ng = nanogram
nm = nanometre
OD = optical density
PCA = principal component analysis
PCR = polymerase chain reaction
PlC = polymorphism information content
RAPD = random amplified polymorphic DNA
RFLP = restriction fragment length polymorphism
RFU = reflective fluorescent unit
rpm = revolutions per minute
SOS = sodium dodecyl sulphate
sec
f = second
SIP = Sorghum Improvement Program, Alemaya University
SSR = simple sequence repeat
TAE = Tris, acetic acid and EDTA
Taq = Thermus aquaticus
TE = Tris EDTA
Tris-HCI = (Tris [hydroxymethyl] aminomethane) hydrochloric acid
!lg = microgram
!lI = microlitre
!lM = micromolar
UPGMA = unweighted pair group method using arithmetic averages
UV = ultraviolet
XYll
APPENDICES
Appendix I AFLP fragment scores for primer combination Ecol +
ACA / Msel + CAA ---------------------------------------------------- 150
Appendix II Amplified fragments score for 10 microsatellite loci:
sb1-10, sb4-15, sb4-22, sb4-32, sb5-85, sb5-236,
sb6-36, sb6-57, sb6-84 and sb6-342. ---------------------------- 156
Appendix III Average values calculated for 16 quantitative traits in
45 sorghum accessions ---------------------------------------------- 160
Appendix IV Binary score of qualitative traits for 45 sorghum
accession s--------------------------------------------------------------- 161
Appendix V Binary scores of quantitative traits for 45 sorghum
accession s------------------------------------------------.--------------- 164
Appendix VI An example of the pair-wise genetic distances
estimated between some of the accessions based on
morpho-agronomical data ------------------------------------------- 169
Appendix VII An example of the pair-wise genetic distances
estimated between some of the accessions based on
AFLP data --------------------------------------------------------------- 170
Appendix VIII An example of the Pair-wise genetic distances
estimated between some of the accessions based
on microsatellites data ------------------------------------------- 171
Appendix IX Partial view of the sensory evaluation of the injera --------- 172
CHAPTER 1
GENERAL INTRODUCTION
Analyses of the extent and distribution of genetic variation in a crop are
essential for understanding the evolutionary relationships between
accessions and to sample genetic resources in a more systematic fashion
for breeding and conservation purposes. Sorghum [Sorghum bicoior (L.)
Moench, 2n = 20] is fifth in importance among the world's cereals (Doggett,
1988). It is the major crop in warm, low-rainfall areas of the world. It is a
crop with extreme genetic diversity (Subudhi et a/., 2002) and
predominantly a self-pollinating crop, with various levels of outcrossing.
The greatest variability is found in the northeast quadrant of Africa, which
includes Ethiopia, Eritrea and Sudan, and most evidence points to this area
as the likely principal area of its domestication (Vavilov, 1951; Doggett,
1970, 1988; House, 1985). According to Gebrekidan (1973, 1981), in
Ethiopia, sorghum exists in tremendous diversity throughout the growing
areas, which contain pockets of isolation with an extremely broad and
valuable genetic base for potential breeding and improvement in the
country and the world at large.
Known under the generic name bishinga by Oromo people, various types
of sorghum are widely cultivated on the highlands of eastern Ethiopia.
Landraces are more preferred by the farmers due to their adaptation to
specific environmental conditions and additional characters such as
storability, food quality, and/or amount and quality of by-products. The
diverse growing environments and the preference of farmers to grow
landraces are ideal for maintenance of a wide range of sorghum types. But,
according to Klingeie (1998) the crop is facing a serious challenge from
shrinking of individual land holdings due to the expansion of a cash crop
chat (Catha edulis). Moreover, Maxted et al. (2002) indicated that though
most genetic diversity of immediate and potential use to plant breeders is
found among landraces there is evidence that it is being rapidly eroded.
Chapter 1 General Introduction 2
Evaluation of genetic diversity can indicate which landraces carry the
greatest genetic novelty, and are the most suitable for rescue, and possible
future use in crop improvement. Furthermore, to improve and stabilize
production and utilization of sorghum in the area, new lines of sorghum
should also yield equal or better than existing landraces familiar to farmers.
Evaluation of genetic diversity levels among adapted, elite germplasm can
provide predictive estimates of genetic variation among segregating
progeny for pureline cultivar development (Manjarrez-Sandoval et al.,
1997). The use of germplasm developed within the same region targeted
for cultivar improvement reduces the risk of losing essential adaptive
characteristics through recombination (Allard, 1996). To improve yield and
other consumer preferred traits through the use of landraces; therefore,
complete information of the genetic diversity and the physical and chemical
properties of sorghums available in the region is a priority.
The accurate, fast, reliable, and cost-effective identification of plant
populations and varieties is essential in agriculture as well as in pure and
applied plant research (MorelI et al., 1995). Traditionally, taxonomists
classify genetic resources in sorghum based on morphological traits
(Stemier et al., 1977). In the first instance, this usually involves description
of variation for morphological traits, particularly morpho-agronomical
characteristics of direct interest to users. While these methods are very
effective for many purposes, morphological comparisons may have
limitations including subjectivity in the analysis of the character; the
influence of environmental or management practices on the character;
limited diversity among cultivars with highly similar pedigrees; and confining
of expression of some diagnostic characters to a particular stage of
development, such as flowering or seed maturity. Menkir et al. (1997)
indicated that important traits, which are related to habitat adaptation and
particular end use of the crop, exhibit enormous variability among sorghum
germplasm. Hence, classifying germplasm accessions based solely on
morphological characters may not provide an accurate indication of the
genetic divergence among the cultivated genotypes of sorghum.
Chapter 1 General Introduction 3
These considerations have led to the exploration or adoption of other
techniques for genetic diversity estimation and cultivar identification,
including cytogenetic analysis; isozyme analysis; and molecular techniques
that analyse polymorphism at the DNA level directly. Molecular markers
are nowadays widely used as tools to assess the soundness of
morphological classification in crop plants. The amplified fragment length
polymorphisms (AFLPs) and rnicrosatellites or simple sequence repeats
(SSRs) DNA markers have proved to be efficient and reliable in supporting
conventional plant breeding programmes (Paterson et al., 1991; More" et
al., 1995; Kumar, 1999).
In this study, the level of genetic diversity was determined among 34
sorghum accessions that were sampled directly from farmers' fields and 11
elite breeding lines, using morpho-agronomic traits, DNA marker
techniques (AFLP's and microsatellite's or SSR's), and evaluated for
chemical composition and food quality characteristics.
General Objectives
1. To analyze the extent of genetic diversity in sorghum accessions from
the eastern Ethiopian highlands using morpho-agronomical characters;
2. To examine the genetic diversity among accessions using DNA (AFLP
and SSR) markers;
3. To verify how useful AFLP and SSR's markers are in determining
distinctiveness of sorghum accessions;
4. To provide an example of the combined use of AFLP and SSR profiles,
and highly reliable morpho-agronomical characters for diversity
assessment;
5. To assess variability for chemical composition and quality
characteristics, and see its integration with morpho-agronomical and
DNA marker data;
6. To examine the distribution of genetic variation in different localities and
give recommendations for future conservation and breeding strategies.
Chapter 1 General Introduction 4
REFERENCES
Allard, R.W. 1996. Genetic basis of the evolution of adaptedness in plants.
Euphytica 92:1-11.
Doggett, H. 1970. Sorghum. Longmans. Green & Co. ltd. London.
Doggett, H. 1988. Sorghum. Longmans (Second edition). Green & Co.
ltd., London.
Gebrekidan, B. 1973. The importance of the Ethiopian sorghum in the
world sorghum collections. Econ. Bot. 27:442-445.
Gebrekidan, B. 1981. Salient features of the sorghum breeding strategies
used in Ethiopia. Eth. J. Agri. Sci. 3:97-104.
House, L.R. 1985. A guide to sorghum breeding (Second Edition).
Patancheru, A.P., ICRISAT, India.
Klingele, Ralph. 1998. Hararghe farmers on the cross-roads between
subsistence and cash economy. http://www.telecom.net.et/-undp-
eue/reports/hararghe.html.
Kumar, L.S. 1999. DNA markers in plant improvement: An overview.
Biotechnology Advances 17:143-182.
Manjarrez-Sandoval, P., Carter, T.E., Webb, O.M. and Burton J.W.
1997. RFLP genetic similarity estimates and coefficient of parentage
as genetic variance predictors for soyabean yield. Crop Sci. 37:698-
703.
Maxted, N., Guarino, L., Myer, L., and Chiwona, E.A. 2002. Towards a
methodology for on-farm conservation of plant gentic resources.
Genet. Resour. Crop Evol. 49:31-46.
Menkir, A., Goldsbrough, P. and Ejeta, G. 1997. RAPD based
assessment of genetic diversity in cultivated races of sorghum. Crop
Sci. 37:564-569.
MorelI, M.K., Peakall, R., Appels, R., Preston, L.R., and Lloyd, H.L.
1995. DNA profiling techniques for plant variety identification.
Australian J. Exp. Agric. 35:807-819.
Chapter 1 General Introduction
5
Paterson, A.H., Tanksley, S.O. and Sorrells, S.M. 1991. DNA markers in
plant improvement. Advances in Agronomy 46:39-90.
Stemier, A.B., Harlan, J.R., and de Wet, J.M.T. 1977. The sorghums of
Ethiopia. Eco. Bot. 31 :446-460.
Subudhi, P.K., Nguyen, H.T., Gilbert, M.L., and Rosenow, D.T. 2002.
Sorghum improvement: past achievements and future prospects. In:
Kang, M.S. (Ed.). Crop improvement: Challenges in the twenty-first
century. The Haworth Press, Inc., NY, pp 109-158.
Vavilov, N.1. 1951. The origin, variation, immunity and breeding of
cultivated plants. Chronica Botanica 13: 1-366.
6
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
Genetic diversity, the heritable portion of observable variation, is the raw
material on which natural and artificial selection has acted to create earth's
vast array of organisms. Estimation of genetic diversity in plant species can
assist in the evaluation of different germplasm as possible sources of
genes that can improve the performance of cultivars. As a result, qualitative
and quantitative traits can be more efficiently introduced into plant breeding
programmes. In search for diverse breeding material, landraces or farmer
varieties (locally adapted populations bred through traditional methods of
direct selection) are usually the major sources of genetic variation. As
discussed by Jain (1975), within the same species, estimates of the
amount of variation may vary widely, depending upon the area sampled,
geographical scale of sampling, etc., presumably due to the complex
interrelationships between the genetic, ecological as well as historical
variables. Individual loci can also vary widely, due to both adaptive and/or
non-adaptive reasons, in the geographical distribution of alleles. This, of
course, adds a great deal to the variation and, therefore, uncertainty about
the expected allelic frequency distribution at any specific locus, in any
individual population. It appears that numerous complex environmental
gradients and the high phenotypic plasticity characteristic of species often
yield highly irregular variation patterns (both phenotypic and genetic
criteria).
Sorghum is known for the ability to grow in harsh environments and has
numerous mechanisms that allows it to survive and be productive in these
conditions. Despite the importance of the sorghum crop, comprehensive
genetic characterization has been limited (Subudhi and Nguyen, 2000a).
Harlan and de Wet (1972) classified traditional sorghum cultivars into five
Chapter 2 Literature Review 7
main races (bicolor, caudatum, durra, guinea, and kafir) and 10
intermediates, mainly on the basis of the morphology of spikelets and
grains. Of the five basic races, four races (bicolor, caudatum, durra, and
guinea) are reported found in Ethiopia (Stemier et a/., 1977). According to
Harlan (1992), the intermediate races involving these four basic races also
widely occur in Ethiopia.
Efforts have been made to identify the different accessions/cultivars of
Ethiopian origin sorghum germplasm based on morphological characters
(Ayana and Bekeie, 1998, 1999; Teshome et a/., 1997; Geleta, 1997;
Abebe and Wech, 1982; Gebrekidan and Menkir, 1979). However,
quantitative traits are influenced by environmental factors and show
variation, resulting in low heritability and high genotype by environment
interactions. Consequently, it is difficult to accurately determine genetic
diversity. However, a continued use of morphological data to describe
cultivars indicates that these data retain popularity as descriptors (Smith
and Smith, 1992). Due to their limited number of detectable loci, allozyme
markers also did not clearly separate the various races of cultivated and
wild sorghum accessions into distinct classes (Ayana, 2001).
Advances in molecular biology provided new methodologies, which extend
the list of useful genetic markers (Paterson et a/., 1991). Determining the
genetic diversity of the different accessions at the DNA level can hold many
advantages for the plant breeder, since it may increase the efficiency of
breeding efforts to improve crop species (Barrett et a/., 1998). This may
explain the reason for the development of the different marker techniques.
On the other hand, Karp et al. (1997) have pointed out that DNA markers
should not be seen as a substitute for other agro-morphological or
biochemical studies that provide researchers with the information they
need. The results of molecular or biochemical studies should be considered
as complementary to morphological characterisation. In this review, the
main techniques available to analyse variation and their major features
have been dealt with.
Chapter 2 Literature Review 8
2.2 Morpho-agronomic traits as markers
2.2.1 Qualitative traits
Morphological traits, for which the variant allelic phenotypes are sufficiently
discrete to allow their segregation to be followed, are the easiest and
generally most economical of all markers to assay. Discrete morphological
traits, though they have high heritability, are limited in number, each being
conditioned by a few genes (Karp et al., 1996, 1997). Thus, only a small
portion of the genome could be covered. They are usually characterised by
epistasis, pleiotropy and dominant-recessive relationships, further limiting
their values as an ideal genetic marker (Smith and Smith, 1992). Besides,
morphological characterisation requires mature plants, it usually displays
dominant phenotype and there are too few available in single species
(Koebner et al., 1994). The method involves a lengthy survey of plant
growth that is labour intensive and time consuming (CIAT, 1993).
In sorghum, as is true for other crop plants, the earliest methods for
estimating genetic diversity include Mendelian analysis of discrete
morphological traits (Doggett, 1988). Sy morphological trait study, earlier
works have shown that eastern Ethiopian sorghum is believed to be
predominantly race durra (Doggett, 1988; Stemier et aI., 1977; Broeke.
1958). Using ex situ conserved sorghum accessions from Ethiopia and
Eritrea, Ayana and Sekele (1998) reported that high and comparable levels
of phenotypic variation exists between the regions of origin. Nevertheless,
in situ pattern of genetic diversity at country as well as regional scale has
not been investigated and remains less understood.
2.2.2 Quantitative traits
Multivariate analysis such as clustering and principal component analysis
of quantitative characters has been used previously to measure genetic
relationships within cereal species. Examples include tef (Ergrostis tef
(Zucc.) Trotter (Assefa et aI., 1999); barley (Hordeum vulgare L.), (Bekeie,
Chapter 2 Literature Review 9
1984); Ethiopian wheats (T. aestivum L.) (Negassa, 1986); durum wheats
(T. turgidum L.) (Jain et al., 1975). Statistical analysis of quantitative
morpho-agronomical traits along with eco-geographic information (de Wet
et al., 1976) is one of the earliest methods used for estimating genetic
diversity in sorghum. It is still widely used to quantify the amount and
distribution of variation in large samples of sorghum germplasm collections
(Prasada Rao and Ramanatha Rao, 1995; Teshome et al., 1997; Ayana
and Bekeie, 1999). Using multivariate analysis procedures, Ayana and
Bekeie (1999) have revealed that the morphological variation in sorghum
germplasm from Ethiopia and Eritrea was structured by environmental
factors.
2.3 DNA-based molecular marker systems (DNA fingerprinting)
Accurate estimates of genetic diversity levels among and within crop plant
species are becoming increasingly useful in crop improvement. During the
past 20 years, DNA marker systems have become extremely useful tools
for assessing genetic diversity levels within and between genotypes.
DNA fingerprinting involves the display of a set of DNA fragments from a
specific DNA sample. Differences in DNA sequence are observed as the
presence/absence of bands. These differences are characteristic and
heritable. A number of problems in plant breeding can be addressed via a
DNA-based molecular marker approach (Karp et al., 1996). In addition to
individual identification, DNA fingerprinting techniques can be used in tests
of parentage, in genetic mapping of loci conditioning economic traits, in
measurement of genetic diversity, and in discerning patterns of genetic
diversity (Smith and Smith, 1992). The decision to exploit the possibilities
opened up by the technology is more often influenced by economical or
practical, rather than by technical considerations. The high variability of
DNA fingerprinting described in humans, animals and plants allows the
identification of different individual genotypes and species (Lin et al., 1993).
Chapter 2 Literature Review 10
Various DNA fingerprinting techniques have been successfully developed
and put in use for estimation of genetic diversity in plant species,
complementing the use of morphological markers. DNA techniques have
the advantage over conventional methods in that the composition of DNA is
consistent in similar tissue types and is not affected by environmental
changes (Beeching et al., 1993). The development of DNA markers
provides an opportunity to detect, monitor and manipulate genetic variation
(Yamamoto et al., 1994) more precisely than in the case of morphological
and expressed phenotypic markers, though results may be confounded by
biased or incomplete genome coverage, detection of co-migrating non-
homologous fragments, or high crossover frequency between markers
used in the evaluation and linked genetic material (Barrett and Kidweil,
1998). These techniques include a variety of different methodologies
commonly referred to as DNA fingerprinting (Nybom et al., 1990). DNA
molecular markers are potentially unlimited in number, are not affected by
the environment and can be mapped on linkage maps (Solier and
Beckmann, 1983; Winter and Kahl, 1995).
Moreli et al. (1995) stated that DNA-based markers offer a number of
advantages over isozymes and other biochemical methods for
demonstrating distinctness. Firstly, the DNA sequence of an organism is
independent of environmental conditions or management practices.
Secondly, the presence of the same DNA in every living cell of the plant
allows tests on any tissue at any stage of growth (provided that DNA of
sufficient purity can be isolated). Thirdly, the recent advent of the
polymerase chain reaction (peR) has enabled the development of new
DNA profiling techniques that are simply and quickly performed. These
techniques offer a number of advantages over other DNA profiling
techniques and conventional methods for identifying plants.
The DNA techniques have been used to investigate the extent of genetic
diversity and genetic relationships within and between cultivars and elite
materials of many plant species. In sorghum, molecular markers have been
used to identify and characterise quantitative trait loci (QTL) associated
Chapter 2 Literature Review 11
with several different traits including plant height and maturity (Pereira and
Lee, 1995), characters concerned with plant domestication (Paterson et al.,
1995), disease resistance (Gowda et al., 1995), and drought tolerance
(Tuinstra et al., 1996, 1997, 1998). In addition, several sorghum linkage
maps (Hulbert et al., 1990; Melake-Berhan et al., 1993; Xu et al., 1994;
Chittenden et al., 1994; Pereira et al., 1994; Ragab et al., 1994; Lin et al.,
1995; Dufour et al., 1997; Boivin et al., 1999) have been generated. Tao et
al. (1998) constructed a sorghum map using a recombinant inbred line
(RIL) population and a variety of probes, including sorghum genomic DNA,
maize genomic DNA and cDNA, sugarcane genomic DNA and cDNA,
cereal anchor probes, and eight SSR loci. Recently, Subudhi and Nguyen
(2000b) completely aligned all the 10 linkage groups of all the major
sorghum RFLP maps using common RIL populations and sorghum probes
from all three sources (Chittenden et al., 1994; Ragab et al., 1994; Xu et
al., 1994) along with many cereal anchor and maize probes.
Over the past decade a number of DNA fingerprinting techniques have
been developed to provide genetic markers capable of detecting
differences among DNA samples across a wide range of scales ranging
from individual/clone discrimination up to species level differences.
Currently available techniques include: RFLPs (restriction fragment length
polymorph isms, Liu and Furnier, 1993), OAF (DNA amplification
fingerprinting, Caetano-Anolles and Gresshoff, 1994), AP-PCR (arbitrarily
primed PCR, Welsh and McClelland, 1990), RAPDs (randomly amplified
polymorphic DNAs, Williams et al., 1990), microsatellites (Tautz, 1989),
and most recently AFLPs (amplified fragment length polymorphisms,
Zabeau and Vos, 1993; Vos et al., 1995). At present, the information
available on genetic diversity within cultivated sorghum utilised RFLPs
(Aldrich and Doebley, 1992; Deu et al., 1994; Cui et al., 1995), RAPDs
(Menkir et al., 1997; Ayana et al., 2000; Dahlberg et al., 2002), and SSRs
(Smith et al., 2000, Dje et al., 2000, Grenier et al., 2000, Brown et al.,
1996) techniques, with varying degrees of success.
Chapter 2 Literature Review 12
2.3.1 Restriction Fragment Length Polymorph isms (RFLPs)
RFLP technology has pioneered the integration of DNA markers into
molecular genetics and plant breeding. The evolution of chromosomal
organization, taxonomic characterization, and the measurement of genetic
diversity are some areas of study that have been greatly enhanced by the
use of RFLPs (reviewed in Yang et al., 1996). The first DNA profiling
technique to be widely applied in the study of plant variation was the RFLP
assay. In RFLP analysis, the complete digestion of genomic DNA with
restriction endonucleases generates the detection of differences in the
length of restriction fragments and the resultant fragments are separated by
gel electrophoresis (Karp et al., 1997; Beckman and Sailer, 1983). RFLP
analysis, as applied to other crops (Demissie et al., 1998; Song et al.,
1988; Miller and Tanksley, 1990), as well as to sorghum (Aldrich and
Doebley, 1992), has proven to be an additional, and more sensitive, tool for
studying the amount of genetic diversity and the phylogenetic relationships
among populations, accessions and species.
Prior RFLP diversity studies in sorghum found low frequencies of
polymorph isms for 27 genotypes examined (Tao et al., 1993), but much
greater allelic diversity among RFLPs detected by maize probes than when
isozymes were used to compare a set of 56 geographically and racially
diverse accessions (Aid rich and Doebley, 1992), and Cui et al. (1995)
reported that there was greater nuclear diversity in the wild subspecies
than in the domestic accessions. Though exceptions were common,
especially for the race bicolour, accessions classified as the same
morphological race tended to group together on the basis of RFLP
similarities (Cui et al., 1995). In species such as maize, wheat, and
soybeans, a large number of DNA probes are available, and extensive
DNA profiling with RFLP analyses is feasible (MorelI et al., 1995). RFLP
analysis requires relatively large amounts of DNA (often requiring
destructive sampling) and produces relatively few bands or polymorphisms.
And these conditions make RFLP a technique of lower priority.
Chapter 2 Literature Review 13
2.3.2 Polymerase chain reaction (pCR)-based techniques
Saiki et al. (1985) indicated that the polymerase chain reaction (PCR) was
invented by Kary B. Mullis in 1985 and has revolutionised many areas of
biological science. The PCR relies on the use of a specific class of
enzymes, DNA polymerase, which all living cells possess and use to copy
their own DNA. DNA polymerase copies single-stranded DNA from the
3'OH end of double-stranded DNA. In PCR, the sample is first heated to
separate the double-stranded DNA (denaturation step of three to five min.
at 94-95°C) into single-stranded molecules. Next, the temperature is
lowered to allow short synthetic DNA molecules called primers (typically 8-
20 nucleotides in length) to anneal to complementary sequences (Rolfs et
a/., 1992). These double-stranded complexes serve as starting points for
the copying of single-stranded DNA polymerase. By flanking a region of
DNA with specific DNA primers and cycling the temperature to facilitate
strand separation, primer annealing, and primer extension, PCR can
exponentially amplify a single copy of a DNA molecule to yield sufficient
DNA for electrophoretic analysis (Moreli et a/., 1995). The use of heat-
stable DNA polymerases that survive the lengthy exposure to high
temperatures, and the development of thermocyclers capable of cycling
temperatures quickly and accurately, have facilitated the automation of this
process. The most critical component for optimising the specificity of any
PCR-based assay is the choice of the annealing temperature (Ruano et a/.,
1991) until they find complementary annealing sites. Yu and Pauls (1992)
concluded that the best results should be obtained by optimising for the
shortest possible denaturing time. Too many cycles may result in primer
depletion and subsequent priming by amplification products, which often
leads to longer products and smears in the gel (Rolfs et a/., 1992).
The main advantage of the PCR-based technique over RFLP analysis is its
inherent simplistic analysis and the ability to conduct PCR tests with
extremely small quantities of tissue for DNA extraction (Edwards et a/.,
1991). Currently, PCR is used worldwide in many areas of biology,
agriculture, and medicine (MorelI et a/., 1995).
Chapter 2 Literature Review 14
2.3.2.1 Random amplified polymorphic DNA (RAPD) markers
The RAPD procedure is peR based and allows a relatively large number of
genetic loci to be assayed rapidly and inexpensively (Williams et a/., 1990).
The assay has alleviated some of the technical problems associated with
RFLP and has been widely used to resolve problems in plant breeding and
genetics (Waugh and Powell, 1992). The DNA fragment patterns generated
by this technique depend on the sequence of the primers and the nature of
the template DNA. No prior sequence characterization of the target
genome is needed and peR is performed at low annealing temperatures to
allow the primers to hybridise to multiple loci. RAPD markers have been
used to estimate genetic diversity in several crops, including sorghum
(Ayana et al., 2000; Menkir et al., 1997; Yang et al., 1996). However, the
need to repeat each peR reaction multiple times and the inability to obtain
identical banding patterns in different labs have limited the use of the
RAPD technique (Bai et al., 1999).
2.3.2.2 The amplified fragment length polymorph isms (AFLP's)
The AFLP technique, developed by Zabeau and Vas (1993) and Vas et al.
(1995), is capable of detecting non-specific but many independent loci, with
reproducible amplification (Pejic et al., 1998). The AFLP fingerprints can be
used to distinguish even very closely related organisms, including near
isogenic lines. The technique involves a selective peR amplification of
restriction fragments from an endonuclease digest of total genomic DNA.
The differences in fragment lengths generated by this technique can be
traced to base changes in the restriction/adaptor site, or to insertions or
deletions in the body of the DNA fragment. Dependence on sequence
knowledge of the target genome is eliminated by the use of adaptors of
known sequence that are ligated to the restriction fragments. The peR
primers are specific for the known sequences of the adaptors and
restriction sites. In order to give reproducible results, several reaction
components need to be optimised in the peR reaction. Reaction
components that should be optimised include template, primer, Mgeb,
Chapter 2 Literature Review 15
enzyme and dNTP concentration (Caetano-Anollés et al., 1991). This
usually relies on the sequential investigation of each reaction variable.
Most importantly, AFLPs have been shown to be reproducible and reliable.
This is at least partially due to the fact that limited sets of generic primers
are used and these are annealed to the target under stringent hybridisation
conditions. The technique can be adjusted to generate consistent banding
patterns from DNA of any origin or complexity, and no appreciable effect
has been observed as a result of template concentration (in the range of
2.5 picogram to 25 nanogram). Typically, 50-200 bands are generated in a
single lane after electrophoresis of the PCR amplified products on an
analytical polyacrylamide gel. However, if the template concentration is too
high then the PCR amplification often results in a smear without distinct
bands (Yu et al., 1993). Certain amplified fragments show continuous
increase or decrease In band intensity depending on template
concentrations (Hosaka and Hanneman, 1994). This implies that adjusting
genomic DNA concentrations to the same level in all samples may be an
initial step in obtaining reproducible and comparable banding patterns.
The AFLP data usually must be treated as dominant markers, since the
identity of homo/heterozygotes cannot be established unless breeding/
pedigree studies are carried out to determine inheritance patterns of each
band. However, the large number of bands gives an estimate of variation
across the entire genome, thus giving a good general picture of the level of
genetic variation. This type of information is generally more applicable to
genotyping, forensics, and conservation biology than detailed information
on variation at one or few loci (example, RFLPs, microsatellites, isozymes).
For DNA isolation, plants can be grown in a variety of environments and in
different locations (Young, 1994). Any part of a plant can be used to extract
DNA, however the most common starting material is young leaves. They
can be fresh, lyophilised, dried in an oven or in some cases dried at room
temperature (Kochert, 1994). Several methods for DNA extraction have
Chapter 2 Literature Review 16
been developed; and simplicity, speed, and utilisation of a small amount of
starting material are a common goal in all of them (Lamalay et ai., 1990).
The AFLP technique is rapidly becoming the method of choice for
estimating genetic diversity in both cultivated and natural/rare populations
(Karp et ai., 1997; Paul et ai., 1997; Qamaruz-Zaman et ai., 1997; Sharma
et ai., 1996; Hili et ai., 1996; Lu et ai., 1996; Travis et ai., 1996). Additional
characteristics of the AFLP technique are described as follows:
1. It is relatively fast (samples can be processed on automated
thermocyclers and DNA sequencers).
2. The technique assays the entire genome for polymorphic markers.
3. It requires relatively small amounts of genomic DNA. Typically 0.05-
0.5tJg of DNA is required, depending upon the size of the genome.
4. It provides 10-100 times more markers and is thus more sensitive than
other fingerprinting techniques (example, isozymes, RFLPs,
microsatellites) (Lu et ai., 1996; Sharma et ai., 1996).
5. Unlike RAPDs, it is highly reproducible. Analyses performed by different
workers or in different labs can be compared or reproduced. The bands
(DNA fragments) can be run on an automated sequencer that resolves
fragment length to single-base units. In addition, since each lane
incorporates a set of size standards, fragment sizes can be estimated
accurately thus facilitating comparison of data across gels.
6. Unlike microsatellites, no taxon-specific primer sets are required.
Commercially available primers are available that work for most
organisms.
There are many applications of AFLP markers, the genetic relationship
studies being an important one (Incirli and Akkaya, 2001; Aggarwal et ai.,
1999; Breyne et ai., 1999; Singh et ai., 1999; Schut et ai., 1997; Negash et
ai., 2002). AFLP technology currently offers the fastest, most cost-effective
way to generate high-density genetic maps for marker-assisted selection of
desirable traits. It is also the ideal tool for determining varietal identity and
assessing trueness to type (Perkin-Elmer, 1996).
Chapter 2 Literature Review 17
In many species, AFLPs assay more loci per PCR than RAPD's, and have
greater reproducibility (RusseIl et al., 1997; Powell et al., 1996), which has
led to the increasing use of AFLPs for DNA profiling (Maheswaran et al.,
1997; Powell et al., 1997; Maughan et al., 1996). The suitability of AFLP
analysis for cultivar identification, is demonstrated by the large number of
reports published on the use of the technique for line identification in a
variety of plant species, such as tomato, soybeans, brassicas, sunflower,
pepper, sugar beet, lettuce (Perkin-Elmer, 1996), wheat (Donini et al.,
1997) and barley (Pakniyat et al., 1997). However, AFLPs provide
dominant markers in most cases and their distribution along the genome is
not uniform (Subudhi and Nguyen, 2000a).
2.3.2.3 Microsatellites or simple sequence repeats (SSRs)
Microsatellites or simple sequence repeats (SSRs) are DNA sequences
with repeat lengths of a few base pairs (2-6 bp). They are highly mutable
loci and they are present at many sites throughout a genome. The flanking
,
sequences at each of these sites are often unique. Variation in the number
of repeats can be detected with PCR by developing primers for the
conserved DNA sequence flanking the SSR. Specific primers can be
designed according to the flanking sequences, which then result in single
locus identification. Alleles that differ in length can be resolved using
agarose gels or sequencing gels where single repeat differences can be
resolved and all possible alleles detected. As molecular markers, SSR's
combine many desirable marker properties including high levels of
polymorphism and information content, unambiguous designation of
alleles, even dispersal, selective neutrality, high reproducibility, eo-
dominance, and rapid simple genotyping assays (Jones et al., 1997). For
measuring genetic diversity, assigning lines to heterotic groups and genetic
fingerprinting, microsatellites provide power of determination equal to or
greater than that of RFLP in a more cost effective manner (Senior et al.,
1998; Smith et al., 1997).
Chapter 2 Literature Review 18
In actual fact, SSR markers are time consuming and costly to develop in
that the genomic regions carrying them must be identified and sequenced.
However, once the primers are developed, the technique is one of the most
informative marker systems available. Even between closely related
individuals, the number of repeat units at a locus is highly variable (Mazur
and Tingey, 1995). SSR's are used to cluster lines into groupings (Liu and
Wu, 1998; Senior et a/., 1998). SSR markers have shown high levels of
polymorphism in many important crops including maize (Senior et a/.,
1998), wheat (Devos et a/., 1995; Roder et a/., 1995), rice (Chen et a/.,
1997), barley (Liu et a/., 1996), beans (Yu et a/., 1999), cowpea (Li et a/.,
2001), soybean (Akkaya et a/., 1992), tomato (Smulders et a/., 1997), and
grapevines (Thomas and Scott, 1993).
In sorghum, microsatellites were used to stud" genetic diversity (Ghebru et
a/., 2002; Dje et a/., 1999, 2000; Grenier et a/., 2000; Smith et a/., 2000;
Dean et a/., 1999; Brown et a/., 1996). Results from these studies have
suggested that the microsatellite markers are suitable for applications
relevant to conservation and use of sorghum germplasm.
2.4 Genetic distance analysis
Analyses of the extent and distribution of genetic variation in a crop are
essential in understanding the evolutionary relationships between
accessions and to sample genetic resources in a more systematic fashion
for breeding and conservation purposes (Ejeta et a/., 1999). Menkir et al.
(1997) suggested that molecular markers, in particular genetic distance
estimates determined by molecular markers, are suitable to assess genetic
diversity and to identify diverse sources in crop germplasm collections.
Genetic distance is the extent of gene differences between cultivars, as
measured by alleles frequencies at a sample of loci (Nei, 1987). Genetic
similarity is the converse óf genetic distances, i.e., the extent of gene
similarities among cultivars. The measure of distance or similarity among
cultivars is the covariance of allele frequencies summed for all characters
(Smith, 1984).
Chapter 2 Literature Review 19
Several genetic distance measures have been used to quantify genetic
relationships among cultivars or germplasm accessions. Each variable of
molecular bands such DNA-based marker bands are considered a locus so
that every locus has two alleles. Banding profiles of each accession or
cultivar can be scored as present (1) or absent (0). Generally, two
approaches are used to deduce phylogenetic relationships from
fingerprinting data. The first widely used approach involves the cluster
analysis of pairwise genetic distances for the construction of dendrograms.
Pairwise genetic distances are calculated directly from input data
containing presence (1) or absence (0) values for all markers. One of the
most commonly used genetic distance formulae is the Euclidean distance,
which is the square root of the sum of squares of the distances between
the multidimensional space values of the distances for any two cultivars
(Kaufman and Rouseeuw, 1990) and it can be put as,
where, GO is the genetic distance between individual X and individual
Y: i = 1 to N; N is the total number of bands, and Xi and Y, are ith band
scores (1 or 0) for individual Xs and Ys. The process is repeated for all the
possible pairwise groupings of individuals and the pairwise distance values
tabled in a pairwise distance matrix. Genetic distance has also been
calculated from several genetic similarity indices (GS) that can be
calculated using either: 0 = 1-S or 0 = -ln (S). One useful similarity index
is that of Nei and Li (1979): GD = 1-[2Nxy/Nx+Ny], here 2Nxy is the number
of shared bands, and the Nx and Ny are the number of bands observed in
individual x and individual y, respectively. Other similarity indices such as
Jaccard's (Rohlf, 1993) and Gower's similarity coefficients (Gower, 1971)
have been extensively used in genetic distance determination (Barrett and
Kidweil, 1998).
The pattern of genetic relationship among accessions can be conveniently
shown by multivariate techniques such as cluster or ordination analyses.
Clustering is a useful tool for studying the relationships among closely
Chapter 2 Literature Review 20
related cultivars or accessions. In cluster analysis, cultivars or accessions
are arranged in hierarchy by agglomerative algorithm according to the
structure of a complex pairwise genetic proximity measure. The hierarchies
emerging from the cluster analysis are highly dependent on the proximity
measures and clustering algorithm used (Kaufman and Rousseeuw, 1998).
In ordination analysis, the multidimensional variability in a pairwise, inter
marker proximity is depicted in one to several dimensions through eigen
structure analysis. Ordination is best suited to revealing interactions and
associations among cultivars or accessions, which are described by
continuous quantitative data (Bretting and Widrlechner, 1995). Principal
component, principal coordinate, and linear discriminate analyses are the
ordination techniques most commonly used in genetic relationships and
cultivar classification studies (Schut et al., 1997). Generally, statistical
methods such as univariate, bivariate and multivariate analysis can be
applied to analyse the data generated from germ plasm accessions.
2.5 Comparison of major marker systems
In general, when morpho-agronomic and genetic marker data are available
on a set of genotypes for studying their diversity and the formation of
homogeneous groups, two types of hierarchical classifications are
independently performed (Franco et al., 2001). One is obtained based on
the morpho-agronomic traits in which a standard metric distance (such as
Squared Euclidean) is applied. The other classification is obtained based
on the genetic marker attributes when genetic similarities (or dissimilarities)
of n individuals are determined with molecular markers such as RFLPs,
AFLPs, or SSRs. Using each fragment as an attribute (with values of 0 and
1 denoting the presence or absence of the fragment in that genotype,
respectively), and applying any clustering strategy (such as single or
complete linkage, UPGMA, the centroid method, etc.), genotypes can be
clustered into groups that are as homogeneous as possible and
heterogeneous among groups. Earlier findings have showed that groups
Chapter 2 Literature Review 21
formed based on both continuous and categorical classifications had a low
to medium consensus (Franco et al., 2001).
Furthermore, availability of a large number of molecular markers
necessitates a comparison of one marker technique with other commonly
used markers. The range of DNA polymorphism assays for genome
fingerprinting, investigating genetic relatedness, for genetic mapping and
marker assisted plant breeding have expanded with the dramatic advances
in molecular genetics (Karp et al., 1997). These techniques include RFLP
(Botstein et al., 1980), RAPD (Welsh and 'McClelland, 1990; Williams et al.,
1990), AFLP (Zabeau and Vos, 1993) and SSR (Tautz, 1989, Weber and
May, 1989). These methods detect polymorphism by assaying subsets of
the total amount of DNA sequence variation iii a genome. Polymorph isms
detected with AFLP and RFLP assays reflect restriction size variation.
RAPD polymorph isms result from DNA sequence variation at primer
binding sites and from DNA length differences between primer binding sites
(Williams et al., 1993). This is also true for AFLPs. SSR loci differ in the
number of repetitive di-, tri- or tetranucleotide units present (Tautz and
Renz, 1984), and this length variation is detected with the peR by utilizing
pairs of primers flanking each simple sequence repeat.
Comparison of different classes of molecular markers was conducted in
several crops including soybean, barley and wheat. In soybean, estimates
were made for the information content (expected heterozygosity), the
multiplex ratio (number of loci simultaneously analysed per experiment)
and the effectiveness in assessing relationships between genotypes.
Estimates of a single parameter, the marker index (product of expected
heterozygosity and multiplex ratio), were also obtained to evaluate the
overall utility of the marker. The use of this approach showed that SSR
markers have the highest expected heterozygosity while the AFLP markers
have the highest multiplex ratio and highest marker index. The utility of the
recently developed AFLP markers has widely been reported in the
literature. Since recently, SSR markers are also considered as markers of
choice in plant species, including sorghum (Ghebru et al., 2002; Smith et
Chapter 2 Literature Review 22
al., 2000; Dje et al., 2000; Brown et al., 1996), maize (Pejic et al., 1998,
Senior et al., 1998), wheat (Ahmad, 2002), soyabean (Akkaya et al., 1992)
and cowpea (Li et al., 2001).
The choice of an appropriate DNA profiling technique is dependent on the
aims of the testing. To facilitate selection of an appropriate technique for a
given application, Powell et al. (1996) have utilized two metrices to
compare different marker systems. The first metric was a good measure of
information content/expected heterozygosity. Expected heterozygosity
corresponds to the probability that two alleles taken at random from a
population can be distinguished using the marker in question. SSR markers
are known to have the highest expected heterozygosity (Pejic et al., 1998).
The second metric was the multiplex ratio of a marker system, which
defines the number of loci (or bands) simultaneously analysed per
experiment, for example in a single gel lane. Both AFLPs and RAPDs
generally have higher multiplex ratios than RFLPs and SSRs. The practical
considerations are that the test must also be inexpensive, technically
straightforward, reliable, reproducible, and capable of unambiguous
analysis. The cost of developing and conducting of the test must also be
justified by the economic importance of the species or variety.
2.6 Food quality characteristics
Sorghum product quality is supposed to be determined by an important role
played by two grain characteristics, endosperm texture and endosperm
type (Pushpamma and Vogel, 1982). Endosperm type refers to either a
horny or floury endosperm (Dewar et al., 1993), while endosperm texture is
the proportion of horny (hard) to floury (soft) endosperm (Cagampang and
Kirleis, 1984).
Some consumers do not positively accept the visual appearance, mouth-
feel and flavour of sorghum foods. The dark colour, pronounced flavour,
grittiness of the flour, tannin content and palatability are some of the
negative aspects associated with sorghum products (Sooliman, 1993). The
Chapter 2 Literature Review 23
grittiness in mouth feel is caused by a high horny endosperm content. The
starch in the horny endosperm, with high protein content, swells less tightly
bound starch. Less swelling causes an underdeveloped jelly layer covering
the particles, with a consequential harder and grittier mouth-feel (Novellie,
1982).
2.6.1 Physical properties and chemical composition
2.6.1.1 Physical properties
The major components of the seed are the pericarp or outer cover, the
endosperm or storage organ, and the embryo or germ, which germinates to
reproduce a plant. The endosperm forms the bulk of the kernel, generally
being corneous on the outer extremes and floury toward the centre. Starch
granules in the corneous outer portion are embedded in a protein matrix
and are difficult to separate. Protein content in the floury endosperm is less
than in the corneous types, and there can be voids in the structure
contributing to a more opaque appearance of this portion of the
endosperm. Starch is more easily recovered from the floury endosperm
(Rooney and Miller, 1982). The embryo appears at the lower portion on one
side of the seed. Most of the oil content of the seed is in the embryo.
2.6.1.2 Chemical composition
2.6.1.2.1 Protein content
The protein content of sorghum is an important quality-attribute in terms of
consumer acceptability (Pushpamma and Vogel, 1982), and nutrition
(Serna-Saldivar and Rooney, 1995).
From a nutritional view, sorghum is mainly utilised in developing countries
where cereals are a staple food. This might cause nutritional problems,
since sorghum and most other grains, when tested for albumin, glutelin and
globulin proteins, are deficient in essential amino acids, especially lysine.
Chapter 2 Literature Review 24
The breeding of high lysine sorghum varieties involves an increase in the
levels of these three proteins, causing these varieties to contain
approximately 50% more lysine and better amino acid profiles than regular
varieties (Serna-Saldivar and Rooney, 1995).
The protein content is usually the most variable (Dendy, 1995). The
average protein content of sorghum is 11 to 12%. In his review Lásztity
(1996) reported that the protein content varies from 6 to 25%. The protein
content and composition varies due to genotype, water availability, soil
fertility, temperatures, and environmental conditions during grain
development. Approximately 80, 16, and 3% of the sorghum protein is
located in the endosperm, germ, and pericarp, respectively (Taylor and
Schussler, 1986). Nitrogen fertilization significantly increases amounts of
protein due to accumulation of prolamins (Warsi and Wright, 1973). The
albumin-globulin and glutelin fractions are rich in lysine and other essential
amino acids. Cultivars with improved protein quality usually contain higher
amounts of albumins, glutelins, and globulins and correspondingly lower
proportions of prolamins. The cooking process of sorghum-flour could
decrease the protein content. Raw sorghum flour was found to contain
10.4% protein, while boiled and roasted flour contain 9.2 and 9.5% protein,
respectively (Singh and Singh, 1991).
2.6.1.2.2 Lipid content
Lipids, which are minor components in cereal grains, are found primarily in
the germ of sorghum. The whole grain consists of three types of lipids. The
most abundant group, the nonpolar lipids, consists mainly of triglycerides,
which serve as reserve nutrients during germination. The other two smaller
groups, i.e. the polar (for example phospholipids, glycolipids) and
unsaponifiable lipids (for example phytosterols, carotenoids and
tocopherols) have other important biochemical fractions to fulfil (Serna-
Saldivar & Rooney, 1995). The lipid content of sorghum ranges from 2.1 to
5.0% (Hoseney, 1994). Different sorghum cultivars show some variation,
but these variations are not as extreme as in the case of other chemical
Chapter 2 Literature Review 25
and physical properties of sorghum. Beta et al. (1995) found that 16
different sorghum cultivars had an average fat content of 3.7 ± 0.6%, while
Yang and Seib (1995) found a fat content ranging from 3.2 to 4.1% for nine
sorghum samples. Thus, lipid contents are significantly reduced when
kernels are decorticated and/or de-germed.
Lipid content has been found to be positively correlated with protein
content, so both traits can be selected for simultaneously (House et al.,
1995). Milling plays an important role in the final lipid content of sorghum
meal, because of the large part of the lipid fraction situated in the sorghum
germ. Lipid content could also be used as a means of quality control of
meal, to indicate whether proper separation of kernel parts took place
during milling.
The fatty acid composition of sorghum oil is similar to that of maize and
pearl millet, which contain higher levels of C18: 1 fatty acids than for
example barley and wheat (Hoseney, 1994). Fatty acid composition of
sorghum oil is also similar to that of maize oil, with high concentrations of
linoleic (49%), oleic (31%), and palmitic acids (14%). In addition, the oil
contains 2.7% linolenic, 2.1% stearic acid, and 0.2% arachidic acid
(Rooney, 1978).
2.6.1.2.3 Carbohydrate content
The carbohydrates of sorghum are composed of starch, soluble sugar,
pentosans, cellulose, and hemicellulose. The quality and quantity of
carbohydrates present in sorghum are important quality characteristics of
sorghum and could influence consumer acceptance of the end product
(Pushpamma and Vogel, 1982). Starch is the most abundant chemical
component, while soluble sugars and crude fibre are low. In this study, only
total starch content was examined.
Chapter 2 Literature Review 26
The primary carbohydrate, starch, is the most abundant chemical
component and makes up about 60 to 80% of the normal, non-waxy,
kernels. The soluble sugars and crude fibre contents are low. This leads to
the major role that starch properties play in the textural properties of
cooked sorghum products (Cagampang and Kirleis, 1985), as well as the
provision of fermentabie sugars for beer brewing during malting (Taylor and
Dewar, 1996). Sorghum starches have off-colours that are dependent on
the pericarp colour and the presence of a black pigment in the glumes or
other portions of the plant. Those containing black pigments have pinkish
colours and those lacking this pigment have yellowish off-colours, while
colour intensity is influenced by the pericarp colour (Watson and Hirata,
1955). The starch content of different sorghum cultivars shows wide
variation. Buffo et al. (1997) found a starch content of 72.1 % in the Dekalb
hybrid, while Wankhede et al. (1989) found the CSH-1 hybrid to contain
64.5% starch. Klopfenstein and Hoseney (1995) also reported that starch
makes up to 60 to 80% of normal (non-waxy) kernels.
Starches exist in highly organized granules in which amylose and
amylopectin molecules are held together by hydrogen bonding. The
amylose component plays a significant role in the rheological and shelf life
properties of sorghum foods such as porridge, tortillas and injera (Ring et
al., 1982) and significantly correlated to the vitreousness of sorghum
(Cagampang and Kirleis, 1984). Starch can be classified as waxy or non-
waxy according to the amylose content. Waxy varieties contain up to 5%
amylose, while non-waxy varieties were found to contain amylose levels
from 24 to 30% (Ring et al., 1982). A third group, the heterowaxy type has
a lower amylose content than non-waxy starches. The waxiness of
sorghum starch influences its rheological properties. Many of the properties
of cereal starches that determine their suitability for particular end-uses are
dependent upon their amylose / amylopectin ratios (Gibson et al., 1997).
These include gelatinisation and gelation characteristics, solubility, and the
formation of resistant starch.
Chapter 2 Literature Review 27
On the basis of chemical composition sorghum endosperm is classified as
waxy (100% amylopectin in starch), normal (75% ampylopectin and 25%
amylose), high lysine, sugary or yellow. Regular endosperm sorghum types
contain from 23 to 30% amylose (Ring et al., 1982). Waxy varieties contain
up to 5% amylose.
The starch content and composition of sorghum are influenced by several
factors. Firstly, the type of endosperm from which starch was extracted
plays a significant role. Starch from the corneous endosperm of sorghum,
exhibits a lower amylose content and higher gelatinisation temperatures, as
well as a higher intrinsic viscosity than that from the floury endosperm
(Cagampang and Kirleis, 1985). Environmental and genetic factors
determine amylose levels in sorghum (Ring et al., 1982), as was
demonstrated with sorghum grown under supplementary irrigation and
rainfed conditions. The starch of irrigated sorghum was shown to have
significantly higher amylose contents than the rainfed ones (Taylor et al.,
1997). Environmental factors may affect the amylose content of starch
more than genetic differences in the case of non-waxy varieties (Ring et al.,
1982).
2.6.1.2.4 Polyphenol / Tannins
All sorghums contain phenolic compounds, including phenolic acids and
flavonoids (Klopfenstein and Hoseney, 1995). Some sorghum cultivars with
a pigmented testa (B1- B2- genes) produce condensed polyphenols known
as tannins (Butler, 1990). The compounds can affect colour, flavour, and
nutritional quality of the grain and products prepared from it (Hahn et al.,
1984). Desirable agronomic characters of high-tannin sorghums are that
they protect the grain against insects, birds, and weathering (Waniska et
al., 1989). The agronomic advantages are accompanied by nutritional
disadvantages and reduced food qualities.
The polyphonols (condensed tannins) are mainly situated in the pericarp
and/ or testa of pigmented sorghum varieties (Deshpande et al., 1982).
Chapter 2 Literature Review 28
These compounds in sorghum grain can be characterised using several
techniques (Dendy, 1995). Most tannin assays measure the level of
phenols, which mayor may not be condensed tannins. The absolute
amount of tannin present in the sorghum kernel is virtually impossible to
determine, because a significant proportion cannot be extracted and
assayed (Butler, 1990; Hahn et al., 1984; Hahn and Rooney, 1986), and a
suitable standard for sorghum tannins is unavailable. Different assays are
likely to yield different tannin values because they respond to different
chemical parts of the tannin molecule (Hagerman and Butler, 1981). Hahn
et al. (1984) indicated that a typical brown sorghum contains the highest
amount of free phenolic acids. Waniska et al. (1989) also partitioned
sorghum phenolic acids and concluded that white cultivars without
pigmented testa contained the lowest amounts of phenolic acids.
Tannins offer advantages of supplying bird-proof sorghum varieties with
bird and mould-resistance (Serna-Saldivar and Rooney, 1995; Menkir et
al., 1996). High tannin sorghums are cultivated in a number of places in the
world including Ethiopia where birds are a serious problem. On the
negative side, tannins are anti-nutritional factors, as they bind proteins with
consequent precipitation, which causes a lower nutritional value. Tannins
also cause astringent tastes (Beta, 1998). The second problem lies in the
colour acceptance of sorghum products. White sorghum produces the most
acceptable products to consumers in terms of colour (Serna-Saldivar and
Rooney, 1995), which is a further shortcoming of bird-resistant varieties.
2.6.2 Food quality / Sensory evaluation
Traditionally, injera is the staple diet of Ethiopia and is prepared from either
tef [Eragrostis tef (Zucc.) Trotter], a cereal unique to Ethiopia or sorghum,
depending on regional preference and/or income level. Sorghum is the first
choice in eastern regions of Ethiopia for injera making and also in other
regions of the country. Sorghum injera quality depends mainly on cultivar
and fermentation process. Sorghum cultivars with white or yellow grain
colour are preferred, but local landrace sorghums with soft endosperm and
Chapter 2 Literature Review 29
a red or a brown colour also make acceptable injera. In the past, studies on
the performance of some Ethiopian sorghum cultivars for injera making
indicated a remarkable variability among the cultivars (Wuhib and Tekabe,
1987; Gebrekidan and Gebrehiwot, 1982). However, information on the
relationships between the physical and chemical characteristics of the grain
and injera quality (end-use) is required for the efficient utilization and
conservation of the genetic resources.
The traditional art of injera preparation is not standardised, and there are
minor differences in details of recipes between households, communities,
and regions due to individual preferences and needs. Sorghum flour is
sometimes mixed with that of tef or barley to improve acceptance and
storability. Traditional preparation of injera involves fermentation of the flour
with a starter from a previous batch. The flour is fermented for about 48 hr.
A fresh batch of flour is gelatinised by adding boiling water, and the
gelatinised flour is added to the fermented batter to hasten a secondary
fermentation. Alternatively, a small part of the fermenting flour is prepared
into a gruel (absit) and added to the fermenting batter a day before injera
preparation. The fermented batter is baked on a hot clay griddle called
mitad/ qibaba. To bake injera, 0.5 I of batter is poured on the hot mitad/
qibaba in centrifugal motion from the edge of the griddle to the center.
When small holes (eyes) start appearing on the top of injera, it is covered
with the griddle lid and left to bake for 2-3 min. A typical injera is a circular
pancake of about 50-cm diameter and 5-mm thick. The front side has
uniformly spaced honeycomb like "eyes", each measuring about 4 mm in
diameter and about four per square centimetre of injera surface. The
underside is smooth, without any holes, and is non-sticky. The colour is
white to brown, depending on the grain colour of the cultivar used for flour.
A good quality injera has a soft and pliable texture, enabling the consumer
to pick up wat (stew) by hand in a piece of injera. A good injera tastes
slightly sour, is spongy, can be folded or rolled without cracking, and keeps
its pliable texture for three days. On the contrary, poor injera is dry and
brittle, does not exhibit uniform "eyes," and cannot be stored overnight.
Chapter 2 Literature Review 30
Insufficient fermentation produces a sweetish taste, and such injera is
called aflegna injera or bidena benni. The quality of injera/ bidena is
influenced to a large extent by the fermentation process and the length of
fermentation time. In general, decorticated sorghum grain produces injera
of better quality and a lighter colour. Soft endosperm sorghum types
produce better quality injera, but such grains cannot be decorticated well.
Earlier workers indicated that more information is required on the type of
sorghums suited for injera preparation (Gebrekidan and Gebrehiwot, 1982;
Klopfenstein and Hoseney, 1995). The colour of sorghum grain and flour
plays an important role in its acceptance. In general, white sorghums
produce the most acceptable food products (Serna-Saldivar and Rooney,
1995). However, in some countries, brown sorghum products are preferred,
e.g., for opaque sorghum beer. Food colour is the result of factors such as
grain colour and type (pericarp colour, pigmented testa, endosperm colour),
degree of milling, and pH of the food system.
Chapter 2 Literature Review 31
REFERENCES
Abebe, B. and Wech, H.B. 1982. The 1981 activities of the Plant Genetic
Resources Centre/Ethiopia. In: Proceedings of the Regional
Workshop on Sorghum Improvement in Eastern Africa, 17-22
October, Addis Ababa, Ethiopia, pp. 31-45.
Aggarwal, R.K., Brar, 0.5., Nandi, 5., Huang, N. and Khush, G.S. 1999.
Phylogenetic relationships among Oryza species revealed by AFLP
markers. Theor. Appl. Genet. 98: 1320-1328.
Ahmad, M. 2002. Assessment of genomic diversity among wheat
genotypes as determined by simple sequence repeats. Genome
45:646-651.
Akkaya, M.S., Bhagwat, A.A. and Cregan, P.B. 1992. Length
polymorph isms of simple sequence repeat DNA in soyabean.
Genetics 132:1131-1139.
Aldrich, P.R. and Doebley, J. 1992. Restriction fragment variation in the
nuclear and chloroplast genomes of cultivated and wild Sorghum
bicolor. Theor. Appl. Genet. 85:293-302.
Assefa, K., Ketema, 5., Tefera, H., Nguyen, H.T., Blum, A., Ayele, M.,
Bai, G., Simane, B. and Kefyalew, T. 1999. Diversity among
germplasm lines of the Ethiopian cereal tef [Eragrostis tef (Zucc.)
Trotter]. Euphytica 106: 87-97.
Ayana, A. 2001. Genetic diversity in sorghum (Sorghum bicoior (L.)
Moeneh) germplasm from Ethiopia and Eritrea. Ph.D. dissertation.
Department of Biology, Science Faculty, Addis Ababa University,
Addis Ababa, Ethiopia.
Ayana, A. and Bekeie, E. 1998. Geographical patterns of morphological
variation in sorghum [Sorghum bicoïor (L.) Moeneh] germplasm
from Ethiopia and Eritrea: qualitative characters. Hereditas 129: 195-
205.
Chapter 2 Literature Review 32
Ayana, A. and Bekeie, E. 1999. Multivariate analysis of morphological
variation in sorghum [Sorghum bicoior (L.) Moench] germplasm from
Ethiopia and Eritrea. Genetic Resources and Crop Evolution 46:
273-284.
Ayana, A., Bryngelsson, T. and Bekeie, E. 2000. Genetic variation of
Ethiopian and Eritrean sorghum (Sorghum bicolor (L.) Moench)
germplasm assessed by random amplified polymorphic DNA
(RAPD). Genetic Resources and Crop Evolution 47(5): 471-482.
Bai, G., Ayele, M., Tefera, H., and Nguyen, H.T. 1999. Amplified
fragment length polymorphism analysis of tef [Eragorstis tef (ZUCC.)
Trotter]. Crop Sci. 39: 819-824.
Barrett, B.A. and KidweIl, K.K. 1998. AFLP-based genetic diversity
assessment among wheat cultivars from the Pacific Northwest. Crop
Sci. 38: 1261-1271.
Barrett, B.A., KidweIl, K.K. and Fox, P.N. 1998. Comparison of AFLP and
pedigree-based genetic diversity assessment methods using wheat
cultivars from the Pacific Northwest. Crop Sci. 38: 1221-1278.
Beckman, J.S. and Soller, M. 1983. Restriction fragment length
polymorph isms in genetic improvements: methodologies, mapping
and costs. Theor. Appl. Genet. 67: 35-43.
Beeching, J.R., Marmey, P., Gavalda, M-C., Noirot, M., Hayson, H.R.,
Hughes, M.A. and Charrier, A. 1993. An assessment of genetic
diversity within a collection of cassava (Manihot escu/enta Crantz)
germplasm using molecular markers. Annuals of Botany 72:515-520.
Bekeie, E. 1984. Analysis of regional patterns of phenotypic diversity in the
Ethiopian tetraploid and hexaploid wheats. Hereditas 100:131-154.
Beta, T. 1998. Polyphenols and tannins in sorghum. In: INSORMILL
Workshop. Department of Food Science, University of Pretoria,
South Africa.
Beta, T., Rooney, L.W. and Waniska, R.D. 1995. Malting characteristics
of sorghum cultivars. Cereal Chemo 72:533-538.
Chapter 2 Literature Review 33
Boivin, K., Deu, M., Rami, J-F., Trouche, G. and Hamon, P. 1999.
Towards a saturated sorghum map using RFLP and AFLP markers.
Theor. Appl. Genet. 98:320-328.
Botstein, D., White, RL., Skolnick, M.H., Davis, RW. 1980. Construction
of a genetic map in man using restriction fragment length
polymorphisms. Am. J. Hum. Genet. 32:314-331.
Bretting, P.k. and Widrlechner, M.P. 1995. Genetic markers and
horticultural germplasm management. HortScienee 37:1349-1356.
Breyne, P., Rombaut, D., Van Gysel, A., Van Montagu, M. and Gerats,
M. 1999. AFLP analysis of genetic diversity within and between
Arabidapsis tha/iana ecotypes. Mol. Gen. Genet. 261: 627-634.
Brooke, C. 1958. The durra complex in the central highlands of Ethiopia.
Econ. Bot. 2: 192-204.
Brown, S.M., Hopkins, M.S., Mitchell, S.E., Senior, M.L., Wang, T.Y.,
Duncan, RR, Gonzalez-Candelas, F. and Kresovich, S. 1996.
Multiple methods for the identification of polymorphic simple
sequence repeats (SSRs) in sorghum [Sorghum bicolor (L.)
Moeneh]. Theor. Appl. Genet. 93: 190-198.
Buffo, RA., Weller, C.L. and Parkhurst, A.M. 1997. Optimisation of sulfur
dioxide and lactic acid steeping concentrations for milling of grain
sorghum. Transactions of the American Soc. of Agric. Engineers
40:1643-1648.
Butler, L.G. 1990. The nature and amelioration of the anti nutritional effects
of tannins in sorghum grain. In: Ejeta, G., Mertz, E.T., Rooney, L.,
Schaffert, R. and Yohe, J. (Eds.). Proc. Int. Conf. On Sorghum
Nutritional Quality. Purdue University, W. Lafayette, Indiana, pp.
191-205.
Caetano-Anolles, G. and Gresshoof, P.M. 1994. DNA amplification
fingerprinting of plant genomes. Methods in Molecular Cell Biology
5:62-70.
Chapter 2 Literature Review 34
Caetano-Anollés, G.C., Bassam, B.J. and Gresshoff, P.M. 1991. DNA
amplification fingerprinting: A strategy for genome analysis. Plant
Molecular Biology Reporter 9:553-557.
Cagampang, G.B. and Kirleis, A.W. 1984. Relationship of sorghum grain
hardness to selected physical and chemical measurements of grain
quality. Cereal Chemo 61 :100-105.
Cagampang, G.B. and Kirleis, A.W. 1985. Properties of Starches isolated
from sorghum floury and corneous endosperm. Starch/Staerke
37:253-257.
Chen, X., Temnykh, S., Xu, Y., Cho, Y.G. and McCouch, S.R. 1997.
Development of a microsatellite framework map providing genome-
wide coverage in rice (Oryza sativa L.). Theor. Appl. Genet. 95:553-
567.
Chittenden, M.L., Schertz, K.F., Lin, Y-R., Wing, R.A. and paterson,
A.H. 1994. A detailed RFLP map of Sorghum bicotor x S.
propinquum, suitable for high density mapping, suggests ancestral
duplication of sorghum chromosomes or chromosomal segments.
Theor. Appl. Genet. 87:925-933.
CIAT, 1993. Biotechnology Research Unit. Annual Report. Cali, Colombia.
Cui, Y.X., Xu, G.W., MagiII, C.W. and Schertz, K.F. 1995. RFLP-based
assay of Sorghum bicoïor (L.) Moench genetic diversity. Theor. Appl
Genet. 90:787-796.
Dahlberg, J.A., Zhang, X., Hart, G.E. and Mullet, J.E. 2002. Comparative
assessment of variation among sorghum germplasm accessions
using seed morphology and RAPD measurements. Crop Sci.
42:291-296.
de Wet, J.M.J., Harlan, J.R. and Price, E.G. 1976. Variability in Sorghum
bicolor. In: Harlan, J.R., de Wet, J.M.J. and Stemier, A.B.L. (eds.).
Origins of African Plant Domestication. Mouton, The Hague, pp.
453-463.
Chapter 2 Literature Review 35
Dean, R.E., Dahlberg, J.A., Hopkins, M.S. Mitchell, S.E. and Kresovich,
S. 1999. Genetic redundancy and diversity among 'Orange'
accessions in the U.S. national sorghum collection as assessed with
simple sequence repeat (SSR) markers. Crop Sci. 39:1215-1221.
Demissie, A., Bjernstad, A. and Kleinhofs, A. 1998. Restriction fragment
length polymorph isms in landrace barleys from Ethiopia in Relation
to geographic, altitude, and agro-Ecological factors. Crop Sci.
38:237 -243.
Dendy, D.A.V. 1995. Sorghum and Millets: Chemistry and Technology,
American Association of Cereal Chemists, Inc. St. Paul, Minnesota,
USA.
Deshpande, 5.5., Sathe, S.K., Salunkhe, D.K. and Cornforth, D.P. 1982.
Effects of dehulling on phytic acid, polyphenols, and enzyme
inhibitors of dry beans (Phaseoulus vulgaris L.) flours. Journal of
Food Sci. 47:1846-1849.
Deu, M., Gonzalez-De-Leon, D., Glaszmann, J.C., Degremont, I.,
Chantereau, J., Lanaud, C. and Hamon, P. 1994. RFLP diversity in
cultivated sorghum in relation to racial differentiation. Theor. Appl.
Genet. 88:838-844.
Devos, K.M., Bryan, G.J., Collins, A.J., Stephenson, P. and Gale, M.D.
1995. Application of two microsatellite sequences in wheat storage
proteins as molecular markers. Theor. Appl. Genet. 90:247-252.
Dewar, J., Von Ascheraden, S.R.F. and Taylor, J.R.N. 1993. Analysis of
hardness and other kernel characteristics of grain sorghum cultivars.
CSIR Report of the Sorghum Board, Pretoria, South Africa.
Djé, Y., Forcioli, D., Ater, M., Lefebvre, C. and Vekemans, X. 1999.
Assessing population genetic structure of sorghum landraces from
north-western Morocco using allozyme and microsatellite markers.
Theor. Appl. Genet. 99:57-163.
Chapter 2 Literature Review 36
Djê, Y., Heuertz, M., Lefébvre, C. and Vekemans, X. 2000. Assessment
of genetic diversity within and among germplasm accessions in
cultivated sorghum using microsatellite markers. Theor. Appl. Genet.
100: 918-925.
Doggett, H. 1988. Sorghum. Longman Group U.K. ltd. Essex, England.
Donini, P., Elias, M.L., Bougourd, S.M. and Koebner, R.M.D. 1997.
AFLP fingerprinting reveals pattern differences between DNA
extracted from different plant organs. Genome 40:521-526.
Dufour, P., Deu, M., Grivet, L., D'Hont, A., Paulet, F., Bouet, A.,
Lanuad, C., Glaszmann, J.C. and Hamon, P. 1997. Construction of
a composite sorghum genome map and comparison with sugarcane,
a related complex polyploid. Theor. Appl. Genet. 94 :409-418.
Edwards, K., Johnstone, C. and Thompson, C. 1991. A simple and rapid
method for the preparation of plant genomic DNA for PCR analysis.
Nucl. Acid. Res. 19:1349-1458.
Ejeta, G., Goldsbrough, P.B., Tuinstra, M.R., Grote, E.M., Menkir, A.,
Ibrahim, Y., Cisse, N., Weerasuriya, Y., Melakeberhan, A. and
Shaner, C.A. 1999. Molecular marker applications in sorghum:
Paper presented at Workshop on Application of Molecular Markers,
Ibadan, Nigeria, 16-20 August.
Franco, J., Crossa, J., Ribaut, J.M., Betran, J., Warburton, M.L. and
Khairallah, M. 2001. A method for combining molecular markers
and phenotypic attributes for classifying plant genotypes. Theor.
Appl. Genet. 103:944-952.
Gebrekidan, B. and Menkir, A. 1979. Ethiopian Sorghum Improvement
Project Progress Report No. 7. College of Agriculture, Addis Ababa
University, Addis Ababa, Ethiopia.
Gebrekidan, B., and Gebrehiwot, B. 1982. Sorghum injera preparations
and quality parameters. In: Rooney, L.W., and D.S. Murty, D.S.
(Eds.). Proc. Int. symp. on sorghum grain quality. ICRISAT,
Patancheru, A. P., India, pp.55-66.
Chapter 2 Literature Review 37
Geleta, N. 1997. Variability and association of morpho-agronomic
characters with reference to highland sorghum [Sorghum bicotor (L.)
Moench] landraces of Hararghie, Eastern Ethiopia. Alemaya
University of Agriculture, Alemaya, Ethiopia. (MSc. Thesis)
Ghebru, B., Schmidt, R.J., and Bennetzen, J.L. 2002. Genetic diversity
of Eritrean sorghum landraces assessed with simple sequence
repeats (SSR) markers. Theor. Appl. Genet. 105:229-236.
Gibson, T.S., Solah, V.A. and McCleary, B.V. 1997. A prcedure to
measure amylose in cereal starches and flours with Concanavalin A.
Journal of Cereal Science 25: 111-119.
Gowda, P.S.B., Xu, G.W., Frederiksen, R.A. and MagiII, C.W. 1995. DNA
markers for downey mildew resistance genes in sorghum. Genome
38:823-826.
Gower, J.C. 1971. A general coefficient of similarity and some of its
properties. Biometrics 27:857-871.
Grenier, C., Deu, M., Kresovich, S., Bramel-Cox, P.J. and Hamon, P.
2000. Assessment of Genetic Diversity in Three Subsets Constituted
from ICRISAT Sorghum Collection Using Random vs Non-Random
Sampling Procedures. Theor. Appl. Genet. 101: 197-202.
Hagerman, A.E. and Butler, L.G. 1981. Choosing appropriate methods
and standars for assaying tannin. J. Chemo Ecol. 15:1795-1810.
Hahn, D.H., Rooney, L.W. and Earp, C.F. 1984. Tannins and phenols of
sorghum. Cereal Foods World 29:776-779.
Hahn, D.H. and Rooney, L.W. 1986. Effect of genotype on tannins and
phenols of sorghum. Cereal Chemo 63:4-8.
Harlan, J.R. 1992. Crops and Man (second edition). American Society of
Agronomy and Crop Science Society of America, Madison, WI.
Harlan, J.R. and de Wet, J.M.J. 1972. A simplified classification of
cultivated sorghum. Crop Sci. 12:172-176.
Chapter 2 Literature Review 38
Hill, M., Witsenboer, H., Zabeau, M., Vos, P., Kesseli, Rand
Michelmore, R 1996. PCR-based fingerprinting using AFLPs as a
tool for studying genetic relationships in Lactuca spp. Theor. Appl.
Genet. 93:1202-1210.
House, L.R, Osmanzai, M., Gomez, M.I., Monyo, E.S. and Gupta, S.C.
1995. Agronomic principles. In: Dendy, D.A.v. (Ed.). Sorghum and
millets: Chemistry and technology. American Association of Cereal
Chemists, St. paul, MN, USA, pp. 27-67.
Hosaka, K. and Hanneman, E. 1994. Random amplified polymorphic
DNA markers detected in a segregating hybrid population of
Solanum chacoense x S. phureja. Jpn. J. Genet. 69:53-66.
Hoseney, R.C. 1994. Principles of Cereal Science and Technology
(Second edition). In: American Assoc. of Cereal Chemists, St. Paul,
USA, pp.7-21, 82-98, and 130-135.
Hulbert, S.H., Richter, T.E., Axtell, J.D. and Bennetzen, J.L. 1990.
Genetic mapping and characterization of sorghum and related crops
by means of maize DNA probes. Proc. Natl. Acad. Sci. 87:4251-
4255.
Incirli, A. and Akkaya, M.S. 2001. Assessment of genetic relationships in
durum wheat cultivars using AFLP markers. Genetic Resources and
Crop Evolution 48:233-238.
Jain, S.K. 1975. Population structure and the effects of breeding system.
In: Frankel, O.H. and Hawkes, J.G. (Eds.). Crop genetic resources
for today and tomorrow. Cambridge University Press, London,
pp.15-36.
Jones, C.J., Edwards, K.J., Castaglione, 5., Winfield, M.O., Sala, F.,
Van De Wiel, C., Bredemeijer, G., Vosman, B., Mathes, M., Daly,
A., Brettschneider, R, Bettini, P., Buiatti, M., Maestri, E.,
Malcevschi, A., Mamiroli, N., Aert, R., Volckaert, G., Rueda, J.,
Linacero, R, Vaazquez, A. and Karp, A. 1997. Reproducibilty
Chapter 2 Literature Review 39
testing of RAPD, AFLP, and SSR markers in plant by a network of
European laboratories. Molecular Breeding 3:381-390.
Karp, A., Kresovich, S., Bhat, K.V., Ayad, W.G. and Hodgkin, T. 1997.
Molecular tools in plant genetic resources conservation: a guide to
the technologies. IPGRI Technical Bulletin No. 2. International Plant
Genetic Resources Institute, Rome, Italy.
Karp, A., Seberg, O. and Buiatti, M. 1996. Molecular techniques in the
assessment of botanical diversity. Ann. Bot. 78:143-149.
Kaufman, L. and Rousseeuw, P.J. 1990. Finding groups in data: An
introduction to cluster analysis. A Wiley-Interscience Publication,
NY.
Klopfenstein, C.F. and R.C. Hoseney. 1995. Nutritional Properties of
Sorghum and Millets. In: Dendy, D.AV. (Ed.). Sorghum and Millets:
Chemistry and Technology. American Association of Cereal
Chemists, Inc. St. Paul, Minnesota, USA, pp.125-184.
Kochert, G. 1994. RFLP technology. In: Phillips, R.L. and Vasil, l.K. (Eds).
DNA-based markers in plants. Kluwer Academic Publishers,
Netherlands, pp.9-35.
Koebner, R.M.D., Devos, K.M. and Gale, M.D. 1994. Advances in the
application of genetic markers in plant breeding. Asian Seed, Chiang
Mai, Thailand.
Lásztity, R. 1996. The chemistry of cereal proteins. Bocaraton, Fla.:CRC
Press.
Li, C.D., Fatokun, C.A., Ubi, B., Singh, B.B. and Scoles, G.J. 2001.
Determining genetic similarities and relationships among cowpea
breeding lines and cultivars by microsatellite markers. Crop Sci.
41 :189-197.
Lin, D., Hubbes, M. and Zsuffa, L. 1993. Differentiation of poplar and
willow clones using RAPD fingerprints. Tree Physiology 14: 1097-
1105.
Chapter 2 Literature Review 40
Lin, Y.-R., Shertz, K.F. and Paterson, A.H. 1995. Comparative analysis of
QTL affecting plant height and maturity across the Poaceae, in
reference to an interspecific sorghum population. Genetics 140: 391-
411.
Liu, X.C. and Wu, J.L. 1998. SSR heterogenic patterns of parents for
marking and predicting heterosis in rice breeding. Molecular
Breeding 4:263-268.
Liu, Z. and Furnier, G.R. 1993. Comparison of allozyme, RFLP, and
RAPD markers for revealing genetic variation within and between
trembling aspen and big tooth aspen. Theor. Appl. Genet. 87:97-
105.
Liu, Z.W., Biyashev, R.M. and Saghai Maroof, M.A. 1996. Development
of simple sequence repeat markers and their integration into a
barley linkage map. Theor. Appl. Genet. 93: 869-876.
Lu, J., Knox, M.R., Ambrose, M.J., Brown, J.K.M. and Ellis, T.H.N.
1996. Comparative analysis of genetic diversity in pea assessed by
RFLP and PCR-based methods. Theor. Appl. Genet. 93: 1103-1111.
Maheswaran, M., Subudhi, P.K., Nandi, S., Xu, J.C., Parco, A., Yang,
D.C., Huang, N. 1997. Polymorphism, distribution, and segregation
of AFLP markers in a double haploid rice population. Theor. Appl.
Genet. 94:39-45.
Maughan, P.J., Saghai-Maroof, M.A., Buss, G.R., Huestis, G.M. 1996.
Amplified fragment length polymorphism (AFLP) in soyabean:
species diversity, inheritance, and near-isogenic line analysis.
Theor. Appl. Genet. 93:392-401.
Mazur, B.J. and Tingey, S.V. 1995. Genetic mapping and introgression of
genes of agronomic importance. Current Opinion in Biotechnology
6:175-182.
Melake-Berhan, A., Hulbert, S.H., Butler, L.G. and Bennetzen, J.L.
1993. Structural and evolution of the genomes of Sorghum bicotor
and Zea mays. Theor. Appl. Genet. 86:598-604.
Chapter 2 Literature Review 41
Menkir, A., Ejeta, G., Butler, L. and Melakeberhan, A. 1996. Physical
and chemical kernel properties associated with resistance to grain
maid in sorghum. Cereal Chemo 73:613-617.
Menkir, A., Goldsbrough, P. and Ejeta, G. 1997. RAPD based
assessment of genetic diversity in cultivated races of sorghum. Crop
Sci. 37:564-569.
Miller, J.C. and Tanksley, S.O. 1990. RFLP analysis of phylogenetic
relationships and genetic variation in the genus Lycopersicon.
Theor. Appl. Genet. 80:437-448.
MorelI, M.K., Peakall, R., Appels, R., Preston, l.R., and Lloyd, H.L.
1995. DNA profiling techniques for plant variety identification.
Australian J. Exp. Agric. 35:807-819.
Negash, A., Tsegaye, A., Treuren, R.van, and Visser, B. 2002. AFLP
analysis of Enset clonal diversity in south and southwestern Ethiopia
for conservation. Crop Sci. 42: 1105-1111.
Negassa, M. 1986. Estimates of phenotypic diversity and breeding
potential of Ethiopian wheats. Hereditas 104: 41-48.
Nei, M. 1987. Molecular evolutionary genetics. Colombia University Press,
New York.
Nei, M. and Li, W.H. 1979. Mathematical model for studying genetic
variation in terms of restriction endonucleases. Proc. Natl. Acad. Sci.
(USA) 79:5269-5273.
NovelHe, L. 1982. Fermented porridges. In: Rooney, L.W. and Murty, D.S.
(Eds.). Int. Symp. on Sorghum Grain Quality. ICRISAT, Patancheru,
India, pp. 121-128.
Nybom, H.Y., Rogstad, S.H. and Schaal, B.A. 1990. Genetic variation
detected by use of the M13 'DNA fingerprint' probe in Malus, Prunus
and Rubus (Rosacea). Theor. Appl. Genet. 79: 153-156.
Chapter 2 Literature Review 42
Pakniyat, H., Powell, W., Baird, E., Handley, L.L., Robinson, D.,
Scrimgeour, C.M., Nevo, E., Hackett, C.A., Caligra, P.D.S. and
Forster, B.P. 1997. AFLP variation in wild barley with reference to
salt tolerance and associated ecogeography. Genome 40:332-341.
Paterson, A.H., Tanksley, S.O. and Sorrells, M.E. 1991. DNA markers in
plant improvement. Adv. Agron. 46:39-90.
Paterson, A.H., Lin, Y., Li, Z., Schertz, K.F., Ooebley, J.F., Pinson,
S.R.M., Liu, S., Stanzel, J.W. and Irvine, J.E. 1995. Convergent
domestication of cereal crops by independent mutations at
corresponding genetic loci. Science 269:1714-1718.
Paul, S., Wachira, F.N., Powell, W. and Waught, R. 1997. Diversity and
genetic differentiation among populations of Indian and Kenyan tea
(Camellia sinensis (L.) O. kuntze) revealed by AFLP markers. Theor.
Appl. Genet. 94:255-263.
Pejic, I., Ajmone-Marsan, P., Morgante, M., Kozumplick, V., Castiglioni,
P., Taramino, G. and Motto, M. 1998. Comparative analysis of
genetic similarity among maize inbred lines detected by RFLPs,
RAPDs, SSRs, and AFLPs. Theor. Appl. Genet. 97: 1248-1255.
Pereira, M.G. and Lee, M. 1995. Identification of genomic regions affecting
plant height in sorghum and maize. Theor. Appl. Genet. 90:380-388.
Pereira, M.G., Lee, M., Bramel-Cox, P., Woodman, W., Doebly, J. and
Whitkus, R. 1994. Construction of a RFLP map in sorghum and
comparative mapping in maize. Genome 37:236-243.
Perkin-Elmer. 1996. AFLP Plant Mapping Kit-the PCR marker of choice
for plant mapping.
Powell, W., Morgante, M., Andre, C., Hanafey, M., Vogel, J., Tingey, S.
and Rafalski, A. 1996. The comparison of RFLP, RAPD, AFLP and
SSR (microsatellite) markers for germplasm analysis. Mol. Breed.
2:225-238.
Chapter 2 Literature Review 43
Powell, W., Thomas, W.T.B., Baird, E., Lawrence, P., Booth, A.,
Harrower, B., McNicol, J.W. and Waugh, R. 1997. Analysis of
quantitative traits in barley by the use of amplified fragment length
polymorphisims. Heredity 79: 48-59.
Prasada Rao, K.E. and Ramanatha Rao, V. 1995. Use of characterization
data in developing a core collection of sorghum. In: Hodgkin, T.,
Brown, A.H.D., van Hintum, Th.J.L. and Morales, E.A.V. (Eds.). Core
collection of plant genetic resources. IBPGRI, Sayee Publishing, and
John Wiley and Sons, Chichester, pp. 109-115.
Pushpamma, P. & Vogel, S.M. 1982. Consumer acceptance of sorghum
and sorghum products. In: Rooney, L.W. and Murty, D.S. (Eds.).
International Symposium on Sorghum Grain Quality. ICRISAT,
Patancheru, India, pp.341-353.
Qamaruz-Zaman, F., Fay, M.F., Parker, J.S. and Chase, M.W. 1997. The
use of AFLP fingerprinting in conservation genetics: a case study of
Orchis simia (Orchidaceae). Preliminary report. Royal Botanical
Gardens, Kew.
Ragab, R.A., Oronavalli, S., Saghai Maroof, M.A. and Vu, Y.G. 1994.
Construction of sorghum RFLP linkage map using sorghum and
maize DNA probes. Genome 37:590-594.
Ring, S.H., Akingbala, J.O. and Rooney, L.W. 1982. Variation in amylose
content among sorghums. In: Rooney, L.W. and Murty, D.S. (Eds.).
International Symposium on Sorghum Grain Quality. ICRISAT,
Patencheru, India, pp.269-279.
Roder, M.S., Plaschke, J., Konig, S.U., Borner, A., Sorrells, M.E.,
Tanksley, S.O. and Ganal, M.W. 1995. Abundance, variability and
chromosomal location of microsatellites in wheat. Mol. Gen. Genet.
246:327 -333.
Rohlf, F:J. 1993. NTSYS-pc numerical taxonomy and multivariate analysis
system. Exerer Software, Setauket, NY.
Chapter 2 Literature Review 44
Rolfs, A., Schuiler, I., Finckh, U. and Weber-Rolfs, I. 1992. PCR: Clinical
Diagnostics and Research. Springer-Verlag, New York.
Rooney, L.W. 1978. Sorghum and pearl millet lipids. Cereal Chemo
55:584-590.
Rooney, L.W. and Miller, F.R. 1982. Variation in the structure and kernel
characteristics of sorghum. In: Rooney, L.W., and Murty, D.S. (Eds.).
International symposium on sorghum grain quality. ICRISAT,
Patancheru, pp. 143-162.
Ruano, G., Brash, D.E. and Kidd, K.K. 1991. The first few cycles.
Amplifications: A Forum for PCR users 4:5-7.
Russel, J.R., Fuller, J.D., Macaulay, M., Hatz, B.G., Jahoor, A., Powell,
W. and Waugh, R. 1997. Direct Comparison of levels of genetic
variation among barley accessions detected by RFLPs, AFLPs,
SSRs and RAPDs. Theor. Appl. Genet. 95: 714-722.
Saiki, R.K., Schart, S., Faloona, F., Mullis, KB., Horn, G.T., Erlich, H.A.
and Arnheim, N. 1985. Enzymatic amplification of ~-globin genomic
sequences and restriction site analysis for diagnosis of sickle cell
anemia. Science 230: 1350-1354.
Schut, J.W., Qi, X. and Stam, P. 1997. Association between relationship
measures based on AFLP markers, pedigree data and
morphological traits in barley. Theor. Appl. Genet. 95:1161-1168.
Senior, M.L., Murphy, J.P., Goodman, M.M. and Stuber, C.W. 1998.
Utility of SSRs for determining genetic similarities and relationships
in maize using an agarose gel system. Crop Sci. 38:1088-1098.
Serna-Saldivar and Rooney, L.W. 1995. Structure and chemistry of
~orghum and millets. In: Dendy, D.A.V. (Ed.). Sorghum and Millets
Chemistry and Technology. American Assoc. of Cereal Chemists,
St. Paul, USA, pp. 69-124.
Sharma, S.K., Knox, M.R. and Ellis, T.H.N. 1996. AFLP analysis of the
diversity and phylogeny of Lens and its comparison with RAPD
analysis. Theor. Appl. Genet. 93:751-758.
Chapter 2 Literature Review 45
Singh, A., Negi, M.S., Rajagopal, J., Bhatia, S., Tomar, U.K.,
Srivastava, P.S. and Lakshmikumaran, M. 1999. Assessment of
genetic diversity in Azadirachta indica using AFLP markers. Theor.
Appl. Genet. 99:272-279.
Singh, U. and Singh, B. 1991. Functional properties of sorghum-peanut
composite flour. Cereal Chemo 68:460-463.
Smith, J.S.C. 1984. Genetic variability within U.S. hybrid maize:
multivariate analysis of isozyme data. Crop Sci. 24: 1041-1046
Smith, J.S.C. and Smith, O.S. 1992. Fingerprinting crop varieties. Adv.
Agron.47:85-141.
Smith, J.S.C., Chin, E.C.L., Shu, H., Smith, O.S., Wall, S.J., Senior,
M.L., Mitchell, S.E., Kresovitch, S. and Ziegle, J. 1997. An
evaluation of the utility of SSR loci as molecular markers in maize
(Zea mays L.): Comparisons with data from RFLPs and pedigree.
Theor. Appl. Genet. 95: 163-173.
Smith, J.S.C., Kresovich, S., Hopkins, M.S., Mitchell, S.E., Dean, R.E.,
Woodman, W.L., Lee, M. and Porter, K. 2000. Genetic diversity
among elite sorghum inbred lines assessed with simple sequence
repeats. Crop Sci. 40:226-232.
Smulders, M.J.M., Bredemeijer, G., Rus-Kortekaas, W., Arens, P. and
Vosman, B. 1997. Use of short microsatellites from database
sequences to generate polymorph isms among Lycopersicon
esculentum cultivars and accessions of other Lycopersicon species.
Theor. Appl. Genet. 97:264-272.
Soller, M. and Beckmann, J.S. 1983. Genetic polymorphism in varietal
identification and genetic improvement. Theor. Appl. Genet. 67:25-
33.
Song, k.M., Osborn, T.C. and William, P.H. 1988. Brassica taxonomy
based on nuclear restriction fragment polymorph isms (RFLPs).
Theor. Appl. Genet. 75:784-794
Chapter 2 Literature Review 46
Sooliman, A.O.G. 1993. Sorghum processing at the crossroads in South
Africa. In: Taylor, J.R.N., Randall, P.G. and Viljoen, J.H. (Eds.).
Cereal Science and Technology: Impact on a Changing Africa. The
CSIR, Pretoria, pp.497-507.
Stemier, A.B.L., Harlan, J.R. and de Wet, J.M.J. 1977. The sorghums of
Ethiopia. Econ. Bot. 31 :446-460.
Subudhi, P.K. and Nguyen, H.T. 2000a. New Horizons in Biotechnology.
In: Wayane Smith, C. (Ed.). Sorghum: Origin, History, Technology,
and production. John Wily and Sons, Inc., pp.349-397.
Subudhi, P.K. and Nguyen, H.T. 2000b. Linkage group alignment of
sorghum RFLP maps using a common RIL mapping population.
Genome 43:240-249.
Tao, Y., Manners, J.M., Ludlow, M.M. and HenzeIl, R.G. 1993. DNA
polymorph isms in grain sorghum [Sorghum bicotor (L.) Moeneh].
Theor. Appl. Genet. 86:679-688.
Tao, Y.Z., Jordan, D.R., HenzeIl, R.G. and Melntyre, C.L. 1998.
Construction of a genetic map in sorghum RIL population using
probes from different sources and its comparison with other
sorghum maps. Aust. J. Agric. Res. 49:729-736.
Tautz, D. 1989. Hypervariability of simple sequences as a general source
for polymorphic DNA markers. Nucl. Acids Res. 17:6463-6471.
Tautz, D. and Renz, M. 1984. Simple sequences are ubiquitous repetitive
components of eukaryotic genomes. Nucl. Acids Res. 12:4127-4138.
Taylor, J.R.N. and Dewar, J. 1996. Recent developments in sorghum
malting quality assessment. In: Randall, P.C. (Ed.). Grain Quality
Assessment. Paper presented at the ICC-SA Seminar. The Maize
Board, Pretoria, pp. 65-69.
Taylor, J.R.N. and Schussler, L. 1986. The protein composition of the
different anatomical parts of sorghum grain. J. Cereal Sci. 4:361-
369.
Chapter 2 Literature Review 47
Taylor, J.R.N., Dewar, J., Taylor, J. and Von Ascheraden, R.F. 1997.
Factors affecting the porridge-making quality of South African
Sorghums. Journal of the Science of Food and Agriculture 73:464-
470.
Teshome, A., Baum, B.R., Fahrig, L., Torrance, J.K., Arnason, T.J. and
Lambert, J.D. 1997. Sorghum [Sorghum bicolor (L.) Moench]
landrace variation and classification in North Shewa and South
Welo, Ethiopia. Euphytica 97:255-263.
Thomas, M.R. and Scott, N.S. 1993. Microsatellite repeats in grapevine
reveal DNA polymorph isms when analysed as sequence-tagged
sites (STSs). Theor. Appl. Genet. 70:1-6.
Travis, S.E., Maschinski, J. and Keim, P. 1996. An analysis of genetic
variation in Astragalus cremnophylax var. cremnophylax, a critically
endangered plant, using AFLP markers. Mol. Ecol. 5:735-745.
Tuinstra, M.R., Ejeta, G. and Goldsbrough, P.B. 1998. Evaluation of
near-isogenic sorghum lines contrasting for OTL markers associated
with drought tolerance. Crop Sci. 38:835-842.
Tuinstra, M.R., Grote, E.M., Goldsbrough, P.B. and Ejeta, G. 1997.
Genetic analysis of post-flowering drought tolerance and
components of grain development in sorghum. Mol. Breed. 3:439-
448.
Tuinstra, M.R., Grote, E.M., Goldsbrough, P.B., and Ejeta, G. 1996.
Identification of quantitative trait loci associated with pre-flowering
drought tolerance in sorghum. Crop Sci. 36:1337-1344.
Vos, P., Hogers, R., Bleeker, M., Reijans, M., Van de Lee, T., Hornes,
M., Frijters, A., Pot, J., Peleman, J., Kuiper, M. and Zabeau, M.
1995. AFLP: a new technique for DNA fingerprinting. Nucl, Acids
Res. 23:4407-4414.
Waniska, R.D., Poe, J.H. and Bandyopadhhyay, R. 1989. Effects of
growth conditions on grain molding and phenols in sorghum
caryopsis. J. Cereal Sci. 10:217-225.
Chapter 2 Literature Review 48
Wankhede, O.M., Deshpahde, H.W., Gunjal, B.B., Bhosale, M.M., Patil,
H.B., Gahilod, A.T., Sawate, A.R. and Walde, S.G. 1989. Studies
on physiochemical pasting characteristics and amyolytic
susceptibility of starch from sorghum (S. bicolor (L.) Moench) grains.
Starch/Staerke 41: 123-127.
Warsi, AS. and Wright, B.C. 1973. Effects of rates and methods of
nitrogen application on the quality of sorghum grain. Indian J. Agric.
Sci.43:722-726.
Watson, S.A, and Hirata, Y. 1955. The wet milling properties of grain
sorghum. Agron. J. 47:11-15.
Waugh, R. and Powell, W. 1992. Using RAPD markers for crop
improvement. Trends Bio/technol. 10:186-191.
Weber, J. and May, P.E. 1989. Abundant class of human DNA
polymorphisms which can be typed using the polymerase chain
reaction. Am J. Hum Genet. 44:388-396.
....
Welsh, J. and McClelland, M. 1990. Fingerprinting genomes using peR
with arbitrary primers. Nucl. Acids Res. 18:7213-7218.
Williams, J.G.K., Kubelic, AR., Livak, K.J., Rafalski, J.A, and Tingey,
S.V. 1990. DNA polymorph isms amplified by arbitrary primers are
useful as genetic markers. Nucl. Acids Res. 18:6531-6535.
Williams, J.G.K., Rafalski, J.A and Tingey, S.V. 1993. Genetic analysis
using RAPD markers. Meth. Enzymol. 218:704-780.
Winter, P. and Kahl, G. 1995. Molecular marker technologies for plant
improvement. World J. Microb. & Biotech. 11:438-448.
Wuhib, E. and Tekabe, F. 1987. Sorghum Utilization Project Final Report,
Ethiopian Nutrition Institute, Addis Ababa, Ethiopia.
Xu, G.W., Magill, C.W., Schertz, K.F. and Hart, G.E. 1994. A RFLP
linkage map of Sorghum bicoiour (L.) Moench. Theor. Appl. Genet.
89:139-145.
Yamamoto, T., Nishikawa, A and Oeda, K. 1994. DNA polymorph isms in
Oryza sativa L. amplified by arbitrary primer peR. Euphytica 78: 143-
148.
Chapter 2 Literature Review 49
Yang, P. and Seib, P.A. 1995. Low-input wet-milling of grain sorghum for
readily accessible starch and animal feed. Cereal Chemo 72:498-
503.
Yang, W., de Oliveira, A.C., Godwin, I., Schertz, K. and Bennetzen, J.L.
1996. Comparison of DNA marker technologies in characterizing
plant genome diversity: variability in Chinese sorghums. Crop Sci.
36:1669-1676.
Young, N.O. 1994. Constructing a plant genetic linkage map with DNA
markers. In: Ronaid, L.P. and Indra, K.v. (Eds.). Kluwer Academic
Publishers, Netherlands, pp.39-58.
Yu, K. and Pauls, K.P. 1992. Optimization of the PCR program for RAPD
analysis. Nucl. Acids Res. 20:2606.
Yu, K., Park, S.J. and Poysa, V. 1999. Abundance and variation of
microsatellite DNA sequences in beans (Phaseo/us and Vigna).
Genome 42:27-34.
Yu, K., Van Deynze, A. and Pauls, K.P. 1993. Random amplified
polymorphic DNA (RAPD) analysis. In: Methods in plant molecular
biology and biotechnology. pp. 287-301. CRC Press, Inc.
Zabeau, M. and Vos, P. 1993. Selective restriction fragment amplification:
a general method for DNA fingerprinting. European patent
Application EP 534858A 1.
!)1"
50
CHAPTER 3
PHENOTYPIC DIVERSITY FOR MORPHO-AGRONOMICAL TRAITS IN
SORGHUM
Abstract
Forty-five sorghum accessions growing in the eastern highlands of Ethiopia
were evaluated for phenotypic diversity. Ten qualitative and 16 quantitative
traits were recorded for all the accessions. Phenotypic frequencies
between the accessions from each of the 10 aanaas / woredas and AU,
grouped in 10 localities were tabulated. Phenotypic diversity index, H', was
analysed and the result indicated the between localities component of
diversity to be relatively smaller than the variation in H' among characters
within localities. Multivariate methods, including clustering and principal
component analyses were used on quantitative traits data to estimate the
patterns and distribution of phenotypic variation. Cluster analysis grouped
the accessions into seven cluster groups. Further among the 16 principal
components involved, the first eight principal components explained 78% of
the total variation between the accessions. Grain yield, panicle weight,
kernel number per panicle and number of primary branches being the most
important traits in the first principal component with 19% of the total
variation, and leaf traits in the second with 17% total variation. The results
both in qualitative and quantitative traits data showed that there is a wide
morpho-agronomical diversity among the accessions studied. The sorghum
improvement programme of AU can use this information for its future
breeding strategy and the conservation of these resources.
3.1 Introduction
Information on the genetic diversity within and among closely related crop
species is essential for rational use and management of genetic resources.
It is particularly useful in characterising individual accessions and cultivars,
in detecting genetic materials with novel genes and thereby rescuing them
Chapter 3 Morpho-agronomical traits 51
from erosion, and as a general guide in selecting parents for crossing in
breeding programmes. Most of the genetic diversity of food crops in
Ethiopia is traditionally maintained by farmers in situ (Worede, 1988). Local
farmers deliberately, and unconsciously too, grow several varietal forms
together to add variety to their diet and also to reduce the risk of economic
loss from new parasites or insect pests or in the event of unusual
environmental conditions (Worede, 1988; Bekeie, 1984, Brooke, 1958).
Stemier et al. (1977) reported the presence of four of the primary races
(except Kafir) in Ethiopia and described their distribution within the country.
However, many valuable landraces of sorghum either have been lost or
under serious risk due to various human and environmental factors
(Teshome, 2001). Investigating the degree of character variation and
distribution of sorghum in Ethiopia, where it was domesticated (Vavilov,
1951) and diversified (Kebede, 1991; Doggett, 1988; Harlan, 1969) is
essential.
Categorizing germplasm accessions into morphologically similar, and
presumably genetically similar, groups is most useful when the population
structure in a collection is unknown (Marshal and Brown, 1975). Genetic
relationships among a large number of accessions can be summarised
using cluster analysis to place similar accessions into groups. Phenotypic
diversity index of morphological characters and/or multivariate analysis of
quantitative characters has been used previously to measure genetic
relationships within cereal crop species. Examples include tef (Assefa et
al., 1999), barley (Demissie and Bjornstad, 1996; Negassa, 1985; Bekeie,
1984; Tolbert et al., 1979), tetraploid wheat (Tesfaye et al., 1991; Bechere
et al., 1996) and Ethiopian wheats (Negassa, 1986).
In sorghum, morphological characters were used to estimate phenotypic
variation among landrace accessions grown in north Shewa and south
Welo regions of Ethiopia (Teshome et al., 1997). Based on morphological
variation assessment of 415 sorghum accessions collected from different
regions of Ethiopia and Eritrea have shown significant levels of variation
within the regions of origin and within the adaptation zones (Ayana and
it-'\\ qL~ CjJ-
Chapter 3 Morpho-agronomical traits 52
Bekeie, 1998, 1999). In a previous investigation, we analysed the level of
morpho-agronomic traits variability in sorghum landraces from the eastern
highland regions of Ethiopia based on quantitative traits data (Geleta,
1997). However, the extent and patterns of phenotypic variation that exist
among and within sorghum accessions of the regions have not been
assessed using Shannon-Weaver diversity index and multivariate analyses.
In this study, both qualitative and quantitative traits were used to estimate
the levels of variation among the sorghum accessions grown in the eastern
highlands of Ethiopia. The main objectives of the study were to: (1)
estimate the extent of genotypic diversity among sorghum accessions
based on 10 qualitative and 16 quantitative traits data, and (2) assess the
regional patterns of phenotypic diversity using qualitative and quantitative
traits data.
3.2 Materials and methods
3.2.1 Qualitative traits
3.2.1.1 Plant materials
Forty-five accessions of sorghum were used and the information on the
collection sites is given (Table 3.1). Among the 45 sorghum accessions 34
were collected from farmers' fields in 10 Aanaas/Woredas of the highlands
of eastern Ethiopia. Five each were elite breeding lines and improved
cultivars (through selection), and one local variety acquired from the
Sorghum Improvement Program (SIP), Alemaya University (AU). The
accessions/cultivars were collected and chosen on representation basis,
stratified systematic sampling method to a given range of geographic area,
a range of morpho-agronomic traits and material under development, and
potential interest in SIP.
Chapter 3 Morpho-agronomical traits 53
3.2.1.2 Methods
The evaluation was conducted at the glasshouse of Plant Breeding,
University of the Free State during 2001. Two plants of each accession
were grown in an 8 f pot, replicated three times. To categorise each
accession morphologically, Sorghum Descriptors (IBPGR/ICRISAT, 1993)
was employed. Table 3.2 lists the qualitative traits, their descriptors and the
codes used in the analyses. Each accession was scored for the most
frequent character-state. Seed colour, glume colour and leaf midrib colour
were examined and scored using the Munsell colour firm (1990).
3.2.1.3 Data analysis
Phenotypic frequency distributions of the characters were worked out for all
the accessions and aanaas/ woredas. The Shannon-Weaver diversity index
(H') was computed using the phenotypic frequencies to assess the
phenotypic diversity for each character for all accessions. The Shannon-
Weaver diversity index as described by Perry and Mclntosh (1991) is given
as:
n
H' = 1-2: Pi logePi
i=1
where, Pi is the proportion of accessions in the ith class of an n-class
character and n is the number of phenotypic classes of traits. Each H'
value was divided by its maximum value (logen) and normalised in order to
keep the values between 0 and 1. By pooling various characters across the
collection sites, the additive properties of H' were used to evaluate diversity
of localities and characters within the population.
Chapter 3 Morpho-agronomical traits 54
3.2.2 Quantitative traits
3.2.2.1 Location of the study
To study the quantitative traits variability the sorghum accessions were
grown at the Department of Plant Sciences Experimental field, AU,
Alemaya during the 2000 and 2001-growing seasons. Alemaya is located
at 9°26 'N latitude, 43°03 'E longitude with an altitude of 1980 m above sea
level. In Ethiopia, Alemaya is one of the recommended testing sites for the
highland sorghum germ plasm characterisation. The area receives an
average annual rainfall of about 800 mm and has a temperature ranging
between 10 to 24°C (Alemaya Weather Station). The accessions were
grown in three rows of 5m long and 0.75 m between rows plot, with three
replications in randomized complete block design. Within every row, plant-
to-plant spacing was 0.20 m.
3.2.1.4 Plant materials
The 45 accessions listed in Table 3.1 were used for this study.
3.2.2.3 Parameters measured
Data were collected for 14 morpho-agronomic characters (Table 3.3). Two
derived characters were also estimated for each accession from the above
measurements (i) grain number per panicle, and (ii) threshing percentage.
For every accession, data was recorded from the middle rowan five
randomly selected individual plants, except for days to 50% flowering,
which was recorded on plot basis (Table 3.3). For leaf characteristics
measurement, a procedure developed by Stickler et al. (1961) was used.
Chapter 3 Morpho-agronomical traits 55
Table 3.1 Local / cultivar name, collection site, altitude and sample status of
sorghum samples used in the study.
Accession Local / cultivar Collection site Altitude Sample
number. name (Aanaa/ Woreda)' (m) status/
1 Wagare Chinhakssen 1950 LR
2 Wagare Chinhakssen 1970 LR
3 Wagare Haro Maya 2120 LR
4 Muyra adi Haro Maya 2120 LR
5 Muyra Kurfa Challe 2220 LR
6 Fandisha duudaa Kurfa Challe 2310 LR
7 Fandisha faca'a Bedeno 2050 LR
8 Wagare Meta 2050 LR
9 Hamedaya Meta 2180 LR
10 Muyra Meta 2180 LR
11 Abedelota Deder 2080 LR
12 Ambajeetee Deder 2080 LR
13 Muyra Tulo 2000 LR
14 Dassile Tulo 1900 LR
15 Hanchiro Tulo 2100 LR
16 Wagare Tulo 1940 LR
17 Key Fendisha Tulo 2140 LR
18 Sharif Tulo 1960 LR
19 Alaa guuraacha Tulo 1940 LR
20 Suuta naqaaphu Tulo 1900 LR
21 Qirendaye Tulo 2200 LR
22 Alegid Tulo 1900 LR
23 Fandisha Doba 1900 LR
24 Fandisha gababa Doba 2230 LR
25 Bulo Doba 2010 LR
26 Janga Doba 2000 LR
27 Harka basi Doba 2000 LR
28 Shafare Doba 2000 LR
29 Shafare Chiro 2270 LR
30 Gababe Chiro 2240 LR
31 Warabi Chiro 1900 LR
32 Zangada Habro 1940 LR
33 Zangada Habro 1940 LR
34 Dassile Habro 1900 LR
35 ETS 721 AU BE
36 ETS 993 AU BE
37 ETS 789 AU BE
38 ETS 804 AU BE
39 Wotet begunche AU BE
40 AL-70 AU IC
41 ETS 2752 AU IC
42 Chiro AU IC
43 ETS 1005 AU IC
44 ETS 576 AU IC
45 Long mu~ra AU LC
, Administrative unit
2 LR = Landrace, BE = Breeding entry, IC = Improved cultivar, LC = Local cultivar
Chapter 3 Morpho-agronomical traits 56
Ch = Chinhakssen
Hm = Hare Maya
Kc = Kurfa Challe
Bn = Bedena
Mt = Meta
Dd = Deder
Db = Daba
TI = Tula
Cr = Chira
Hb = Habro
Figure 3.1 Geographic locations where the sorghum landrace accessions
used in the study were collected. (The sites are located in Oromiya and
Somali Regional States of Ethiopia.)
Chapter 3 Morpho-agronomical traits 57
Table 3.2 Character, descriptor and codes used for characterisation of
sorghum accessions.
Character Descriptor & Code
Plant colour Pigmented (1) and Tan (2)
Stalk juiciness Dry (0) and Juicy (1)
Leaf midrib colour White (1), Dull green (2) & Yellow (3)
Inflorescence exsertion Slightly exserted (1), Exserted (2),
Well-exserted (3), and Peduncle re-
curved/ goose (4)
Panicle compactness & shape Very lax panicle (1), Very loose erect
primary branches (2), Loose erect
primary branches (4), Semi-loose erect
primary branches (6), Semi-compact
elliptic (8), Compact elliptic (9), and
Compact oval (10)
Awns (at maturity) Absent (0) and Present (1)
Glume colour White (1), Yellow (2), Grey-orange
group (3), Orange-red (4), Purple (5),
Black (6), and Grey (7)
Grain covering 25% grain covered (1), and 50% grain
covered (3)
Grain colour White (1), Yellow (2), Red (3), Light
brown (4), Brown (5), Red brown (6),
Dark brown (7), Grey (8), and Straw (9)
Endosperm texture Completely corneous (1), Mostly
corneous (3), Intermediate (5), Mostly
starchy (7), and Completely starchy (9).
Chapter 3 Morpho-agronomical traits 58
Table 3.3 List, code and descriptions of the quantitative characters
recorded in the study.
Character Code Description
Days to 50% flowering (count) DF From emergence to when 50% of plants
have started flowering.
Leaf number (count) LN Count of total number of leaves per plant
(main stalk).
Leaf length (cm) LL Length of the third or fourth leaf from the
flag leaf.
Leaf width (cm ) LW Width of the third or fourth leaf from the
flag leaf.
Leaf area (ern") LA Area of the third or fourth leaf from the
flag leaf, computed as (LL x LW x 0.747)
suggested by Stickler et al. (1961).
Internode length (cm) 1NL Length of the third internode counted
from the ground surface.
Leaf sheath Length (cm) LSL Length measured on leaf sheath found
on the third internode from the ground.
Plant height (cm) PHt Height of the main stalk from the ground
to the tip of the panicle.
Panicle length (cm) PL Length of panicle from its base to tip.
Panicle width (cm) PW Width of panicle in natural position at the
widest part.
Number of primary branches NPBP Number of branches arising directly from
per panicle (count) the rachis of the panicle.
Head weight (g) HWt Weight of head (panicle) before
threshing.
Grain yield per panicle (g) GYPP Weight of grain per panicle.
1000-seed weight (g) TSWt Weight of 1000 seed counts.
Threshing percent (%) TP The ratio of grain weight per panicle to
the head weight of the same multiplied
by 100.
Grain number per panicle GNPP The ratio of grain weight per panicle to
(count) the average 1aaa-seed weight per
panicle of the same multiplied by 1000.
Chapter 3 Morpho-agronomical traits 59
3.2.2.4 Statistical analysis
Raw data was entered into a Microsoft Excel spreadsheet and mean values
were calculated for each accession, and the mean values were imported
into the Number Cruncher Statistical Systems (NCSS, 2000) program. The
16 quantitative traits data were used as columns and the 45 accessions as
rows. Pair-wise genetic distance estimates were obtained between
accessions and used to group them using UPGMA method. Results were
obtained from UPGMA analysis as dendrogram.
Principal component analysis (PCA) was also carried out on the same data
employing the multivariate analysis option of the NCSS program. PCA is a
data analysis tool that is usually used to reduce the dimensionality (number
of variables) of a large number of interrelated variables, while retaining as
much of the information (variation) as possible. PCA calculates an
uncorrelated set of variables (factors or pc's). These factors are ordered so
that the first few retain most of the variation present in all of the original
variables. The PCA was performed on the correlation matrix rather than
covariance matrix.
3.3 Results and discussion
3.3.1 Qualitative traits
The amount of phenotypic diversity estimates based on Shannon-Weaver
diversity index (H') and its partitioning within and between collection sites
are shown in Table 3.4. The 10 characters differed in their distribution as
well as the amount of variation. The overall average phenotypic diversity
(H') among accessions was 0.71, varying from 0.36 (grain covering) to 0.95
(grain colour). Leaf midrib colour, stalk juiciness, awns, and grain covering
were relatively monomorphic, while plant colour was intermediate, and
inflorescence exsertion, panicle compactness and shape, glume colour,
grain colour, and endosperm texture were highly polymorphic (Table 3.4).
Chapter 3 Morpho-agronomical traits 60
The distribution of phenotypic frequencies for stalk juiciness and grain
covering at collection site level showed only weak clinal variation among
aanaas/ woredas. The majority of the accessions (42 out of 45) were non-
juicy/dry.
The H' pooled across characters by region of collection ranged from 0.38 to
0.73 (Table 3.5). The aanaas that had the highest H' were Kurfa Challe,
Chiro, and accessions from AU. The lowest values of H' are from Doba
(0.38), Habro (0.45) and Meta (0.46). Here, low H' values were not
necessarily associated with lack of adequate sample size. The importance
of the sample size for phenotypic diversity suggests, therefore that the best
way to sustain biodiversity in sorghum fields depends on the many options
to be offered to the local farmers by national government and/or
international organisations.
Table 3.4 Estimates of H', partitioning into within and between collection
sites for 10 qualitative characters in 45 sorghum accessions.
Character H' Hel Hel/H' (H'-Hel)/H'
Leaf midrib colour (LMC) 0.55 0.36 0.65 0.35
Plant colour (PC) 0.73 0.38 0.52 0.48
Stalk juiciness (SJ) 0.37 0.10 0.27 0.73
Inflorescence exsertion (IE) 0.90 0.63 0.70 0.30
Panicle compactness & Shape (PCS) 0.90 0.89 0.99 0.01
Awns 0.57 0.34 0.60 0.40
Glume colour (GLC) 0.85 0.81 0.95 0.05
Grain covering (GC) 0.36 0.23 0.64 0.36
Grain colour (GRC) 0.95 0.93 0.98 0.02
Endosperm texture (ET) 0.90 0.85 0.94 0.06
Average 0.71 0.55 0.72 0.28
H' = Diversity index for each character calculated from entire data set; Hel =
Average diversity index of each character for the 10 localities; Hel/H' = Proportion
of diversity within localities; and (H'-Hel)/H' = Proportion of diversity between
localities in relation to the total variation.
Chapter 3 Morpho-agronomical traits 61
Table 3.5 Estimates of the Shannon-Weaver diversity index, H', for 10
qualitative characters in sorghum accessions by location of collection.
Character
Location LMC:J: PC SJ PE PCS Awn GLC GC GCI ET Mean±S.E.
Cht 0.00 0.00 0.00 1.0 0.99 0.00 0.99 0.00 1.00 1.00 O.50±O.17
Hm 0.00 1.00 0.00 1.0 0.99 0.00 0.99 0.00 1.00 1.00 0.60±0.16
Kc 0.00 0.92 0.00 0.92 0.92 0.92 0.92 0.92 0.82 0.92 0.73±0.12
Mt 0.00 0.00 0.00 0.92 0.83 0.00 0.92 0.00 0.99 0.92 0.46±0.15
Dd 1.00 0.00 0.00 0.00 0.83 0.00 0.00 0.00 1.00 1.00 0.38±0.16
TI 0.00 0.47 0.00 0.86 0.86 0.00 0.85 0.47 0.76 0.86 0.51±0.12
Db 0.99 0.00 0.00 0.65 0.83 0.65 0.90 0.00 0.96 0.92 0.59±0.13
Cr 0.92 0.00 0.00 0.00 0.92 0.92 0.82 0.92 0.99 0.92 0.64±0.14
Hr 0.00 0.92 0.00 0.00 0.92 0.92 0.82 0.00 0.92 0.00 0.45±0.15
AU 0.68 0.47 0.85 0.95 0.83 0.00 0.85 0.00 0.83 0.91 0.64±0.11
:I: Character abbreviations as defined in Table 3.4
t Location abbreviations as defined in Figure 3.1.
3.3.2 Quantitative traits
3.3.2.1 Clustering
The UPGMA dendrogram (Figure 3.2) shows the clustering of sorghum
accessions based on quantitative data. First two major cluster groups were
formed at a genetic distance of about 1.90: the first group included only
three accessions (two from Chinhakssen and one Tulo) and the second
contained all the other accessions. The second major group was further
split into two subgroups at a genetic distance of 1.45. The first of this
subgroup was further split into two sub-subgroups, and the first comprised
Chapter 3 Morpho-agronomical traits 62
a single accession (#24), and the second consisted of all the rest. In total,
seven clusters were formed, where two of the clusters (cluster V and VI)
were made only of a single accession each. The constitution of each
cluster is given in Table 3.6. The second cluster contained about 45% of
the accessions, and a wide intra-cluster variation is observable.
Table 3.7 showed differences among clusters by summarising cluster
means for 16 characters. Cluster I was characterised by lowest values in
most of the variables except days to 50% flowering, leaf number, number of
primary branches, and threshing percentage. Cluster II comprises the
maximum number of accessions from various localities and it is
characterised by low to medium mean values in all variables. Longest leaf
sheath, tallest plant height, higher number of primary branches, highest
head weight, and highest grain yield characterised cluster Ill. The days to
50% flowering in cluster III is medium. Cluster IV includes four of the five
accessions known as Fandisha and was identified mainly by longest days
to 50% flowering, lowest threshing percentage, widest leaf area and
panicle. The grain yield was second to cluster II.
Cluster V consisted of only a single accession from Kurfa Challe aanaa,
and it has recorded the longest leaf length, largest leaf number, broadest
leaf, and heaviest 1000-seed weight. Cluster VI also consisted of a single
accession from Doba, and it has recorded the shortest number of days to
50% flowering, lowest 1000 seed weight, highest threshing percentage and
highest number of grains per panicle. The last cluster, cluster VII, is
characterised by lowest number of leaves, longest internode length,
longest panicle length, and fewest number of branches.
Accessions from the same source/ collection site were clustered together.
Nevertheless, some exceptions were observed. For example, out of the
three accessions from Habro, one (#34) was clustered in cluster II, while
two (#32 and 33) clustered in cluster VII. Two previous studies reported
using qualitative and quantitative data supports the absence of clear
grouping of accessions based on geographical origin (Ayana and Bekeie,
Chapter 3 Morpho-agronomical traits 63
1999; Teshome et a/., 1997). There was evidence that both natural
selection for adaptation to environment and farmers' selection for specific
use or specific cropping systems accounted for most of the morpho-
agronomic diversity (Grenier et a/., 2001).
L----c===== 3393--AHb____j L 32-HUb } VVIII
I-~ 30-Cr A_____
'----------------- 24-Db""'--- V
S
-1, __ -lI 23-Db
L__ - 7-Kc
17-TI
6-Kc
10-Mt
45-AU
I ---c==l_ ===== 4434--AAUU3376--AAUU III
l,_.---------- 43-5H-AmU
r---
L
- -_--
---{--=---r~==
--=-=========
22-TI
== 126--DTIdL L..f-----:r-------- 402-AU
L____fr------- 41-AU
- 11-Dd
26-Db
'-----i 38-A U
'--------- 18-TI
-~,.----c------ccccccc=31-Cr II'-- L 2174-DTIb
8-Mt
28-Db
21-TI
25-Db
9-Mt
L-----------c==L'"-==========~=======33-4H-Hmb21-3C-ThI }
,.-~~~~~r__~~~~~~-~~~~~~-~~~1-Ch
2.00 1.50 1.00 0.50 0.00
Dissim ilarity
Figure 3.2 Dendrogram showing cluster groups among the 45 sorghum
accessions based on 16 quantitative traits data.
Clustering from this study provides a structure for sampling accessions for
further germplasm collection; for genetic, breeding, or agronomic studies
where information on multiple quantitative traits is required. Cluster
analysis based on quantitative traits also may aid parental selection by
providing specific trait information that would allow the breeder to focus on
sampling of accessions from specific locations for cultivar improvement.
Chapter3 Morpho-agronomicatrlaits 64
Table 3.6 Distribution of the 45 sorghum accessions into seven clusters by
location of origin using average values of quantitative characters.
Location Cluster Location
II III IV V VI VII Total
Cht 2 2
Hm 1 1 2
Kc 2 3
Mt 2 1 3
Dd 2 2
TI 5 4 10
Db 4 1 1 6
Cr 1 1 3
Hb 2 3
AU 4 6 1 11
Total 3 20 8 8 1 4 45
t Location abbreviations as defined in Figure 3.1
3.3.2.2 Principal component analysis
The principal component analysis (PCA) grouped the 16 variables
(Appendix Ill) into 16 components, which accounted for the entire (100%)
variability evident among the test accessions. It also showed that the first
10 eigenvectors explained about 88% of the total variance. Of these, the
first eight (as only the ones with eigenvalues over 1 accounted for a
cumulative of 77.9% of the entire variability apparent among the
accessions were shown in Table 3.9. Each trait was an important source of
variation in at least one PC axis. Because each of the PC axes was given
equal weight in the cluster analysis, each trait contributed to the information
used to group accessions; however, some characters may have greater
importan.ce in determining plant phenotype than others.
Grain yield per panicle was significantly correlated (P ::;0.01) to eight of the
15 characters (Table 3.8). Internode length, threshing percentage, panicle
Chapter 3 Morpho-agronomical traits 65
width and panicle length didn't show significant correlation with grain yield
and the other components. The highest degree of correlation of grain yield,
head weight and grain number per panicle with other characters was
supported by the PC analysis where these three are the primary source of
variation with coefficient values of -0.34, -0.35 and -0.30, respectively
(Table 3.8). In this study, the first two PC axes accounted for 36% of the
multiple variations among genotypes (Table 3.9), indicating a high degree
of correlation among characters for these accessions.
The existence of wider morpho-agronomical diversity among sorghum
accessions studied was further substantiated by the PCA plot (Figure 3.3)
analysed based on 16 quantitative traits. The first principal component
alone explained 19% of the gross variability among the accessions. This
has been due chiefly to variations in grain yield per panicle, head weight,
grain number per panicle, and number of primary branches. Similarly, 17%
of the overall variability of the accessions comes from the second principal
component that originated primarily from variations in leaf characteristics
(leaf length, leaf area, leaf width and leaf number).
The PC analysis didn't completely place the sorghum accessions into
distinct groups. They remained scattered in all four quadrants, showing
greater variability among them. Overlap between accessions with similar
names was not seen. In some cases, the samples collected/obtained from
similar locations fall close to each other (for example, accessions number 1
and 2, 32 and 33). Like in clustering, accession # 24 occupied an extreme
of the first axis. Jain et al. (1975) indicated that variation either in terms of
individual genotypic frequencies or overall measures of genetic or
phenotypic diversity may not show obvious geographic patterns. Rather,
the variation was higher within the same location than between locations.
In a similar study, Ayana and Bekeie (1999) reported that the within regions
variation accounted for a large portion of the total variation compared with
the between regions variation. The trend of higher diversity within than
between regions also has been reported in barley germ plasm (Kebebew et
al., 2001).
Chapter3 morpho-agronomical variation 66
Table 3.7 Cluster means for 16 quantitative characters in 45 sorghum accessions.
-
Character
Cluster --
OFt LN LL LW LA 1NL LSL PHt PL PW NPBP HWt GYPP TSWt TP GNPP
Cl 92.67 9.57 59.3* 7.15* 316.73* 22.06* 16.77* 197.5* 11.2* 9.2* 69.6 88.4* 63.37* 27 71.83 2377.65*
CII 96.75 11.74 73.04 9.53 521.31 25.05 21.39 287.55 19.45 10.32 88.75 151.13 107.14 27.05 71.07 3997.02
CIII 100.75 13.1 71.93 10.24 555.15 26.56 22.41 ** 320.38** 14.71 10.6 124.14** 178.46** 131.59*' 29.71 73.67 4435.6
CIV 106** 12.95 81.16 11.67** 707.43 26.09 21.83 282.47 18.59 11.05** 78.94 165.51 112.22 23.75 68.09* 4739.56
Cv 106 13.3** 89.2** 10.8 719.6** 24 19 251.3 13.6 10.6 116.3 123.66 87.56 31.7** 70.81 2762.15
CVI 89* 10.3 66.4 9.85 488.6 22.67 19.2 230 17.5 10.2 69.95 131.63 102.04 18* 77.52** 5668.89**
CVII 95 9.44* 72.93 8.80 478.58 27.23** 21.7 280.25 21.28** 10.08 67.21* 91.27 65.14 21.7 71.5 3025.14
-
t Character abbreviations as defined in Table 3.4.
*,** Lowest and highest values, respectively.
Chapter 3 morpho-agronom ical variation 67
Table 3.8 Correlation coefficients (n = 45) between quantitative traits and
grain yield per panicle, head weight and grain number per panicle in
sorghum.
HWt GNPP
HWt 0.94**
GNPP 0.74** 0.73**
OF 0.25 0.29 0.22
PHt 0.49** 0.43** 0.35*
LN 0.56** 0.62** 0.46**
LL 0.15 0.26 0.25
LW 0.48** 0.56** 0.57**
LA 0.34* 0.44~* 0.44**
1NL 0.18 0.15 0.21
LSL 0.46** 0.44** 0.44**
NPBP 0.60** 0.58** 0.21
TSWt 0.43** 0.37** -0.27
TP 0.17 -0.16 0.08
PW 0.10 0.14 0.23
PL -0.13 0.00 0.10
*, ** r values significant at p = 0.05 and p = 0.01 probability level,
respectively. t Character abbreviations as defined in Table 3.3.
Chapter 3 morpho-agronomical variation 68
Table 3.9 Principal component (PC) analysis of 16 quantitative traits in 45
sorghum accessions showing eigenvectors, eigenvalues and proportion of
variations explained with the first eight PC axes.
Character PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
DFt -0.20 0.09 -0.12 0.19 0.17 0.68 0.34 0.21
LN -0.33 -0.04 -0.21 0.03 0.04 0.14 -0.22 -0.35
LL -0.24 0.32 -0.25 0.29 -0.12 -0.23 -0.22 0.05
LW -0.35 0.23 -0.13 0.03 -0.08 0.06 -0.05 0.24
LA -0.32 0.30 -0.23 0.17 -0.11 -0.05 -0.15 0.16
1NL -0.17 0.03 0.48 0.41 0.15 0.12 0.19 -0.27
LSL -0.27 -0.00 0.43 0.11 0.13 -0.05 0.12 0.03
PHt -0.26 -0.16 0.28 0.30 -0.01 -0.32 -0.11 -0.04
PL -0.04 0.39 0.25 -0.24 0.25 -0.38 0.18 0.44
PW -0.11 0.14 0.26 -0.40 0.39 0.34 -0.59 0.07
NPBP -0.23 -0.36 -0.08 0.01 0.27 -0.17 -0.32 -0.10
HWt -0.35 -0.18 -0.09 -0.32 0.02 -0.10 0.27 -0.07
GYPP -0.34 -0.29 -0.00 -0.25 -0.14 -0.03 0.18 0.12
TSWt -0.06 -0.44 -0.28 0.11 0.39 -0.08 0.12 0.38
TP 0.05 -0.33 0.27 0.16 -0.49 0.19 -0.31 0.53
GNPP -0.30 0.02 0.15 -0.38 -0.45 0.07 0.11 -0.15
Eigenvalues 2.97 2.74 1.16 1.07 1.34 1.09 1.06 1.04
Individual% 18.55 17.10 7.24 6.71 8.39 6.82 6.60 6.49
Cumulative% 18.55 35.64 42.88 49.59 57.98 64.80 71.40 77.89
t Character abbreviations as defined in Table 3.3.
Chapter 3 morpho-agronom ical variation 69
3.00-
-
• 7
2.00
• 4
• 5 <> 18
• 17
.......... <> 8 • 15 • 6
oo 1.00 <> 26
<> 20
.r.-..-.-. <> 23<> 29
• 40
N <> 38e ~1 <> 41 e 27
U 0.00 <> 30 43 <> 35 ~ I~
<> 39
o,
<> 11 <> 22
<> 44 • 3. 9 <> 6328• 30 <> 21
<> 37 <> 32
-1.00 <> 45
<> 24
<> 1 <> 12
<> 42
<> 2
.34
<> 10 <> 16 <> 25
<> 13
-2.00+-'---'--'--'--'-'---'--'---.-r--o--,----,--.--,-,-,--,--,--,.---,-,---.--,---,
-2.00 -1.00 0.00 1.00 2.00 3.00
PC1 (19.00)
Figure 3.3 Principal component plot of the 45 sorghum accessions,
estimated using 16 quantitative traits data. (Each accession is designated
by number given in Table 3.1).
In general, the name given by the farmers to landraces does reflect
morpho-agronomically different sorghum landraces. For example, the two
Zangadas (#32 and 33) from Habro were clustered in cluster VII, while
Dassile (#34) from the same collection site was grouped in cluster II. On
the other hand, between identically named landraces grown in the same
locality or different localities, a greater level of dissimilarity was observed.
Accessions with the local name Muyra (#4, 5, 10, 13 and 45) were grouped
into three distinct cluster groups (Figure 3.2).
The implications of these results for genetic resource collection and
maintenance are immense. Firstly, as expected, geographical separation is
likely to be associated with overall genetic differences. However, as
confirmed from these results large differences may also be found within
quite small regions. Secondly, although seven well-differentiated clusters
were obtained from the 16 quantitative traits based data appreciable intra-
Chapter 3 morpho-agronom ical variation 70
cluster diversity was found. Phenotypic diversity estimates, aided by
molecular marker analysis may help to establish the amount of genetic
diversity still available for conservation and for further sorghum crop
improvement.
3.4 Conclusions
Among the 10 qualitative traits measured in the 45 sorghum accessions,
highest diversity index (H') was obtained for grain colour, inflorescence
exsertion, panicle compactness and shape, endosperm texture, and glume
colour. On the other hand, the traits that showed the lowest average
diversity index for the 10 localities were stalk juiciness, grain covering,
awns and leaf midrib colour (Table 3.4). Furthermore, it was found that the
proportion of total diversity obtained within collection sites were larger than
between the collection sites. Pooled over characters within localities, the
mean of H' ranged from 0.38 for Deder to 0.73 for Kurfa Challe. Panicle
compactness and shape, and glume colour showed high diversity index in
all the localities (Table 3.5).
Clustering of the accessions based on dissimilarity of quantitative traits
produced seven groups. In most cases, accessions from one collection site
appeared in more than one cluster, indicating the existence of phenotypic
variation within the locations. The exceptions are accessions from
Chinhakssen and Deder, which fell in cluster I and II, respectively (Table
3.6). The existence of wider agro-morphological diversity among the
sorghums accessions implies the potential to improve the crop and the
need to conserve these resources.
Chapter 3 morpho-agronomical variation 71
REFERENCES
Assefa, K., Ketema, 5., Tefera, H., Nguyen, H.T., Blum, A., Ayele, M.,
Bai, G., Simane, B. and Kefyalew, T. 1999. Diversity among
germplasm lines of the Ethiopian cereal tef [Eragrostis tef (Zucc.)
Trotter]. Euphytica 106: 87-97.
Ayana, A. and Bekeie, E. 1998. Geographical patterns of morphological
variation in sorghum (Sorghum bicolor (L)Moench) germ plasm from
Ethiopia and Eritrea: qualitative characters. Hereditas 129: 195-205.
Ayana, A. and Bekeie, E. 1999. Multivariate analysis of morphological
variation in sorghum [Sorghum bicolour (L.) Moench] germplasm
from Ethiopia and Eritrea. Genetic Resources and Crop Evolution
46: 273-284.
Bechere, E., Belay, G., Mitiku, D., and Merker, A. 1996. Phenotypic
diversity of tetraploid wheat land races from north-central regions of
Ethiopia. Hereditas 124:165-172.
Bekeie, E. 1984. Analysis of regional patterns of phenotypic diversity in the
Ethiopian tetraploid and hexaploid wheats. Hereditas 100:131-154.
Brooke, C. 1958. The durra complex in the central Highlands of Ethiopia.
Econ. Bot. 2: 192-204.
Demissie, A. and Bj0rnstad, A. 1996. Phenotypic diversity of Ethiopian
barley in relation to geographical regions, altitudinal range, and agro-
ecological zones: as an aid to germplasm collection and
conservation strategy. Hereditas 124: 17-29.
Doggett, H. 1988. Sorghum (Second edition). Longman, UK.
Geleta, N. 1997. Variability and association of morpho-agronomic
characters with reference to highland sorghum [Sorghum. bicolour
(L.) Moeneh] landraces of Hararghe, eastern Ethiopia. M.Sc. Thesis,
Alemaya University of Agriculture, Ethiopia.
Chapter 3 morpho-agronomical variation 72
Grenier, C., Bramel-Cox, P.J. and Hamon, P. 2001. Core collection of
sorghum: I. Stratification based on eco-geographical data. Crop Sci.
41 :234-240.
Harlan, J.R. 1969. Ethiopia: A center of diversity. Economic Botany
23:309-314.
IBPGRlICRISAT, 1993. Descriptors for Sorghum [Sorghum bicolour (L)
Moeneh]. International Board of Plant Genetic Resources. Rome,
Italy/ International Crop Research Institute for Semi-Arid Tropics,
Patancheru, India.
Jain, S.K., Qualset, C.O., Bhatt, G.M. and Wu, K.K. 1975. Geographical
patterns of phenotypic diversity in a world collection of durum wheat.
Crop Sci. 15:700-704.
Kebebew, F., Tsehaye, Y. and McNeilly, T. 2001. Morphological and
farmers cognitive diversity of barley (Hordeum vu/gare L. [Poaceae])
at Bale and North Shewa of Ethiopia. Genetic Resources and Crop
Evolution 48:467-481.
Kebede, Y. 1991. The role of Ethiopian sorghum germplasm resources in
the national breeding programme. In: Engels, J.M., Hawakes, J.G.
and Worede, M. (Eds.). Plant Genetic Resources of Ethiopia,
Cambridge University Press. Cambridge, pp.315-322.
MarshalI, D.R. and Brown, A.H.D. 1975. Optimum sampling strategies in
genetic conservation. In: Frankel, O.H. and Hawkes, J.G. (Eds.).
Crop genetic resources for today and tomorrow. Cambridge Univ.
Press, London, pp 53-80.
Munsell Colour Firm. 1990. The Munsell book of colour: Matte collection.
New Windsor, New York.
NCSS, 2000. Number Cruncher Statistical Systems, Dr. Jerry L. Hintze,
329 North 1000 East, Kaysville, Utah 84037, Canada.
Negassa, M. 1985. Patterns of phenotypic diversity in an Ethiopian barley
collection and the Arsi-Bale highland as a centre of origin of barley.
Hereditas 102:139-150.
Chapter 3 morpho-agronomical variation 73
Negassa, M. 1986. Estimates of phenotypic diversity and breeding
potential of Ethiopian wheats. Hereditas 104:41-48.
Perry, M.C. and Mclntosh, M.S. 1991. Geographical patterns of variation
in the USDA soybean germplasm collection: I. Morphological traits.
Crop Sci. 31:1350-1355.
Stemier, A.B.L., Harlan, J.R. and de Wet, J.M.J. 1977. The sorghums of
Ethiopia. Economic Botany 31 :446-460.
Stickler, F.C., Weaden, S., and Pauli, A.W. 1961. Leaf area determination
in grain sorghum. Agron. J. 53:187-188.
Tesfaye T., Getachew, B. and Worede, M. 1991. Morphological diversity
in tetraploid wheat landrace populations from central highlands of
Ethiopia. Hereditas 114: 171-176.
Teshome, A. 2001. Long-term sustainability of Ethiopian landraces at risk.
In: Eberlee, J. (Ed.) International Development Research Centre
Reports Online. http://www.idrc.ca/reports
Teshome, A., Baum, B.R., Fahrig, L., Torrance, J.K., Arnason, T.J. and
Lambert, J.D. 1997. Sorghum [Sorghum bicolour (L.) Moeneh]
landrace variation and classification in North Shewa and South Welo,
Ethiopia. Euphytica 97: 255-263.
Tolbert, D.M., Qualset, C.O., Jain, S.K., and Craddock,J.C. 1979. A
diversity analysis of a world collection of barley. Crop Sci. 19:789-
794.
Vavilov, N.1. 1951. The origin, variation, immunity and breeding of
cultivated plants. Chronica Botanica 13:1-366.
Worede, M. 1988. Diversity and genetic resource base. Eth. J. Agric. Sci.
10:39-52.
74
CHAPTER 4
ANALYSIS OF GENETIC DIVERSITY BASED ON DNA MARKERS IN
SORGHUM
Abstract
Sorghum [Sorghum bicolor (L.) Moeneh] is one of the most important cereal
crops providing food in Ethiopia, which is believed to be its centre of origin
and diversity. Genetic diversity among landrace sorghum accessions from
10 localities in the eastern highlands of Ethiopia and breeding cultivars
obtained from Alemaya University, Ethiopia, was assessed using amplified
fragment length polymorphism (AFLP) and microsatellite (SSR) markers.
The extent of genetic diversity among the different sorghum accessions
was as high as 85% based on AFLPs and 90% using microsatellite data.
The accessions studied separated into five clusters for both AFLP and
I microsatellite data. AFLPs provided more detail discrimination than
microsatellite data. Both cluster and PCA analyses failed to distinctly group
accessions from the same locality or presumably the same name. Despite
the relatively low discrimination level observed for SSR marker data in this
study, both marker techniques proved to be useful for characterising and
identifying genetic diversity in sorghum.
4.1 Introduction
Sorghum is one of the world's most important crop plants, ranking fifth in
acreage among cereals (Doggett, 1988) and first in the eastern regions of
Ethiopia (CSA, 2000). Northeastern Africa, which includes Ethiopia, is
believed to be the centre of origin and domestication for sorghum (Vavilov,
1951; Doggett, 1988; Stemier et a/., 1977). The sorghum grown by smalI-
scale farmers in eastern Ethiopia is observably diverse for characters
including plant height, panicle types and seed colour. This is attributed to
adaptation to a wide range of geographical and ecological niches and
climatic regimes. On the basis of survey observations and current
Chapter 4 DNA markers 75
germplasm collection, accessions Muyra, Fandisha, Wagare, and Oassile
are the most frequently represented in the AU/SIP collection. However, it is
impossible to determine whether a specific accession represents a single
genotype due to continued adaptation and selection. In order to establish a
sound basis and strategy for germplasm utilisation and maintenance,
sorghum accessions need to be characterised genetically.
Molecular markers are considered to be an efficient method for estimating
genetic diversity due to the abundance of markers that are not affected by
environmental or epistatic interactions that may affect morphological traits
(Schut et aI., 1997; Gepts, 1993). Techniques to generate markers based
on the polymerase chain reaction provide new opportunities for
characterising and describing germplasm and assessing genetic diversity
within and between different crop varieties. These techniques include
RAPD (Welsh and McClelland, 1990), AFLP (Zabeau and Voss, 1993) and
SSR (Tautz, 1989). Previous reports to assess genetic variation in sorghum
are based on RFLPs (Ahnert et al., 1996), RAPDs (Ayana et al., 2000;
Menkir et aI., 1997) and SSRs (Ghebru et aI., 2002; Smith et aI., 2000; Djé
et aI., 1999; Brown et aI., 1996) and have shown that these techniques are
suitable for applications relevant to conservation and utilisation of sorghum
germplasm.
The potential of semi-automated, robust, cost-effective molecular genetic
markers, specifically AFLPs, allows the evaluation of quality in germplasm
collections for enhanced breeding (Mitchell et aI., 1997; Moreli et aI., 1995).
A previous study based on qualitative and quantitative characters has
identified significant variability among sorghum accessions taken from the
same region as the present study (Geleta, 1997). However, there is no
molecular data for comparison of these accessions. The objectives of the
study were (1) to assess the extent and pattern of genetic diversity among
sorghum accessions from the selected region using AFLP and SSR
markers, (2) examine the distribution patterns of genetic diversity in
different localities, and (3) verify the applicability of AFLP and SSR markers
in accession identification.
Chapter 4 DNA markers 76
4.2 Materials and methods
4.2.1 Plant material
A total of 45 accessions (Table 3.1) were used in this study. Three to four
plants were grown in 8 L size pots, containing soil (Bainsvlei soil type),
under standard glasshouse conditions at the University of the Free State,
Bloemfontein, South Africa, during March through August 2001. The growth
temperature was set at 14±2°C night and 28±2°C day.
4.2.2 DNA extraction
Leaf material was taken from 10, four-to six-week-old, plants of each
accession. Single-plant samples were ground to a powder in liquid nitrogen
using a. mortar and pestle. A modified monocot extraction procedure
(Edwards et al., 1991) was followed to isolate the DNA. Extraction buffer
(10 ml) (1M Tris-HCI pH 8: 0.25M EDTA, and 1.25% (w/v) SDS) and 1 ml
(10% w/v) Cetyl triethyl ammonium bromide (CTAB) was added. The
homogenate was vortexed and incubated at 65°C for 60 min, with periodic
shaking. Chloroform extraction was performed to remove cellular debris
and proteins by the addition of 10 ml chloroform-isoamyl alcohol (24: 1v/v)
followed by centrifugation for 15 min at 10 krpm. Thereafter, the DNA was
precipitated by the addition of two volumes of cold absolute ethanol. The
precipitate was spooled using a sterile Pasteur pipette and washed twice in
70% ethanol. The DNA was dissolved in 250 )lI sterile distilled water and
stored at -20°C.
4.2.3 DNA concentration determination
The concentration and protein content of individual DNA sample was
determined spectro-photometrically (U-2000), by taking readings at 260 nm
and 280 nm. The DNA concentration was calculated using the formula,
[DNA] = Optical density (OD260) x dilution x constant (50 uq/rnl).
Chapter 4 DNA markers 77
The DNA was diluted to a working concentration of 100 ng/J-l1in sterile
distilled water. Equal quantities (100 ng) of genomic DNA from 10 plants for
each accession were bulked and used in AFLP and SSR analyses.
4.2.4 AFLPs
4.2.4.1 Restriction endonuclease digestion and ligation of adaptors
Genomic DNA (250 ng of the bulked DNA) was double digested with 5
units each of EcoRI and Msel endonuclease, at 37°C for 2 hr. The digested
DNA fragments were ligated to EcoRI and Msel adaptors (Table 4.1) with
T4 DNA ligase for 2 h at 20 ± 2°C. The ligated DNA was diluted 1:10 In TE
buffer (10 mM Tris-HCI (pH 8.0), 0.1 mM EDTA) and stored at -20°C.
4.2.4.2 PCR amplification reactions
PCR was performed in two consecutive reactions: a pre-selective and
selective PCR, following the protocol supplied by the manufacturer (GIBCO
BRL). In the pre-selective reaction, genomic DNA was amplified using an
AFLP primer pair, each having one selective nucleotide (Table 4.1).
Accordingly, a 5 J-l1diluted ligation product, 40 J-l1pre-amplification primer
mix, 5 J-l110X PCR buffer mixed with MgCI2 for AFLP and 1 unit I 0.2 J-l1of
Taq polymerase were mixed for the pre-selective reaction. The pre-
selective reactions were performed as follows: 20 cycles of 30 sec at 94°C,
60 sec at 56°C and 2 min at 72°C. Pre-selective PCR amplification was
confirmed by gel electrophoresis and the amplified product diluted to 1:50
in TE buffer (10 mM Tris-HCI [pH 8.0] and 0.1 mM EDTA) and used as
template for the selective amplification using AFLP primers, each
containing three selective nucleotides.
Selective PCR amplification was performed in 20 J-l1reactions consisting of,
5 J-l1pre-selective template DNA (1:50 dilution), 4.5 J-l1(6.7 ng/J-ll, dNTPs)
Mse primer with selected nucleotide extensions, 1 J-l1(1 J-lM) Eco primer
with selected nucleotide extensions (Eco-ACA and Eco-AAC labelled with
Chapter 4 DNA markers 78
FAM and NED, respectively (PE Biosystems), 2 ul 10x PCR buffer (200
mM Tris-HCI [pH 8.4], 15 mM MgCI2, 500 mM KCI) and 1 0.1 ).lI Taq
polymerase (5 units / ut). Selective PCR amplification reactions were
performed for 35 cycles, with 30 sec at 94°C, and 30 sec at 65°C, followed
by 2 min at 72°C. The annealing temperature was lowered 0.7°C in each
subsequent cycle during the first 12 cycles down to 56°C. All amplification
reactions were performed in a PCR System 2700 (Applied Biosystems).
Table 4.1 Adaptors and primers used for pre-selective and selective AFLP
amplification reactions.
Primer / adaptor code Sequence
1. Adaptor:
EcoRt adaptor 5'-CTCGT AGACTGCGT ACC-3'
3'-CATCTGACGCATGGTTAA-5'
Mset adaptor 5'-GACGATGAGTCCTGAG-3'
3'-TACTCAGGACTCAT-5'
2. Primer:
EcaRt primer
E-AAC 5'-GATCTGCCTACCAATTCAAC-3' (NED)
E-ACA 5'-GATCTGCGTACCAATTCACA-3' (FAM)
Mset primer
M-CAA 5'-GATGAGTCCTGAGTAACAA-3'
M-CAT 5'-GATGAGTCCTGAGTAACAT -3'
M-CTA 5'-GATGAGTCCTGAGTAACTA-3'
M-CAG 5'-GATGAGTCCTGAGTAACAG-3'
Following selective amplification, 5 ).lI of amplification product was mixed
with 24 ul formamide (deionised) and 1 ).lI GeneScan™ 500 ROX™ size
standard marker (PE Biosystems), denatured at 94°C for 10 min and quick
cooled in ice slurry and resolved according to size on a Perkin-Elmer
ABI310 Automated Capillary Sequencer (PE Biosystems).
Chapter 4 DNA markers 79
4.2.4.3 AFLP analysis
AFLP analysis was performed using GeneScan® software. Only clear and
unambiguous bands were included in the analyses. AFLP fragments larger
than or equal to 60 bp with a peak height above or equal to 45 RFUs were
scored. A visual comparison was used to correlate the binary output of
electropherograms.
4.2.4.4 Statistical analysis
Each AFLP fragment was treated as a unit character and all the accessions
were scored for presence or absence of AFLP fragments against the eight
primer combinations used. Data was entered into a binary matrix as
discrete variables ("1" for presence and "0" for absence of a 'homologous'
fragment). The number of polymorphic and monomorphic fragments was
determined from the amplified fragments for each primer pair (Appendix I).
Monomorphic loci were excluded from further data analyses. The bivariate
1-0 data were used to estimate genetic distance for all possible pair-wise
comparisons between accessions using the Euclidean distance method.
The genetic distance matrix was used for cluster analysis using the
Unweighted Pair Group Method with arithmetic Averages (UPGMA)
algorithm. Principal component analysis (peA) was performed to visualise
the dispersion of individual accessions in relation to the first two principal
axes of variation. All statistical analyses were done using the NeSS
statistical package (NeSS, 2000).
4.2.5 Microsatellites (SSRs)
4.2.5.1 SSR primers
Fifteen SSR sorghum primer pairs (Brown et al., 1996) were used in this
study. Primers were excluded from the study if banding patterns were too
complex to score accurately from agarose gels and/or if the primers failed
to amplify consistently in ali 45 accessions. The SSR markers were
Chapter 4 DNA markers 80
selected across all the sorghum linkage groups (A to I). Ten SSR primer
pairs were used in the final analysis (Table 4.2).
4.2.5.2 SSR amplification
A standard PCR method was used to amplify microsatellites. PCR
conditions were optimised for each primer pair by adapting the annealing
temperature (Tm). The PCR reaction was performed by taking 0.5 III of
bulked DNA, 2.5 III 10x PCR buffer (200 mM Tris - HCI (pH 8.4), 500 mM
KCI), 0.75 III MgCI2 (50 mM), 0.5 III dNTP's (40 mM), 0.2 III Taq
polymerase (5 units / Ill) and 18.25 III sterile distilled water in a total
reaction volume of 25 Ill. PCR cycling conditions were: 2 min initial
denaturation at 95°C; followed by 30 cycles of 30 s at 94°C, 45 s at either
45°C (Sb4-32, sb5-85 and sb6-84), 50°C (sb4-22) or 55°C (sb1-10, sb4-15,
sb5-236, sb6-36, sb6-57 and sb6-342) and 1 min elongation at 720C,
followed by a final elongation of 10 min at 72°C. All PCR reactions were
performed on a PCR System 2700 (Applied Biosystems).
4.2.5.3 SSR locus visualization / gel analysis
The result of the PCR amplification was analyzed by electrophoresis on a
2% agarose gel (Molecular Screening agarose Roche) for resolution of
fragments ranging from 50 to 1000 bp in TAE buffer (40 mM Tris-acetate, 1
mM EDTA, pH 8.0) run at 80V for 2.5 hours. Amplified fragments were
visualised and sized using the Gel Doc 1000™ image analysis system
(Biorad) after ethidium bromide (0.5 Ilg/ml) staining.
4.2.5.4 Data collection and statistical analyses
The presence or absence of each fragment was coded as 1 or 0,
respectively, and scored in a binary data matrix. The polymorphism
. information content (PlC) was determined according to the formula
described in Smith et al. (2000):
Chapter 4 DNA markers 81
n
PlC = 1 - llfj)2
i=1
where fj is the frequency of the ith allele carried by the population,
calculated for each SSR locus. PlC values range from 0 (monomorphic) to
1 (highly discriminative). The PlC values provide an estimate of the
discriminatory power of a marker by taking into account not only the
number of alleles at a locus, but also the relative frequencies of those
alleles in the population under study.
Table 4.2 Summary of the SSR-primer pairs used in this study.
SSR Primer Repeat Linkage Size range
Locus sequence motif group (bp) 1
Sb1-10 F:GTGCCGCTTTGCTCGCA (AGb 0 242-488
R:TGCTATGTIGTTTGCTICTCCCTICTC
Sb4-15 F:GCTGCT AAGCCGTGCTGA (AG)16 E 120-134
R:TI ATTTGGGTGAAGTAGAGGTGAACA
Sb4-22 F:TGAGCCGAAAACCGTGAG (ACGAC)4 (AG)6 NA 270-300
R:CCCAAAACCAAGAGGGAAGG
Sb4-32 F:CTCGGCGGTI AGCACAGTCAC (AG),s E 160-216
R:GCCCATAGACAGACAGCAAAGCC
Sb5-85 F:AGACGCnnCTCTCTCTCTCTCTCTCTCT (AG)12 NA 200-225
R:TAGCCCTGCCGCATACTGAA TG
Sb5-236 F:GCCAAGAGAAACACAAACAA (AG)2o G 162-222
R:AGCAATGTATTTAGGCAACACA
Sb6-36 F:GCAT AA TGACGGCGTGCTC (AG),g C 155-199
R:CTICCAAGTGAAAGAAACCATCA
Sb6-57 F:ACAGGGCTTT AGGGAAA TCG (AG),s C 283-313
R:CCATCACCGTCGGCATCT
Sb6-84 F:CGCTCTCGGGATGAATGA (AG),4 F 170-212
R:TAACGGACCACT AACAAA TGA TI
Sb6-342 F:TGCTIGTGAGAGTGCCTCCCT (AC)2s A 250-320
R:GTGAACCTGCTGCTTT AGTCGATG
1 Data from Brown et al., 1996; Dean et al., 1999; Ghebru et al., 2002.
NA = Not available
Genetic distances between pairs of accessions for SSR data were
calculated from comparisons of the band scores using the Euclidean
distance method in the NCSS program. The 45 accessions were clustered
Chapter 4 DNA markers 82
based on genetic distance, using the UPGMA clustering method, and
relationships between accessions visualised as dendrograms. Principal
component analysis (PCA) based on correlation matrix of the allelic
frequencies was performed with NCSS statistical package (NCSS, 2000).
4.3 Results and discussion
4.3.1 AFLP markers
4.3.1.1 Level of AFLP polymorphism
AFLP analysis of bulked DNA of 45 sorghum accessions from the eastern
highlands of Ethiopia using eight primer combinations identified a total of
651 fragments of which 85% were polymorphic (Table 4.3). A typical AFLP
electropherogram of two accessions illustrating band sizes and peak
heights is shown in Figure 4.1. The number of scorable fragments detected
by an individual primer combination ranged from 55 (for primer pair E-
AAC/M-CAT) to 114 (for primer pair E-ACAlM-CAA). The number of
polymorphic fragments for each primer pair varied from 45 (for primer pair
E-AAC/M-CT A) to 102 (for primer pair E-ACAlM-CAA) with an average of
69 per primer pair (Table 4.3).
Based on AFLP data percentage polymorphism identified per primer pair
ranged from 69% (E-AAC/M-CTA) to 94% (E-ACAlM-CAT). The
polymorph isms generated by any single primer combination were able to
uniquely distinguish all accessions. The two accessions (#25 and #26)
most closely related based on AFLP data differed by 2 AFLP fragments.
Both accessions were from Doba but are known by different names. In 49
accessions of tef, four or more primer pair combinations were required to
separate each accession distinctly (Bai et al., 1999)
The frequency distribution of the 990 pair-wise genetic distance (GD)
estimate values generated among 45 sorghum accessions for the
combined AFLP data is presented in Figure 4.2. The average GD among
Chapter 4 DNA markers 83
all the accessions was 0.62 with values ranging from 0.41 (accession #1 vs
#5) to 0.75 (accession #5 vs #36). Most (60%) of the GD values ranged
from 0.61 to 0.70, indicating the absence of a close genetic relationship
between those pairs of accessions. Accession pairs #1 and #2, #44 and
#45, and #1and #3 were more closely related to each other with GD
ranging from 0.41 to 0.44.
Table 4.3 The number of AFLP fragments and degree of polymorphism
determined for 45 sorghum accessions using eight AFLP primer
combinations.
Primer combinations Total Polymorphic Polymorphism
bands bands rate (%)
E-AAC/M-CAA 90 84 93
E-ACAlM-CAA 114 102 90
E-AAC/M-CAT 55 49 89
E-ACA/M-CAT 79 74 94
E-AAC/M-CTA 65 45 69
E-ACAlM-CT A 96 75 78
E-AAC/M-CAG 68 57 84
E-ACAlM-CAG 84 66 79
Total 651 552
Average 81 69 85
Cluster analysis using the UPGMA method grouped the 45 accessions into
two major clusters (Figure 4.3). At a genetic distance of 0.62 the first major
cluster was split into two sub-clusters and the second into three sub-
clusters. The cophenetic correlation coefficient between the dissimilarity
and the original matrix for data generated from eight primer combinations
was high (0.82), indicating a good fit of the cluster analysis performed. In
most cases, the accessions were clustered in general agreement with their
site of collection and geographic distribution. For example, seven of the 11
accessions from AU were clustered in the second major cluster, while four
of them grouped in the first major cluster.
Chapter 4 DNA markers 84
ABI _31 AFLP
PRISM
o 50 100 150 200 2S0 sso 4,0
250
200
ISO
100
lA I II 1~ h
"~ VW'Y \.oW lJJ...J ll., w I...,.. '-'- ~ JL ft J\._
t ...r.-l. .-_ '" '1 I ,....I I I I Ir I'
2SO
ZOO
150 ~ 1
100
;Q \ t I I ~A, ~,~ A A ~
"\i vW JJI W ~I WUUUY VI LA-rlWI,..... IMJV~ WWV\.. 'l.,
Figure 4.1 Genescan electropherograms showing part of the AFLP runs of
bulked DNA of two accessions (ETS 721 and ETS 2752) using EcoRl1 ACA
and Msell CTA primers in the present study. Arrows indicate some of the peaks
that are polymorphic between the two accessions.
Cl)
C
0
Cl)
'C
ns 300.
Q.
E
0
0
Q)
Cl)
.~ 200 .
-lijQ..0...
Q) 100.
..0
E
:::J
Z
Genetic distance coefficient
Figure 4.2 Frequency distribution of pair-wise genetic distance
coefficients obtained for 45 sorghum accessions using AFLP data.
Chapter 4 DNA markers 85
Similarly, eight of the 10 accessions from Tulo were grouped together in
cluster II, whereas two accessions (accessions #15 and 20) were grouped
in cluster V together with accession #12 from Deder.
It is interesting to note that clustering based on similarity of local or cultivar
name was not common. As an example, accessions with the name Wagare
(# 1, 2, 3, 8 and 16) were clustered in three different clusters, where # 1, 2,
and 3 grouped in cluster I, and #8 and #16 were grouped in clusters III and
II, respectively. Similarly, the five accessions with the local name Fandisha
(# 6, 7, 17, 23 and 24) were grouped in the first major cluster, but in two
clusters, #6 and #7 in cluster I, whereas #17, 23 and 24 grouped in cluster
II. The accessions named Muyra (# 4, 5, 10, and 13) grouped within the
first major cluster, but subsequently accession #4 and 5 were included in
cluster I, while #10 & 13 were grouped in cluster II. The two accessions
collected from Habro with the local name Zangada (#32 & 33), also first
grouped under the first major cluster, and then split into two different sub-
clusters.
The results this study confirms that AFLP markers are applicable for DNA
fingerprinting sorghum accessions to determine their genetic relationships
and for genotype identification studies. Furthermore, the high level of
genetic diversity identified among the different sorghum accessions
indicates the potential for sorghum improvement. In previous studies,
sorghum accessions from wider regions of Ethiopia and Eritrea have been
analysed using RAPDs and identified only intermediate levels of genetic
variation (Ayana et al., 2000). In this study, a different approach was taken
by analysing accessions from a smaller region, including the eastern
highlands of Ethiopia using the AFLP marker technique. The findings
clearly demonstrate the usefulness and efficiency of AFLPs in analysing
genetic diversity and discriminating the accessions studied as well as the
importance of studying the genetic diversity within smaller regions.
Chapter 4 DNA markers 86
---jl- 20-11 }15-11 V
12-Od
444-AU4~OA-AUU1
43-AU IV
36-AU
38-AU
~ 39-AU III
8-Mt
18-11
10-Mt
24-Db
r- ~ 23-Db17-11
-j 42-AU
34-Hb
37-AU
35-AU
32-Hb
~ 30-Cr
'- 28-Db
29-Cr II
'-- r--r-t 27-Db26-Db
25-Db
44 22-1121-1116-1114-11
13-11
19-11
~ 31-Cr
9-Mt
41-AU
33-Hb
11-Od
7-Kc
6-Kc
4-Hm
2-Ch
3-Hm
~ 5-Kc
1-Ch
0.80 0.60 0.40 0.20 0.00
Dissimilarity
Figure 4.3 Dendrogram constructed based on AFLP data, showing genetic
distance and cluster groups among 45 sorghum accessions. Each horizontal
line represents a separate sorghum accession, and the number-letter after each
line indicates sample number and the locality from which the accession was
collected/ obtained (refer Figure 3.1).
Chapter 4 DNA markers 87
4.3.1.3 Principal component analysis (PCA)
Principal component analysis (PCA) is a useful way to determine the effect
of different variables on the data set. The PCA plots analysed using a
combined AFLP data set are shown in Figure 4.4. The first and second
components (PC1 and PC2) explained 45.62% of the variance. The first
axis (PC1) accounted for 23.24% of the variation and was influenced by
primer combination E-ACNM-CAT and E-AAC/M-CAT (with factor loading
0.81 and 0.78, respectively). The second axis (PC2) explained 22.38% of
the variation and was positively influenced by primer combination E-AAC/
M-CAG (loading 0.88) and E-ACN M-CAG (loading 0.81). The analysis
confirmed the wider genetic diversity among the accessions; as they were
randomly distributed all over the four quadrants. The AU accessions, for
example, had a wider distribution of genetic variation, as seen from the first
two axes.
3.00-
-
-
037
1.50 028 017014 035
021 011
042 020
o ~7 016
oC\J o 230 2 0 3i ~ to 0 130.001------r;"""""----t-~".,'I__. P __ ~!a_----0.. u l!:> 39 0 ~9"' vv
024 045 0035
040 08 018 031 Q 4
043 06438 019 041
-1.50 036 0 07
06
010
-3.00,+--.---.---.--.--.---.---.--.---.----lr--.----.----.----.----.----.----.----.---,-
-3.00 -1.50 0.00 1.50 3.00
PC1
Figure 4.4 Plot of the 45 sorghum accessions against the first two principal
components (PC1 and PC2) based on AFLP data. (Individual accessions are
shown by numbers as described in Table 3.1).
Chapter 4 DNA markers 88
4.3.2 Microsatellite markers
4.3.2.1 Polymorphism of microsatellites in sorghum accessions
The 45 sorghum accessions were analysed using 10 microsatellite primer
pairs, which amplified bands distinguishable on agarose gels. An example
of typical microsatellites polymorphism for a single primer pair (sb6-36) is
shown in Figure 4.5. The level of polymorphism was estimated from the
number of alleles and their frequency was analysed. The primers, their
repeat number, allele number, and polymorphism information content (PlC)
are given in Table 4.4. In total, the 10 primer pairs detected 48 polymophic
and monomorphic alleles. The number of alleles scored per primer pair
varied from one (for sb4-15) to nine (for sb4-32), with an average of 4.8
alleles per primer pair. The number of alleles- observed for most of the loci
was in agreement with the range reported in sorghum (Dean et al., 1999;
Brown et al., 1996), maize (Senior et al., 1998) and wheat (Ahmad, 2002),
but lower than in other studies reported in sorghum (Djé et al., 2000;
Ghebru et al., 2002).
The PlC values for SSR loci ranged from 0.52 for sb4-32 and sb6-342 loci.
to 0.79 for sb4-22, while one primer pair (sb4-15) was found to be
monomorphic. The mean PlC value was 0.645. Smith et al. (2000) reported
similar observations of PlC in sorghum detected by SSR markers. In this
study, however, no significant correlation was detected between the repeat
number and the allele number (r = -0.31) or polymorphic information
content (r = -0.07). The 43 polymorphic SSR alleles collectively yielded
unique genotypes for each of the 45 accessions. A wide range of fragment
sizes (differences between the shortest and longest alleles ranged from 51
to 421 bp was obtained, with most within the ranges previously reported in
studies with different sorghum germ plasm (Ghebru et al., 2002; Dean et al.,
1999; Djé et al., 2000).
Chapter 4 DNA markers 89
2 3 4 5 8 7 8 9 10 11 12
Figure 4.5 Agarose gel electrophorsis of SSR-PCR products amplified using
primer Sb6-36 (AG)19.Lanes 1 and 12 contain 100 bp size standard markers.
Lanes 2 to 11 contains amplification products using bulk DNA from sorghum
accessions 1 to 10, according to the accessions list in Table 3.1.
4.3.2.2 Genetic diversity
The distribution of pair-wise genetic distance among 45 accessions for nine
microsatellite loci is shown in Figure 4.6. Genetic distances ranged from
o.15 (for ETS 993 x ETS 804) to 0.76 (for Fandisha faca' a x Warabi), with
the majority (63%) of pair-wise comparisons having a value from 0.60 to
0.70. Cluster analysis identified two major clusters consisting of 5
subclusters at a genetic distance of 0.58 (Figure 4.7). Most of the
accessions from the same collection area clustered together. Cluster I
consists of accession #1, 2 and 3, the first two from Chinhakssen and the
third from Haro Maya all having the same local name, Wagare. Cluster II
comprised a large grouping of accessions predominantly from Tulo, and
Chapter 4 DNA markers 90
some AU germ plasm, and also accessions from MeUa and Deder. Cluster
III contained accessions collected from Aanaas in the Western Hararghe
zone, Doba, Chiro and Tulo. Cluster IV was a group of accessions mainly
from Kurfa Challe. Accession #4 from Haro Maya also fell within this
cluster. A large grouping of AU germplasm, as well as the three accessions
from Habro and one Chiro collected accession were found in Cluster V.
Table 4.4 Number of alleles, size range (in base pairs), and PlC value
for SSR loci found in 45 accessions of sorghum.
SSR Repeat No. of Size PlC
Locus Number alleles range value
Sb1-10 (AG)27 4 110-510 0.717
Sb4-15 (AG)16 1 135 0.000
Sb4-22 (ACGAC)4/(AG)6 6 254-492 0.793
Sb4-32 (AG)1s 9 112-533 0.521
Sb5-85 (AG)12 7 109-510 0.594
Sb5-236 (AG)2o 4 110-265 0.578
Sb6-36 (AG)19 5 165-279 0.773
Sb6-57 (AG)18 4 292-343 0.683
Sb6-84 (AG)14 3 173-295 0.630
Sb6-342 (AC)2s 5 254-634 0.520
Average 4.8 0.645
From Brown et al., 1996
Chapter 4 DNA markers 91
400.0
C/)
oc
.:C::/):
Cl)
a.
E
8
ID
:C/)~
Cl)
a.
o
ID
.0
E
:::J
Z
0.2 0.4 0.6 0.8
Genetic distance coefficient
Figure 4.6 Frequency distribution of pair-wise genetic distance coefficients
among 45 sorghum accessions based on SSR data.
A principal component analysis was performed on the basis of allele
frequencies, and individual accessions were plotted against the first two
principal components (PC1 and PC2) that were responsible for 36.8% of
the total variance (Fig. 4.8). The first axis accounted for 19.2% of the
variance and the principal component scores were positively influenced by
primer pairs sb1-1 0, sb6-84 and sb6-36 (with factor loading 0.82, 0.82 and
0.56, respectively). The second axis described 17.6% of the variance and
was largely influenced by sb6-57 (loading 0.88) and sb4-32 (loading 0.75).
The majority of the accessions clustered very closely in the bottom left
quadrant, showing the existence of a bias in terms of selection pressure for
the microsatellites. Accessions #14, 25 and 43 occupied the most extreme
of the first, third and fourth quadrants, respectively. The two accessions
from Chinhakssen (#1 and 2) together fell in the lower left quadrant.
Chapter 4 DNA markers 92
I
- I v
~
L[_f J- IV
1
r---
III
~
r-l_
- ----1
-l II
-
L-
L--..-j
0.80 0.60 0.40 0.20 0.00
Dissimilarity
Figure 4.7 Dendrogram constructed for 45 accessions of sorghum based on data
from 43 polymorphic SSR alleles. [Each horizontal line represents a separate
sorghum population sample, and the number-letter after each line indicate sample
number and the locality from which the accession was collected (abbreviations as
in Figure 3.1 )].
The use of microsatellite markers in the study of genetic diversity within
and between sorghum germplasm have already been demonstrated in
previous studies (Brown et al., 1996; Dean et al., 1999; Djé et al., 2000;
Ghebru et al., 2002). SSR markers identified two to six fragment sizes per
polymorphic locus with a total diversity index ranging from 0.21 to 0.73 on
17 temperately and tropically adapted lines of sorghum. The SSR loci are
widely spread over sorghum genome with 14 of these 10 sorghum different
linkage groups (Dean et al., 1999). With the same SSR set, Dean et al.
(1999) assessed the diversity among 95 'Orange' accessions and found
from three to 11 alleles per locus and a genetic diversity ranging from 0.16
Chapter 4 DNA markers 93
to 0.77. Furthermore, using the same SSR set, Grenier et al. (2000)
reported from seven to 33 alleles per locus and a genetic diversity range of
0.71 to 0.93.
The present study is the first report of the application of the microsatellite
markers for genetic diversity estimation and cultivar identification in
Ethiopian originated sorghums. It is interesting to note the bias identified
through PCA. This suggests that SSR loci are under selection pressure.
The use of additional microsatellite loci may collectively allow the sorghum
genome to be surveyed more comprehensively, and include the molecular
mapping of important traits.
4.00
043
02
2.50
01
031
N 030 08
0 1.00 025
0... 09
07
~3
-0.50 CO 1t); 014O~~~ ~420 12
045
011
017
013
-2.00
-2.00 -0.75 0.50 1.75 3.00
PC1
Figure 4.8 Plot of the 45 sorghum accessions against the first two principal
components analysis (PC1 and PC2) computed using the SSR data. Individual
accessions are designated using numbers as described in Table 3.1.
4.3.3 Comparison of AFLP and SSR markers
Both PCR-based DNA marker techniques generated high levels of
polymorph isms to distinguish between all the studied sorghum accessions.
The percentage of polymorphic bands was slightly higher in SSR (90%)
~---------------------------------------------
Chapter 4 DNA markers 94
than AFLP (85%) data. However, the AFLP technique was 14 times more
efficient in detecting polymorphism per assay, as it has a higher multiplex
ratio. The average polymorphic information content (PlC) values among the
45 accessions for AFLP and SSR markers were 0.464 and 0.645,
respectively (Table 4.5).
Although there are some similarities between the dendrograms from AFLP
(Figure 4.3) and SSR (Figure 4.7) data, both techniques produced different
results. Genetic clustering of sorghum accessions, depending on the site of
collection / geographical origin, was clearly observed in SSR based
dendrogram compared to AFLP data. For example, the three accessions
from Metta (#8, 9 and 10) that classified together based on SSR markers,
accession #8 was clustered in a different cluster based on AFLP markers.
However, some accessions from the same collection site (#1 and 2) and
sharing the same local name, Wagare (#, 1, 2 and 3), were genetically
distinct.
Forty-five sorghum accessions were analysed for variability using eight
selective AFLP primer combinations and 10 microsatellite loci. Both AFLP
and SSR markers were highly efficient in detecting polymorph isms among
the studied accessions, showing their usefulness in characterisation of
sorghum germplasm accessions. The overall results confirm the high
variability that can be found among landrace populations, underlining the
value of landraces for future breeding programmes, which require the
flexibility offered by a wide gene pool, in order to improve grain quality,
neutralize the effects of environmental stresses, such as drought.
Chapter 4 DNA markers 95
Table 4.5 Level of polymorphism and comparison of the amount of
information obtained with AFLP and SSR markers in 45 sorghum
accessions.
Marker system
Parameters AFLP SSR
Total number of assays 8 (primer combinations) 10 (primer pairs)
Total number of bands 651 48
Number of polymorphic bands 552 43
Mean number of bands per assay 81.4 4.8
% polymorphic bands 85.8 89.6
PlC 0.46 0.65
~ 4.4 Conclusions
Forty-five sorghum accessions consisting of landraces, elite breeding
I entries and improved cultivars were analysed for variability using eight
AFLP primer combinations and 10 SSR loci. The UPGMA clustering
algorithms grouped the accessions into five clusters for each marker
technique. However, the results indicated that the accessions could be
more clearly discriminated with AFLP markers than microsatellite markers.
This may result from the relatively low microsatellite alieles used, and
availability of more SSR loci would facilitate a better identification and
discrimination of sorghum accessions. This study has also identified a
selection bias in SSR data that was not observed in AFLP data for the
accessions studied.
This study also provided information for evaluating how well the sorghum
landrace collection has been managed by SIP/AU. The breeding entries /
improved cultivars included in the study were grouped in four and two,
different clusters based on AFLP and microsatellite markers, respectively.
Chapter 4 DNA markers 96
REFERENCES
Ahmad, M. 2002. Assessment of genomic diversity among wheat
genotypes as determined by simple sequence repeats. Genome 45:
646-651.
Ahnert, D., Lee, M., Austin, D.F., Livini, C., Woodman, W.L.,
Openshaw, S.J., Smith, J.S.C., Porter, K. and Dalton, G. 1996.
Genetic diversity among elite sorghum inbred lines assessesd with
DNA markers and pedigree information. Crop Sci. 36:1385-1392.
Ayana, A., Bryngelsson, T. and Bekeie, E. 2000. Genetic variation of
Ethiopian and Eritrean sorghum [Sorghum bicolor (L.) Moeneh]
germplasm assessed by random amplified polymorphic DNA
(RAPD). Genetic Resources and Crop Evolution 47:471-482.
Bai, G., Ayele, M., Tefera, H., and Nguyen, H.T. 1999. Amplified fragment
length polymorphism analysis of tef [Eragrostis tef (Zucc.) Trotter].
Crop Sci. 39:819-824.
Brown, S.M., Hopkins, M.S., Mitchell, S.E., Senior, M.L., Wang, T.Y.,
Duncan, R.R., Gonzalez-Candelas, F. and Kresovich, S. 1996.
Multiple methods for the identification of polymorphic simple
sequence repeats (SSRs) in sorghum [Sorghum biceter (L.)
Moeneh]. Theor. Appl. Genet. 93: 190-198.
Central Statistical Authority (CSA). 2000. The Federal Democratic
Republic of Ethiopia: Agricultural sample survey report on area and
production for major crops, Statistical Bulletin No 227. Addis Ababa,
Ethiopia, pp 16-17.
Dean, R.E., Dahlberg, J.A., Hopkins, M.S. Mitchell, S.E. and Kresovich,
S. 1999. Genetic redundancy and diversity among 'Orange'
accessions in the U.S. national sorghum collection as assessed with
simple sequence repeat (SSR) markers. Crop Sci. 39:1215-1221.
Djé, Y., Forcioli, D., Ater, M., Lefebvre, C. and Vekemans, X. 1999.
Assessing population genetic structure of sorghum landraces from
Chapter 4 DNA markers 97
north-western Morocco using allozyme and microsatellite markers.
Theor. Appl. Genet. 99:57-163.
Djê, Y., Heuertz, M., Lefébvre, C. and Vekemans, X. 2000. Assessment
of genetic diversity within and among germplasm accessions in
cultivated sorghum using microsatellite markers. Theor. Appl. Genet.
100: 918-925.
Doggett, H. 1988. Sorghum. Longman Group U.K. ltd. Essex, England.
Edwards, K., Johnstone, C. and Thompson, C. 1991. A simple and rapid
method for the preparation of plant genomic DNA for PCR analysis.
Nucl. Acid. Res. 19:1349-1458.
Geleta, N. 1997. Variability and association of morpho-agronomic
characters with reference to highland sorghum [Sorghum bicolor (L.)
Moeneh] landraces of Hararghie, Eastern Ethiopia. MSc. Thesis.
Alemaya University of Agriculture, Alemaya, Ethiopia.
Gepts, P. 1993. The use of molecular and biochemical markers in crop-
evaluation studies. In: Hecht, M.K. (Ed.). Evolutionary biology, vol.
27. Plenum press, New York, pp 51-94.
Ghebru, B., Schmidt, R.J., and Bennetzen, J.L. 2002. Genetic diversity
of Eritrean sorghum landraces assessed with simple sequence
repeat (SSR) markers. Theor. Appl, Genet. 105:229-236.
Grenier, C., Deu, M., kresovich, 5., Bramel-Cox, P.J. and Hamon, P.
2000. Assessment of Genetic Diversity in Three Subsets Constituted
from ICRISAT Sorghum Collection Using Random vs Non-Random
Sampling Procedures. Theor. Appl. Genet. 101 :197-202.
Menkir, A., Goldsbrough, P. and Ejeta, G. 1997. RAPD based
assessment of genetic diversity in cultivated races of sorghum. Crop
Sci.37:564-569.
Mitchell, S.E., Kresovich, 5., Jester, C.A., Hernández, J. and Szewc-
McFadden, A.K. 1997. Application of multiplex and fluorescence-
based, semi-automated allele sizing technology for genotyping plant
genetic resources. Crop Sci. 37:617-624.
Chapter 4 DNA markers 98
MorelI, M.K., Peakall, R., Appels, R., Preston, L.R., and Lloyd, H.L.
1995. DNA profiling techniques for plant variety identification.
Australian J. Exp. Agric. 35:807-819.
NeSS, 2000. Number Cruncher Statistical Systems, Dr. Jerry L. Hintze,
329 North 1000 East, Kaysville, Utah 84037, Canada.
Schut, J.W., ai, X. and Stam, P. 1997. Association between relationship
measures based on AFLP markers, pedigree data and
morphological traits in barley. Theor. Appl. Genet. 95:1161-1168.
Senior, M.L., Murphy, J.P., Goodman, M.M. and Stuber, C.W. 1998.
Utility of SSRs for determining genetic similarities and relationships
in maize using an agarose gel system. Crop Sci. 38: 1088-1 098.
Smith, J.S.C., Kresovich, S., Hopkins, M.S., Mitchell, S.E., Dean, R.E.,
Woodman, W.L., Lee, M. and Porter, K. 2000. Genetic Diversity
among Elite Sorghum Inbred Lines Assessed with Simple Sequence
Repeats. Crop Sci. 40:226-232.
Stemier, A.B.L., Harlan, J.R. and de Wet, J.M.J. 1977. The sorghums of
Ethiopia. Econ. Bot. 31 :446-460.
Tautz, D. 1989. Hypervariability of simple sequences as a general source
for polymorphic DNA markers. Nucl. Acids Res. 17: 6463-6471.
Vavilov, N.1. 1951. The origin, variation, immunity and breeding of
cultivated plants. Chronica Botanica 13:1-366.
Welsh, J. and McClelland, M. 1990. Fingerprinting genomes using PCR
with arbitrary primers. Nucl. Acids Res. 18: 7213-7218.
Zabeau, M. and Vos, P. 1993. Selective restriction fragment amplification:
a general method for DNA fingerprinting. European patent
Application EP 534858A 1.
99
CHAPTER 5
COMPARISON OF AFLP, SSR AND MORPHO-AGRONOMICAL MARKERS
FOR ESTIMATING GENETIC DIVERSITY IN SORGHUM
Abstract
A comparison of the results of the different methods of the estimation of
genetic diversity is important to evaluate their utility as a tool in plant
breeding and germplasm conservation. Amplified fragment length
polymorph isms (AFLP's), microsatellites or (SSR's) and morpho-agronomical
markers were used to evaluate 45 sorghum accessions in terms of genetic
diversity assessment and discrimination power. The diversity index for SSR
markers was a higher (0.645) than for AFLPs (0.464). The average pair-wise
genetic distance estimates were 0.566 for morpho-agronomical markers,
0.604 for SSR and 0.615 for AFLP based data. There was no congruency
between dendrograms constructed from the three different data matrices and
the combined matrix. In some cases, accessions from the same locality
and/or with the same name were found to exhibit particularly high levels of
association overall the different methods used. Both AFLP and SSR-based
data matrices differentiated between the 45 accessions more distinctly than
morpho-agronomical trait data and genetic diversity estimates from morpho-
agronomic traits was not well suited for elucidating more complex
relationships but was adequate for estimating the overall pattern of genetic
variation among the accessions. Although relationships determined by
molecular data are different to those determined by morpho-agronomical
traits, this remains a useful way to assess diversity for breeding purposes
even though the more detailed genetic relationships may be misrepresented.
Therefore the strategy of combining molecular and morpho-agronomic traits
would be best to study genetic diversity of sorghum accessions.
Chapter 5 Comparison of markers 100
5.1 Introduction
Sorghum [Sorghum bicolor (L.) Moeneh] is one of the most important cereals
of the semi-arid tropics. It is the third most important cereal crop after tef
[Eragrostis tef (Zucc.) Trotter] and maize, and first in the eastern regions of
Ethiopia, in terms of cultivation area and production (CSA, 2000). Current
models of sorghum race and variety distribution differentiate the main S.
bicolor races as Bicolor, Caudatum, Durra, Guinea, and Kafir (Harlan and de
Wet, 1972). All of these (except Kafir) are found in Ethiopia (Stemier et al.,
1977; Teshome et al., 1997), and have a broad agro-ecological variation,
which has resulted in the accumulation of genetic diversity in this crop
species.
Estimation of genetic diversity to identify groups with similar genotypes is
important for conserving, evaluating and utilising genetic resources, for
studying the diversity of different germ plasm as possible sources of genes
that can improve the performance of cultivars, and for determining the
uniqueness and distinctness of the phenotypic and genetic constitution of
genotypes with the purpose of protecting the breeder's intellectual property
rights (Franco et al., 2001; Subudhi et al., 2002). In the past, plant breeders
made selections of breeding material on the basis of morphological
characteristics that were readily observable and that were co-inherited with
the desired trait. Although these methods remain effective, morphological
comparisons have limitations, including the influence of environment or
management practice, subjectivity in the character evaluation are also linked
to developmental stage (MorelI et al., 1995). In addition, morphology is not
efficient in discrimination between closely related lines that differ for the trait
of interest due to the scarcity of phenotypic markers (Perkin-Elmer, 1996).
The use of DNA markers for evaluating genetic diversity has improved
dramatically since the advent of the polymerase chain reaction. Different
techniques are used to generate DNA based markers that result in different
estimates of genetic similarity depending on the number of markers
generated and the genome coverage. SSR marker technique has been used
Chapter 5 Comparison of markers 101
to characterize genetic diversity represented by elite inbred genotypes and
cultivated races of sorghum (Brown et al., 1996; Dean et al., 1999; Djé et al.,
2000; Smith et al., 2000).
Although DNA markers have been compared in the assessment of sorghum
genetic diversity (Yang et al., 1996; de Oliveira et al., 1996; Smith et al.,
2000), both AFLPs and SSRs are more recent techniques, and have not
been evaluated for use in discriminating between different sorghum
accessions. The objective of the study was to compare the use of AFLPs,
SSRs and morpho-agronomical markers to assess genetic diversity in
different accessions of sorghum in Ethiopia.
5.2 Materials and methods
5.2.1 Plant material
The sorghum accessions listed in Table 3.1 were used.
5.2.2 Methods
5.2.2.1 Morpho-agronomical traits
The data collection methods described in Chapter Three, Sections 3.2.1.2
and 3.2.2.3 were used. Ten qualitative and 16 quantitative traits data were
coded as presence or absence (on a 1/0) basis to compare them with DNA
markers data. For qualitative traits, the presence or absence of the different
variants was coded as 1 or 0, respectively (Appendix IV). In the case of the
quantitative traits, data were transformed to binary data (Appendix V) by
using a statistical significance test after running an Analysis of Variance
(ANOVA) on the original data.
Chapter 5 Comparison of markers 102
5.2.2.2 DNA markers
5.2.2.2.1 AFLP's
The generation of AFLP markers is described in Chapter Four, Section 4.2.3.
AFLP analysis was performed using GeneScan® software. Only clear and
unambiguous bands were included in the analyses. AFLP fragments larger
than or equal to 60 bp with a peak height above or equal to 45 RFUs were
scored.
5.2.2.2.2 Microsatellites (SSR's)
Ten microsatellite sorghum primer pairs were used in this study. A standard
peR method was used to amplify microsateliites, the detail is described in
Chapter Four, Section 4.2.4. The result of the PCR amplification was
analyzed by electrophoresis on a 2% agarose gel (Molecular Screening
agarose Roche) in TAE buffer (40 mM Tris-acetate, 1 mM EDTA, pH 8.0) run
at 80V for 2.5 hours. Amplified fragments were visualised and sized using the
Gel Doe 1000™ image analysis system (Biorad) after ethidium bromide (0.5
ug / ml) staining.
5.2.3 Data analysis
A combined binary matrix included the morpho-agronomical matrix with the
AFLP and SSR data matrices. The NCSS computer program (NCSS, 2000)
was used for all data analyses. The dissimilarity values were used to
graphically represent genetic relationships between the accessions by the
unweighted pair group method using an arithmetic average (UPGMA)
clustering algorithm the NCSS computer programme (NCSS, 2000), and
relationships between accessions visualised as dendrograms.
Chapter 5 Comparison of markers 103
5.3 Results and discussion
5.3.1 Level of polymorphism detection
The levels of polymorphism detected by the different marker approaches
showed wide differences (Table 5.1). The eight AFLP primer pairs used in
this study generated 552 polymorphic bands (average = 81 per pair), and the
10 microsatellite loci used produced 43 polymorphic bands (4.4 per pair)
across 45 sorghum accessions. The highest polymorphism level was
obtained for AFLP markers compared to microsatellite and morpho-
agronomical markers. These can be probably attributed to the small data set
for SSR and morpho-agronomic techniques compared to the AFLPs.
Polymorphism detection efficiency among sorghum accessions by AFLPs
and SSRs compared favourably with other available marker systems. Yang
et al. (1996) detected 55%, 25%, 44% polymorphic bands for RFLP, RAPO,
and ISSR techniques, respectively, in a selection of 34 Chinese sorghums.
5.3.2 Genetic diversity estimation
The summary of genetic distance (GO) coefficients calculated using
Euclidean distance type from the individual marker data matrices, pair of data
matrices and from the combined (AFLP, SSR and morpho-agronomic traits)
data matrix is shown in Table 5.1. The GDs were estimated using 552
polymorphic AFLP fragments, 43 SSR polymorphic alleles, and 26 morpho-
agronomical traits with 96 variants. The average GO estimates were 0.615
(for AFLP), 0.604 (for SSR), 0.566 (for morpho-agronomical traits) and 0.609
(for the combined data set). Range-wise, AFLP data produced lower (0.413
to 0.745) GD estimates compared to SSR (0.152 to 0.762), but the average
GOs were very close in both. Furthermore, the genetic diversity estimates
resulted with AFLP and microsatellite data sets were comparable despite the
differences in the number of markers generated by each technique. An
example of the pair-wise GOs estimates for the three markers were shown in
(Appendices VI to VIII).
Chapter 5 Comparison of markers 104
Table 5.1 Number of polymorphic bands, average and range of pair-wise
genetic distance (GO) estimates among 45 sorghum accessions based on
AFLP, SSR, morpho-agronomical traits data.
Marker No. of polymorphic Average Range
system markers
AFLP 552 0.615 0.413-0.745
SSR 43 0.604 0.152-0.762
Morpho-agro 96 0.566 0.354-0.707
AFLP + SSR 595 0.615 0.423-0.741
Combined 691 0.609 0.426-0.723
GO estimates can be affected by several factors such as, the distribution of
markers in the genome (genome coverage) and the nature of evolutionary
mechanisms underlying the variation measured (Powell et al., 1996). AFLPs
are believed to detect mainly point mutations while SSRs are specific to
hypervariabie loci (Giancola et al., 2002). Another important factor is the
influence of individual loci used for the analysis. While the SSR loci were
based on availability, AFLP loci were randomly distributed whereas morpho-
agronomical traits were selected.
Correlations between genetic distance matrices based on the two molecular
marker techniques and the morpho-agronomical traits were significant (Table
5.2). In a similar study, Tatineni et al. (1996) reported a high correlation
between RAPO and morphological characters. However, a lower correlation
between AFLP and SSR genetic distance estimates has been reported
(Giancola et al., 2002). Powell et al. (1996) also reported that SSR similarity
estimates were not significantly correlated to RFLPs, RAPOs, or AFLPs in
soybean. Renganayaki et al. (2001) indicated that if comparisons are
restricted within a species, then overall correlations between marker systems
are significantly lower.
Chapter 5 Comparison of markers 105
Table 5.2 Correlation coefficients between genetic distance values
estimated for the three marker techniques (AFLP, SSR and morpho-
agronomical traits), with sample size of 990.
AFLPs SSRs
SSRs 0.281**
Morpho-agronomical traits 0.084* 0.188**
*, ** significant at p = 0.05 and p = 0.001, respectively.
5.3.3 Clustering based on AFLP, SSR and morpho-agronomical markers
Dendrograms resulting from UPGMA cluster analyses of morpho-
agronomical, AFLP, SSR, AFLP and SSR, and combined data are illustrated
in Figure 5.1a to e. On the whole, the dendrograms separated the 45
sorghum accessions into five to seven cluster groups. However, the output of
each cluster tree was rather unique with some evident similarities; for
instance, three of the five accessions with the name Wagare showed a high
level of similarity across all the dendrograms, while the fourth and fifth were
found to be quite different. In a similar study, Yang et al. (1996) found
differences among RFLP, RAPD, and Inter-simple sequence repeat
amplification (ISSR) markers in the discrimination of 34 Chinese sorghum
lines. The lack of correspondence between levels of genetic diversity
obtained from different techniques has already been reported (Djé et aI.,
1999). Similarly, after studying sorghum accessions from Ethiopia and
Eritrea, Ayana (2001) indicated a higher level of variation for morphological
traits than those obtained for allozymes and RAPD markers. This situation,
according to Yang et al. (1996), could have originated by contamination of
the original material, by independent selection of very different inbreds from
an originally diverse line, or by different materials receiving the same name.
On the other hand, the similarity obtained in the dendrograms produced by
the UPGMA analytical method proved the clustering to be acceptable.
Chapter 5 Comparison of markers 106
Clustering based on morpho-agronomical data analysis produced five
clusters (Figure 5.1a), and the two accessions (1 and 2) from Chinhakssen
and one (13) from Tulo were found the most different from the rest
accessions. Cluster II contained the most accessions from different localities,
nine from AU, five from Tulo, three each from MeUa and Doba, two each
from Deder and Chiro, and one each from Habro and Kurffa Challe. AFLP
based clustering (Figure 5.1 b) also resulted in five groups, where cluster II
constituted the largest accessions from various localities. In cluster I,
accession 41 was the most distinct. Cluster IV was formed by five accessions
only from AU.
In the dendrogram constructed from SSR markers analysis (Figure 5.1c), it
can be observed that there was more grouping based on the site of collection
than observed in the morphological and AFLP based clustering. For instance,
all accessions from Doba were grouped together with two Tulo and two Chiro
collections. The groupings for accessions from Kurffa Challe and Habro also
support the clustering together of accessions from the same site. But, AFLP
and SSR based cluster analysis (Figure 5.1d) showed more dissimilarity
among accessions from the same site. The clustering based on AFLP + SSR
data analysis largely resembles that of AFLP data alone, and it is probably
due to the large data point obtained from AFLPs when compared to SSRs.
As the result greater degree of discrimination was obtained in AFLPs and
AFLPs + SSRs data than SSRs alone. In addition, DNA markers may be
affected by selection, drift, and mutation (Senior et al., 1998). The same
author further indicated that incongruities can result from the clustering
process whenever clusters are non-overlapping.
A clustering performed on the combined data is shown in Figure 5.1e.
Cluster I contained a mixed group of accessions from Chinhakssen, Haro
Maya, Habro and AU. All of the accessions from Doba were found in cluster
II. Accession 11 and 12, both from Deder clustered separately in cluster Ill.
Similarly, accessions 15 and 20 exhibited the most dissimilarity from the
other Tulo accessions. Except accessions 41, 42 and 39 accessions from AU
consistently showed closer relationships.
Chapter 5 Comparison of markers 107
The lack of strong collection site based differentiation observed in cluster and
principal component analyses could be partly ascribed to gene flow between
the sites. The clustering together of accessions from the same sites is an
indication of the evolution of eo-adaptive association of quantitative
characters (Zhong and Qualset, 1995). The consistent clustering of most
breeding entries / improved cultivars from SIP/AU together in present study
apparently substantiates this. Individual characters differ in their patterns of
distribution and amount of variation. In general, the present results showed
that by using the AFLP or SSR DNA technique, a large set of informative
data could be generated in less time than with morpho-agronomical analysis.
Also when simultaneously using DNA markers and morpho-agronomic traits
to classify genotypes, it is possible to obtain a relevant minimum subset of
marker-fragments that can be used in conjunction with available morpho-
agronomic data to better classify genotypes compared to using only the
quantitative or only the qualitative traits. The present results imply that
although morpho-agronomical characterisation is influenced by the
environment and is time consuming in general, among other disadvantages
in relation to AFLP's and SSR's, it can still be an important and practical
means of making progress in germplasm evaluation by conservationists and
breeders.
Chapter 5 Comparison of markers 108
Morpho-agronomic
(a) } v
} IV
} III
II
0.80 0.60 0.40 0.20
Dissimilarity
AFLP
(b)
li
I
·0.80 0.60 0.40 0.20 0.00
Dissimilarity
Chapter 5 Comparison of markers 109
SSR
(c)
-AU
-AU
~i4=r -AU-AU-AU \I-AU
~~~
_-~~- cc
r
1\1
~ ~
III
=
~ - I
g
- b
- I
--t =A8
-AU
- U
'- ---{_
II
- d
- I
~ - = ~
f=~
0.80 0.60 0.40 0.20 0.00
Dissimilarity
AFLP + SSR
(d) \Ill
\II
\I
1\1
III
II
0.80 0.60 0.40 0.20 0.00
Dissimilarity
Chapter 5 Comparison of markers 110
AFLP + SSR + Morpho-agronomic traits
(e) r---l 20-TI }-15-TI VI
45-AU
44-AU
43-AU -
40-AU V
38-AU
36-AU
~ 39-AU
8-Mt 1 IV
r-1 12-Dd
11-Dd
.---! 18-TI IIII
r- 1O-Mt
r-l-r 24-Db23-Db
17-TI
42-AU
37-AU
35-AU
34-Hb
~ 32-Hb30-Cr
29-Cr
28-Db
L- 27-Db II
~ 26-Db
25-Db
22-TI
21-TI
16-TI
14-TI
~ 13-TI
19-TI
~ 31-Cr
9-Mt
41-AU
33-Hb
7-Kc
6-Kc
5-Kc
4-Hm
3-Hm
~ 2-Ch1-Ch
0.80 0.60 0.40 0.20 0.00
Dissimilarity
Figure 5.1 Dendrograms of 45 sorghum accessions constructed using
dissimilarity matrix from (a) morpho-agronomic, (b) AFLP, (c) SSR, (d)
AFLP + SSR and (e) combined data.
Chapter 5 Comparison of markers 111
5.4 Conclusions
Characterisation of sorghum accessions at the DNA level can help identify
genetically representative, non-redundant sets of germplasm for sorghum
breeding and conservation purposes. As observed from significant
correlation coefficients obtained between genetic distance values from the
three marker techniques, all have shown a comparable genetic diversity
level. AFLP markers were more powerful than SSRs in distinguishing
accessions that were collected from the same site. Overall, from this result,
using DNA-based markers clearly suggests that conventional methods have
been effective in selecting unique collections, and further indicate that
diversity assessed by molecular markers may efficiently represent the
genetic diversity in morpho-agronomic traits. Although the relationships
determined by molecular data are different to those identified by morpho-
agronomical traits, the latter is still useful in assessing genetic diversity for
the purpose of breeding selection on condition that the genotypes under
investigation are not too closely related. Molecular techniques have a clear
advantage over morpho-agronomical traits in elucidating complex
relationships, especially of genotypes sharing morpho-agronomic traits or
coming from the same geographic location.
Chapter 5 Comparison of markers 112
REFERENCES
Ayana, A. 2001. Genetic diversity in sorghum (Sorghum bicolor (L.) Moeneh)
germplasm from Ethiopia and Eritrea. Ph.D. dissertation. Department
of Biology, Science Faculty, Addis Ababa University, Addis Ababa,
Ethiopia.
Brown, S.M., Hopkins, M.S., Mitchell, S.E., Senior, M.L., Wang, T.Y.,
Duncan, R.R., Gonzalez-Candelas, F. and Kresovich, S. 1996.
Multiple methods for the identification of polymorphic simple sequence
repeats (SSRs) in sorghum [Sorghum bicolor (L.) Moeneh]. Theor.
Appl. Genet. 93: 190-198.
Central Statistical Authority (CSA). 2000. The Federal Democratic
Republic of Ethiopia: Agricultural sample survey report on area and
production for major crops, Statistical Bulletin No 227. Addis Ababa,
Ethiopia, pp 16-68.
de Olivera, A.C., Richter, T. and Bennetzen, J.L. 1996. Regional and racial
specificities in sorghum germplasm assessed with DNA markers.
Genome 39:579-587.
Dean, R.E., Dahlberg, J.A., Hopkins, M.S. Mitchell, S.E. and Kresovich,
S. 1999. Genetic redundancy and diversity among 'Orange'
accessions in the U.S. national sorghum collection as assessed with
simple sequence repeat (SSR) markers. Crop Sci. 39:1215-1221.
Djê, Y., Forcioli, D., Ater, M., Lefebvre, C. and Vekemans, X. 1999.
Assessing population genetic structure of sorghum landraces from
north-western Morocco using allozyme and microsatellite markers.
Theor. Appl. Genet. 99:57-163.
Djê, Y., Heuertz, M., Lefébvre, C. and Vekemans, X. 2000. Assessment of
genetic diversity within and among germplasm accessions in
cultivated sorghum using microsatellite markers. Theor. Appl. Genet.
100: 918-925.
Chapter 5 Comparison of markers 113
Franco, J., Crossa, J., Ribaut, J.M., Betran, J., Warburton, M.L. and
Khairallah, M. 2001. A method for combining molecular markers and
phenotypic attributes for classifying plant genotypes. Theor. Appl.
Genet. 103:944-952.
Giancola, S., Marcucci Poltri, 5., Lacaze, P. and Hopp, H.E. 2002.
Feasibility of integration of molecular markers and morphological
descriptors in a real case study of a plant variety protection system for
soybean. Euphytica 127:95-113.
Harlan, J.R., and de Wet, J.M.J. 1972. A simplified classification of
cultivated sorghum. Crop Sci. 12:172-176.
MorelI, M.K., Peakall, R., Appels, R., Preston, L.R., and Lloyd, H.L. 1995.
DNA profiling techniques for plant variety identification. Australian J.
Exp. Agric. 35:807-819.
Ness, 2000. Number Cruncher Statistical Systems, Dr. Jerry L. Hintze, 329
North 1000 East, Kaysville, Utah 84037, Canada.
Perkin-Elmer. 1996. AFLP Plant Mapping Kit-the PCR marker of choice for
plant mapping.
Powell, W., Morgante, M., Andre, C., Hanafey, M., Vogel, J., Tingey, S.
and Rafalski, A. 1996. The comparison of RFLP, RAPD, AFLP and
SSR (microsatellite) markers for germplasm analysis. Molecular
Breed. 2:225-238.
Renganayaki, K., Read, J.C. and Fritz, A.K. 2001. Genetic diversity among
Texas bluegrass genotypes (Poa arachnifera Torr.) revealed by AFLP
and RAPD markers. Theor. Appl. Genet. 102:1037-1045.
Smith, J.S.C., Kresovich, S., Hopkins, M.S., Mitchell, S.E., Dean, R.E.,
Woodman, W.L., Lee, M. and Porter, K. 2000. Genetic Diversity
among Elite Sorghum Inbred Lines Assessed with Simple Sequence
Repeats. Crop Sci. 40:226-232.
Stemier, A.B.L., Harlan, J.R. and de Wet, J.M.J. 1977. The sorghums of
Ethiopia. Econ. Bot. 31 :446-460.
Chapter 5 Comparison of markers 114
Subudhi, P.K., Nguyen, H.T., Gilbert, M.L. and Rosenow, D.T. 2002.
Sorghum improvement: Past achievements and future prospects. In:
Kang, M.S. (Ed.). Crop improvement: Challenges in the Twenty-First
Century. The Haworth Press, Binghamton, NY, pp. 109-159.
Tatineni, V., CantreIl, R.G. and Davis D.D. 1996. Genetic diversity in elite
cotton germ plasm determined by morphological characters and
RAPDs. Crop Sci. 36:186-192.
Teshome, A., Baum, B.R., Fahrig, L., Torrance, J.K., Arnason, T.J. and
Lambert, J.D. 1997. Sorghum [Sorghum bicolor (L.) Moench]
landrace variation and classification in North Shewa and South Welo,
Ethiopia. Euphytica 97:255-263.
Yang, W., de Oliveira, A.C., Godwin, I., Schertz, K. and bennetzen, J.L.
1996. Comparsion of DNA marker technologies in characterizing plant
genome diversity: variability in Chinese sorghums. Crop Sci. 36: 1669-
1676.
Zhong, G.Y. and Qualset, C.O. 1995. Quantitative genetic diversity and
conservation strategies for an allogamous annual species, dasypyrum
villosum (L.) Candargy (Poaceae). Theor. Appl. Genet. 91 :1064-1073.
115
CHAPTER 6
PHYSICO-CHEMICAL ANALYSIS OF SORGHUM AND SENSORY EVALUATION
OF INJERA
Abstract
Samples of 13 genetically diverse sorghum accessions were analysed for physical
properties and chemical composition. The food (lnjera) quality and the phenolic
(condensed tannins) content of six sorghum samples were also analysed. The
accessions showed a wide variation in protein (7.99 to 17.8%), lipids (2.52 to
3.72%), starch (51.88 to 85%), and amylose (12.30 to 28.38%) content. Grain
weight ranged from 19.5 to 33.7 g/1000 seeds, and endosperm texture varied from
intermediate to soft (completely starchy). Linoleic acid (18:2) and oleic acid (18: 1)
were found to be the major fatty acid constituents of sorghum lipids. Only a few
significant correlations were obtained among the physical and chemical properties,
indicating that these properties could not be predicted from other properties. The
principal component analysis showed that protein and lipid contents, and
endosperm texture largely contributed to grouping the accessions in PC1, and
grain colour and amylose content in PC2.
Three out of the six accessions evaluated for sensory analysis, namely
Ambajeettee, AL-70 and ETS 2752 were chosen for their desirable properties in
injera making. The chemical and physical properties of the selected accessions
were characterised by having high protein content, low tannins, intermediate
endosperm texture, and white and yellow seed colours. Red sorghum and white
sorghum with pigmented testa were found to be less desired. From the sensory
analysis study, it was observed that two of the accessions chosen, Ambajeettee
and ETS 2752 have shown a unique DNA profile and cluster group. Further
investigation may be necessary to validate the result by replicating the trial over
seasons and/or locations.
Chapter 6 Physicochemical & sensory analyses 116
6.1 Introduction
Grain sorghum is one of the major crops grown for human consumption in arid and
semi-arid regions of Africa and Asia. Asia, northern and Central America, and
Africa contribute most of the world production of sorghum. Nigeria, Mali, Niger,
Burkina Faso, Chad, Cameroon, Sudan, Ethiopia, Botswana, and Rwanda are the
major African countries in which sorghum production is of critical importance
(Subudhi et a/., 2002). According to Vavilov (1951) the Ethiopian area is
considered as the centre of its origin; and the contribution of Ethiopian origin
sorghums in world sorghum improvement is well recognised. For example,
extensive use of the zerazera sorghums of Ethiopian origin in the U.S. hybrid
sorghums has made major contributions to disease resistance, yield potential, and
quality (Rosenow and Dahlberg, 2000). High lysine was initially found in two
sorghum accessions from Ethiopia, IS 11167 and LS11758 (House et a/., 1995). In
Ethiopia as a whole, sorghum ranks fourth, next to maize (Zea mays L.), tef
[Eragrostis tef (Zucc.) Trotter] and wheat (Triticum spp.), in total production and
area under cultivation, and represents 13% of the total cereal production (CSA,
2000). In the eastern regions of Ethiopia, sorghum is the most important food crop
(accounting for 45% in total area and production of cereals). It is traditionally
cultivated by small-scale farmers, and is used either alone or in mixtures with other
grains for different types of food preparations.
/njera, fermented pancake-like bread from sorghum or tefflour is the staple diet in
Ethiopia and Eritrea. It is well known that injera quality depends mainly on cultivar
and fermentation process. From the point of view of the nutritive value of sorghum
grain, high tannin is the primary nutrient-limiting component (Dendy, 1995). A wide
variability among some Ethiopian sorghum cultivars on their performance for injera
making was reported (Wuhib and Tekabe, 1987; Gebrekidan and Gebrehiwot,
1982). However, a detailed understanding of the physical and chemical basis for
cultivar differences in sorghum injera quality is lacking. Furthermore, previous
workers (Murty and Kumar, 1995; Gebrekidan and Gebrehiwot, 1982) indicated
that more information is required on the type of sorghums suited for injera
preparation.
Chapter 6 Physicochemical & sensory analyses 117
For industrial use and various food products, many quality parameters have been
determined (Rooney et al., 1997). Information on nutritional and quality
characteristics of sorghum of the region, in terms of genetic control and the
influence of environmental factors, need to be studied. With enhanced
characterisation and improved end-use utilization, sorghum could maintain, or
increase, its contribution to the region's food security in particular, and world
agriculture in general. In most cases, since food quality varies by region and use,
quality criteria have been somewhat difficult to define and therefore to use
(Subudhi et al., 2002). Use of multiple-trait selection criteria may enhance
development of genotypes with a combination of desirable physical and chemical
kernel attributes to improve sorghum injera quality.
Exploration of the available genetic variation in landraces and improved cultivars
for chemical and physical grain attributes and their association with end-uses,
such as injera quality, would require the screening of germplasm for quality
evaluation before subsequent inclusion in breeding programs. The objectives of
the study in this chapter were (1) to assess variability among the accessions for
physical and chemical parameters, and (2) to understand relationships between
the different physical and chemical parameters and sorghum injera quality.
Chapter 6 Physicochemical & sensory analyses 118
6.2 Materials and methods
6.2.1 Selection of sorghum accessions
Thirteen of the sorghum accessions used for morpho-agronomical traits and
molecular markers, based on genetic diversity analyses, were chosen for the study
(Table 6.1). The selection was based on representing accessions from the
clustering result formed from DNA (AFLPs and SSRs) data. In addition to the
clustering result, consideration was also given to include accessions with variable
physical traits (namely, grain colour, 1000-grain weight and endosperm texture).
Data generated under sections 3.2.1.2 (on grain colour and endosperm texture)
and 3.2.2.3 (on 1OOO-kernelweight) were used.
Table 6.1 List of sorghum accessions included In the chemical composition
determination.
NQ Local / cultivar 1000 Kernel Grain colour Endosperm
name weight (g) texture
1 Wagare 29.0 Light brown 91
2 Fandisha 23.3 Yellow 5
3 Muyra 33.7 Red 7
4 Ambajeette 25.0 Yellow 5
5 Suuta naqaphu 24.3 White 7
6 Zangada 19.5 Dark brown 9
7 ETS 721 25.0 Light brown 9
8 Wotet begunche 20.3 Red 7
9 AL~70 30.0 White 5
10 ETS 2752 30.0 White 5
11 ETS 1005 31.0 Red 7
12 ETS 576 33.0 White 7
13 Long muyra 28.7 Red 9
1 Indicates endosperm texture (5 = intermediate; 7 = mostly starchy; and
9 = completely starchy).
Chapter 6 Physicochemical & sensory analyses 119
6.2.2 Methods
Kernels were cleaned manually and ground in a small Braun sample mill, and the
flour was stored at 4°C. Analysis was conducted in triplicate, and averaged results
were expressed on a dry matter basis.
6.2.3 Chemical composition
6.2.3.1 Moisture content
Moisture content was determined in a Memmert UL 80 drying oven, according to
the method described by Gomez et al. (1997). Pre-weighed samples (2 g) were
dried at 11O°C for 5 hr and re-weighed.
6.2.3.2 Protein content
Flour sample was weighed, oven-dried and protein content was determined by a
combustion method (Leco ®, model FP-528, St. Joseph, MI) in the Nutritional
Laboratory, Department of Animal, Wildlife and Grassland Sciences, University of
the Free State, South Africa.
6.2.3.3 Lipid extraction and methylation
Lipids were extracted by using chloroform: methanol (2: 1 v/v) with 0.001 % BHT
from 1 g samples of milled flour (Folch et al., 1957) in the Lipid Chemistry
Laboratory, Department of Microbial, Biochemical and Food Biotechnology,
University of the Free State.
Methylation. Fatty acid methyl esters were prepared using BF3 - Methanol (14%),
according to Slover and Lanaza (1979).
Chapter 6 Physicochemical & sensory analyses 120
6.2.3.4 Fatty acid analysis
Fatty acids were quantified by flame ionisation gas chromatography (Varian GX
3400), with aChrompack CPSIL 88 fused silica capillary column (100 m length,
0.25 pm ID, 0.2 pm film thickness). Identification of sample fatty acids was made
by comparing the relative retention times of fatty acid methyl ester peaks from
samples with those of standards obtained from SIGMA (cat. no. 189-19).
6.2.3.5 Starch content
The starch was hydrolysed enzymatically according to Horn et al. (1992). To
solubilize the starch, the procedure proposed by Thomas et al. (1996) was
followed. Accordingly, about 200 mg of the homogenized sample was weighed into
a 100 ml Erlenmeyer flask, and added 20 ml dimethyl sulphoxide (DMSO) and 5
ml hydrochloric acid (HCI, 8 mol/I). This was followed by the addition of 5 ml
sodium hydroxide (NaOH, 8 rnol/l), The Erlenmeyer flask was covered with
parafilm and the sample was incubated for 30 min at 60°C in a water bath. After
immediate cooling in a cold water bath, 500 pi of the sample (aliquot of the culture
supernatant) was taken and adjusted to a pH of 6.0 by adding 1.25 ml of 0.5 M
citrate-phosphate (pH 6.6) buffer plus 0.5ml of 0.5 M citrate buffer (pH 4.5), and 10
pi of a thermo-stable a-amylase was added and boiled for 10 min. Then 1 ml of
0.5 M citrate buffer (pH 4.5) was added, followed by 10 .uI of glucoamylase and
incubated for 15 min at 60°C. The mixture was then cooled in a cold water bath
and, the starch released (as glucose equivalent) was determined colorimetrically,
using 3, 5 - dinitrosalicylic acid (ONS).
A standard glucose curve was obtained by diluting a 1 mg/ml pure glucose
solution to 0.25, 0.5, 1.0, 1.25 and 1.5 mg glucose per ml. The standard curve
determined with the glucose standards is shown in Figure 6.1.
Chapter 6 Physicochemical & sensory analyses 121
Absorbance measurements were carried out as follows:
a) Standard glucose solution: One ml distilled H20 was pipetted into a blank test
tube, and into five other labelled test tubes, 1 ml of each glucose solution (0.25
to 1.5 mg/I). Then 1 ml ONS reagent and 2 ml water were added to each tube.
Absorbance of each solution was determined at 540 nm.
b) Hydrolysate: One ml of hydrolysate prepared was pipetted into a test tube and
2ml water and 1ml ONS reagent was added.
c) Standards and hydrolysates: To allow the reaction between glucose and ONS to
occur, all tubes were heated in a boiling water bath for 5 min. Then, the tubes
were cooled down, and each volume was adjusted to 20 ml with distilled water
and mixed well. The absorbance of each solution was then read at 540 nm. The
percentage total starch was calculated as follows:
Total starch (%) = .(6540/m)x 20 x 3.27 x 60 x 10 = (A540/O.06)x 39240
mass (mg)
where, A540= Absorbance at 540 nm, and m = slope.
0.6
0.5
E
c
0 0.4
'<t
on
CIÏ
IJ 0.3
c::
ra
..0...
0
I/) 0.2
.0
<t:
0.1
0
0 2 3 4 5 6 7 8
Glucose (pg/m)
Figure 6.1 Standard graph for glucose content determined by duplicate
data points.
Chapter 6 Physicochemical & sensory analyses 122
6.2.3.6 Amylose content
Amylose content of starch was determined by the method of Knutson and Grove
(1994). A standard curve was prepared with duplicate samples of amylose
concentrations ranging from 5 to 20 ,LIg/mi (dry weight basis). Absorbance was
measured at 600 nm and the concentrations were used to develop the amylose
standard curve (Figure 6.2). Finally, percentage amylose was calculated using a
formula,
Amylose% = A600 x 9.1 x 55 x 100 = A600 x 2429.61
20.60 x mass (mg) mass (mg)
where, A600 = Absorbance at 600 nm.
1-
1
E
I:
0 0.8
0
(0
Cl) 0.6
0
I:
ro
.0 0.4
"-
0
If)
.«0
0.2
0
0 5 10 15 20 25
Amylose (JIg/mi)
Figure 6.2 Standard amylose curve determined by duplicate data points.
6.2.3.7 Polyphenol / Tannin content
The polyphenol (condensed tannin) content was analysed for six accessions,
which were also included in the sorghum injera sensory evaluation. A modified
phenolic compounds determination method (Menkir et al., 1996) was used. A 250
mg flour sample was extracted in 15 ml of 0.5% Hel in methanol for 20 min. The
suspension was centrifuged for 5 min at 5000 rpm and the supernatant was
Chapter 6 Physicochemical & sensory analyses 123
carefully removed. A 1 ml aliquot was taken from the supernatant and mixed with
14 ml of 30% HCI in 1-butanol. A blank was prepared by mixing 0.5 ml of acidic
methanol extract with 7 ml of methanol, 0.1N acetic acid and butanol (15: 15:70,
v/v). The sample and the blank were vortexed and left to stand for 1 hr. Test tubes
containing the supernatant were then maintained in boiling water for 2 hr. After
cooling at room temperature and correcting for blanks, the concentrations of
tannins (proanthocyanidins) were measured at 550 nm. Results of the
concentrations were expressed as ug/g of dry sample.
6.2.4 Sensory evaluation of Injera
6.2.4.1 Sorghum samples
Six out of the 13 accessions (Table 6.1) studied for the chemical composition
analysis were selected and used for the evaluation. The selection was done
mainly on the basis of the cluster groups formed by using DNA marker based data.
In addition, their chemical components were considered together with grain colour
variability and endosperm texture. The accessions used include, Fandisha (A),
Ambajeette (B), ETS 1005 (C), ETS 576 (D), ETS 2752 (E) and AL-70 (F). The
English letter in parenthesis was used to represent the accession in the analysis
and discussion.
Samples were coded using three digit numbers picked from a table of random
numbers. Evaluations were performed at room temperature (25 -30°C) in
individual testing booths in the Sensory Evaluation Laboratory, Department of
Microbial, Biochemical and Food Biotechnology, University of the Free State.
Clear white lights were used to facilitate the evaluation of the colour.
Chapter 6 Physicochemical & sensory analyses 124
6.2.4.2 Methods of sensory evaluation
After cleaning, samples were milled in a falling number mill, 8 mm screen at
Bethlehem Grain Institute, Bethlehem, South Africa. Injera was made following the
steps described by Umeta and Faulks (1988) with some modifications. Injera
batter was prepared from the flour by mixing the necessary ingredients, and
fermented for 48 hr. The breads were baked in a heated clay griddle/ mitad one
day before testing and stored in glass containers, covered with cling film, to
prevent it from drying out. For evaluation, 60 x 25 mm samples were cut and
presented on white polystyrene trays.
Visual and taste quality characteristics of sorghum injera were evaluated using the
preference ranking test (Basker, 1988). Fifty-one panellists (25 - 49 years of age)
were recruited for the panel session from Ethiopian and Eritrean students (47
males; 4 females) presently enrolled at the University of the Free State, South
Africa. A partial view of the injera evaluation practice is shown in Appendix IX.
Three visual and one tactile characteristic were evaluated along with taste. The
visual characteristics included colour, eye quality (the presence, size and amount
of coagulated bubbles on the surface of the injera) and underside appearance.
Texture was evaluated inside the mouth as well as by manipulation by hand, since
the injera is often used as eating utensils to soak up a wat/ stew. Panellists
assigned ranks by using a scale of 1 to 6, with 1 = like/prefer the most and 6 =
like/prefer the least. No ties were allowed, i.e., no two samples were awarded the
same numerical value. Tap water at room temperature was provided for rinsing the
palate between samples during taste sessions.
The values for each sample were added to obtain a rank sum for each sample.
Differences in the preference rank sums between all possible pairs of samples
were calculated and considered according to Basker (1988).
Chapter 6 Physicochemical & sensory analyses 125
6.2.5 Statistical analyses
Univariate analyses
All analyses were performed in triplicate and data from individual chemical
component experiments were analysed by analysis of variance (ANOVA) using the
AeB procedure of Agrobase (Agrobase, 2000). The least significant difference
(LSD) test was used for mean separation where the F-test was found significant
for accessions. Pearson's correlation coefficients were calculated to determine
relationships between the physical, chemical and injera properties.
Multivariate analysis
Principal component analysis (peA) was computed from the correlation matrix
generated from the chemical and physical properties using an NeSS Program
(NeSS, 2000). The first and second pes were used to show the patterns of the
data.
Chapter 6 Physicochemical & sensory analyses 126
6.3 Results and discussion
6.3.1 Chemical composition
The average moisture content of the 13 sorghum samples included in the
study was 10.31±0.46%. The average values and LSD test for content of
protein, lipid, starch and amylose are presented in Table 6.2.
Protein content varied between 7.99 and 17.80% and was within the range
cited in the literature (Lasztity, 1996; Martin, 1984; Stemier et al., 1976).
The protein content was highest for AL-70 (17.8%), followed by Ambajeette
(15.88%). Wotet begunche, a high lysine content sorghum, was also high in
protein content. The lowest protein content was obtained in ETS 1005, ETS
576, and Long muyra; all three are of the Muyra sorghums, indicating the
probability of genetic influence. In his review Lasztity (1996) reported that
the protein content of sorghum varies from 6 to 25%. This wide variation in
protein content and composition was indicated to be due to genotype,
water availability, soil fertility, temperature, and environmental conditions
during grain development. Nevertheless, according to (Hulse et al., 1980)
genotypic effects on protein content were greater than environmental
effects .
.Among the accessions evaluated, the total starch content ranged between
52 and 85%. It can be seen that the starch content of ETS 2752, AL-70 and
Fandisha was the highest. Zangada and Long muyra had the lowest starch
content and can be classified as hetrowaxy. Previous workers (Buffo et al.,
1997; Klopfenstein & Hoseney, 1995; Lásztity, 1996; Wankhede et al.,
1989) have reported that starch makes up to 60 to 80% of normal (non-
waxy) sorghum kernels.
Amylose content was the highest for accessions ETS 2752, ETS 721,
Suuta naqaphu, AL-70, and ETS 576. In contrast, Zangada, Wotet
begunche and Long muyra had the lowest amylose content. The result
obtained in this study is within the range reported for sorghum by Serna-
Chapter 6 Physicochemical & sensory analyses 127
Saldivar and Rooney (1995). According to Ring et al. (1982), most of the
accessions in this study can be grouped as non-waxy (contain 23 to 30%
amylose) type sorghums, and some heterowaxy ones, but no waxy types
(those that contain up to 5% amylose). It was indicated in the literature
(Taylor et aI., 1997; Ring et aI., 1982) that environmental and genetic
factors determine amylose levels in sorghum.
Table 6.2 Average values and mean separation of the chemical
compositions for 13 sorghum accessions.
Local / cultivar
name Protein Lipid Starch Amylose
-------------------------------%-- -------------------------------------
Wagare 11.18ft 3.17cd 73.68e 20.71de
Fandisha 12.10de 2.52e 81.60ab 24.15c
Muyra 11.67def 3.22cd 77.51 bc 20.97d
Ambajeette 15.88b 3.00cd 71.9ge 19.63de
Suuta naqaphu 11.1i 3.22cd 65.83e 27.01ab
Zangada 12.19d 2.97d 51.88f 12.30f
ETS 721 11.46ef 3.34bc 71.51d 27.45ab
Wotet begunche 15.55bc 2.97d 74.59cd 14.55f
AL-70 17.80a 2.94d 82.73ab 26.65abc
ETS 2752 14.97c 3.09cd 85.01a 28.38a
ETS 1005 7.99h 3.66ab 82.22ab 25.04bc
ETS 576 10.409 3.72a 63.27e 26.17abc
Long muyra 10.379 3.66ab 53.44f 17.96e
Average 12.52 3.19 71.94 22.38
LSD (P= 0.01) 0.64 0.36 5.46 2.84
CV(%) 2.50 5.59 3.77 6.24
t Means for each trait factor followed by different superscript letters are
siqnlflcantly different at p< 0.01.
The lipid content ranged from 2.52 to 3.72%, with an average of 3.19%.
ETS 576 had the highest lipid content, but not significantly different from
Chapter 6 Physicochemical & sensory analyses 128
ETS 1005 and Long muyra. On the other hand, Fandisha was found to be
the lowest container of lipid. Results, which are similar (a lipid content that
ranged between 2.1 to 5.0%) to the results obtained in the present study,
were reported in previous studies (Hoseney, 1994; Beta et al., 1995; Yang
and Seib, 1995). Table 6.3 shows the composition of fatty acids of 10
sorghum accessions.
Table 6.3 Fatty acid compositions (%) of sorghum accessions.
Local/ cultivar name 16:0 18:0 18: 1 18:2 18: 3
Wagare 11.92eft 1.15f 29.23h 53.65a 2.50b
Fandisha 16.34a 1.41a 29.889 46.13f 4.52a
Muyra 12.17de 1.33bc ai.oz' 52.69b 1.37de
Ambajeette 12.18de 1.1gef 29.609h 53.6r 1.83c
Suuta naqaphu 11.68f 1.24de 34.35e 49.67c 1.48d
Zangada 13.62b 1.37ab 43.82a 36.28h 2.53b
ETS 721 12.25de 1.27cd 36.56c 47.12e 1.23ef
ETS 1005 12.45cd 1.44a 35.64d 47.83d 1.23ef
ETS 576 12.69c 1.41a 38.26b 44.799 1.32e
Long muyra 12.49cd 1.33bC 36.91c 46.4rf 1.1i
Average 12.78 1.32 34.53 47.83 1.92
LSD (p = 0.01) 0.41 0.07 0.60 0.65 0.14
CV% 1.55 2.64 0.83 0.65 3.56
t Means for each trait factor followed by different superscript letters are
significantly different at p< 0.01.
The major fatty acid was linoleic acid (18:2), followed by oleic acid (18: 1)
and palmitic acid (16:0). Serna-Saldivar and Rooney (1995) have indicated
that the fatty acid composition of sorghum oil is similar to that of maize and
is dominated by linoleic and oleic acids. Accession Zangada contained
oleic acid (18: 1) as its major fatty acid constituent. The accession with the
lowest lipid content, Fandisha, has an interesting fatty acid composition,
with the highest content of palmitic acid (16:0) and linolenic acid (18:3).
Linoleic, oleic, and palmitic acids were reported as the main constituents in
Chapter 6 Physicochemical & sensory analyses 129
three species of small millets (Sridhar and Lakshminarayana, 1994). The
average polyphenol (condensed tannins) content and mean separation for
the six sorghum samples is presented in Table 6.4. Among the samples,
Ambajeette (yellow seeded), ETS 2752 (white seeded) and AL-70 (white
seeded) contained lower amount of tannins, while Fandisha (yellow
seeded) showed a significantly higher tannin content than ETS 2752, but
similar to AL-70. ETS 1005 (red sorghum) scored the highest tannin
content, but statistically not different from ETS 576 (a white sorghum with
pigmented testa).
In general, the result from this study and Menkir et al. (1996) showed that
white sorghum accessions without a pigmented testa were characterised
by low tannin content.
Table 6.4 Average tannin content and mean separation for six sorghum
accessions.
Local / cultivar name Tannin (%)
Fandisha 1.61 bt
Ambajeette 1.55c
ETS 1005 1.83a
ETS 576 1.81 a
ETS 2752 1.52c
AL-70 1.58bc
Mean 1.65
LSD (p = 0.05) 0.14
CV% 0.02
t Means followed by different letters are significantly different (p< 0.05).
The correlation coefficients among the different properties are presented in
Table 6.5. There was a negative correlation coefficient between protein and
lipids, and protein and endosperm (soft) texture. House et al. (1995) found
a negative correlation between protein and lipid contents. A positive
Chapter 6 Physicochemical & sensory analyses 130
correlation was obtained between starch and endosperm (soft) texture,
lipids and 1000 seed weight (TSWt), soft endosperm texture (ET) and dark
grain colour (GCI). The other attributes were not significantly related to one
another; therefore, they could not be predicted from other properties. The
correlation obtained between protein and lipids in this study disagrees with
what House et al. (1995) found. In maize, lipid content was positively
correlated (r > 0.5) with starch (Dorsey-Redding et al., 1991).
Table 6.5 Correlation coefficients (n = 39) between chemical and
physical properties for 13 sorghum accessions.
Protein Lipid Starch Amyolse TSWt ET
Lipid -0.62*
Starch 0.32 -0.35
Amylose -0.27 0.37 -0.05
TSWt -0.25 0.56* 0.34 0.39
ET -0.58* 0.47 0.67* -0.01 -0.14
GCI -0.33 0.02 -0.39 -0.37 -0.37 0.76**
*, ** Significant at p = 0.05 and p = 0.01, respectively.
The PC analysis (Table 6.6) showed that the first three eigenvectors
explained 85% of the total variance apparent among the 13 accessions.
The first two PC axes accounted for 55% of the multiple variations among
the accessions (Figure 6.3), indicating a high degree of association among
the parameters studied. The first PC explained 30% of the gross variability,
and this was due mainly to variations in lipids and protein content, and
endosperm texture. Similarly, 27% of the overall variability of the
accessions comes from the second PC that originated mainly from
variations in grain colour and amylose content. Eigenvectors of the PC1
had large positive weights for protein, starch and amylopectin, large
negative weights for lipids, endosperm texture and grain colour, and a.
smaller, but positive weight for 1000 kernel weight (Table 6.6).
Chapter 6 Physicochemical & sensory analyses 131
The PC analysis grouped the accessions into groups over the four
quadrants (Figure 6.3). The different groups of accessions could be seen
as under the right bottom quadrant (Long muyra and ETS 576) with similar
protein, lipid, and amylose contents. The left bottom quadrant contained the
red and brown accessions (ETS 1005, Muyra, ETS 721 and Wagare) with
significant intra-cluster variability for protein, lipid and amylose contents.
The accessions Zangada and Wofef begunche occupied the left top
quadrant and both had similar lipid contents, but differed in their content of
protein, starch and amylose. The top right quadrant contained two closely
grouped yellow seeded accessions (Ambajeette and Fandisha) and three
white sorghums (Suuta naqaphu, ETS 2752, and AL-70). ETS 2752 and
AL-70 showed different protein contents; but they are statistically similar in
other physical and chemical properties.
Table 6.6. Principal component (PC) analysis of eight physicochemical
parameters in 13 sorghum accessions with eigenvectors, eigenvalues and
proportion of variations explained by the first three PC axes.
Eigenvectors
Parameter PC1 PC2 PC3
Protein 0.38 0.24 -0.35
Lipids -0.31 -0.51 0.11
Starch 0.45 -0.11 0.40
Amylose 0.01 -0.49 -0.35
Amylopectin 0.43 0.06 0.51
1000 kernel wt. 0.13 -0.56 0.32
Endosperm texture -0.48 0.06 0.26
Grain colour -0.35 0.35 0.40
Eigenvalues 2.44 1.98 2.39
. Individual% 30.52 24.74 29.83
Cumulative% 30.52 55.25 85.08
Chapter 6 Physicochemical & sensory analyses 132
2
1.5
0.5
No
c.. 0
-0.5
-1
-1.5
-2
-2 -1.5 -1 -0.5 0 0.5 1.5 2
PC1
Figure 6.3 PC plots of 13 sorghum accessions analysed using eight
physicochemical parameters.
6.3.2 Sensory evaluation of injera
Injera samples showing the different physical variations are shown in
Figure 6.4 and Figure 6.5. The evaluation values for each sample were
added to obtain a rank sum for each sample, and are presented from
Table 6.? through 6.11. Differences in the preference rank sums
between all possible pairs of samples were calculated and considered,
for example, between sample A and B (Table 6.?) it was 84, and
between sample C and D it was 40. If any of these (absolute) differences
exceeded a critical value, then the preferences for that pair of samples
differed from one another at the stated statistical significance level.
Basker (1988) compiled a set of tables with critical values of differences
among rank sums for multiple comparisons. From the significance
tables, a significance level of p = 0.05 is attained when the rank sum
differences are greater than or equal to 53.8 (for 51 panellists and 6
products), and a significance level of p = 0.01 is attained when the rank
sum differences are greater than or equal to 63.6.
Chapter 6 Physicochemical & sensory analyses 133
Table 6.7 Rank sums and significance tests for injera colour.
Sample A B C 0 E F
Rank sum 203 119 247 287 105 110
Difference vs A 84 44 84 98 93
B 128 168 14 9
C 40 142 137
0 182 177
E 5
Significance level p = 0.05 P = 0.01
Critical difference 53.8 63.6
Sample E a a
F ap ap
B ap ap
A p P
C V V
D Ó V
In the table above, the evaluation of the colour of the sorghum injera, the
results are arranged with the products in decreasing order of preference,
that is, in increasing order of rank sums. Sample E (ETS 2752) is put
first, because it had the lowest rank sum and was preferred "the most".
It is followed by sample F (AL-70), which had the second lowest rank
sum, then by B (Ambajeette), A (Fandisha), C (ETS 1005) and finally by
sample D (ETS 576), which had the highest rank sum and was preferred
"the least". Lowercase Greek letters were used to indicate samples
. whose rank sums did not differ significantly. From this result it is clear
that at both significance levels, sample E (ETS 2752) was significantly
preferred over samples A, C and D, but not over samples F and B.
It can be seen from Table 6.8 that samples B (Ambajeette) and E (ETS
2752) had the same rank sums for 'eye' quality. At p = 0.05 samples B
(Ambajeette), and E (ETS 2752) were significantly preferred over
samples A (Fandisha), D (ETS 576) and C (ETS 1005), but not over
sample F (AL-70). However, at p =0.01, samples Band E were
significantly preferred only over samples D and C in regard to 'eye'
quality, but not over samples F and A.
Chapter 6 Physicochemical & sensory analyses 134
Figure 6.4 Injera / bidena prepared from two sorghum accessions, Ambajeette
(A) and ETS 1005 (B) showing the colour variation and the distribution of the
'eyes.'
Table 6.8 The rank sums and differences between products along with the
significance levels for 'eye' quality of injera.
Sample A B C D E F
Rank sum 192 138 220 215 138 168
Difference vs A 54 28 23 54 24
B 82 77 0 30
C 5 82 52
D 77 47
E 30
Significance level p = 0.05 P = 0.01
Critical difference 53.8 63.6
Sample B a a
E a a
F a~ a~
A ~ a~
D Y ~
C YfJ ~
For underside appearance (Table 6.9), sample E (ETS 2752) was
significantly preferred over samples A (Fandisha), C (ETS 1005) and D
(ETS 576) at both significant levels, but not over samples F (AL-70) and
B (Ambajeette).
Chapter 6 Physicochemical & sensory analyses 135
Table 6.9 Summarised results for underside appearance of injera from six
sorghum samples.
Sample A B C D E F
Rank sum 203 160 216 249 119 124
Difference vs A 43 13 46 84 79
B 56 89 41 36
C 33 97 92
D 130 125
E 5
Significance level p = 0.05 p = 0.01
Critical difference 53.8 63.6
Sample E a a
F ap ap
B ap ap
A /3 /3
C /3y Py
D V V
When evaluating texture (Table 6.10), samples E (ETS 2752) and F (AL-
70) scored the same rank sum totals, as can be seen from Table 6.10.
Samples E and F were significantly preferred over samples C (ETS
1005), A (Fandisha) and 0 (ETS 576) at p = 0.05, but not over sample B
(Ambajeette). However, at p = 0.01, samples E (ETS 2752) and F (AL-
70) were significantly preferred over only samples A (Fandisha) and 0
(ETS 576).
Table 6.10 Summarised rank sums and significance tests for texture of injera.
Sample A B C 0 E F
Rank sum 196 158 188 265 129 129
Difference vs A 38 8 69 67 67
B 30 107 29 29
C 77 59 59
0 36 136
E 0
Significance level p = 0.05 P = 0.01
Critical difference 53.8 63.6
Sample E a a
F a a
B ap ap
C /3 ap
A Py /3
0 V V
Chapter 6 Physicochemical & sensory analyses 136
Table 6.11 Rank sums and significance tests for injera taste results.
Samples A B C D E F
Rank sum 195 150 222 237 131 133
Difference vs A 45 27 42 64 62
B 72 87 19 17
C 15 91 89
D 106 104
E 2
Significance level p = 0.05 P = 0.01
Critical difference 53.8 63.6
Sample E a a
F a~ a~
B a~ a~
A ~ ~
C Y Y
D Yl!> Yl!>
The final characteristic evaluated was taste and the results are
presented in Table 16.11. There was a significant statistical difference
among accessions. ETS 2752 (E) was desired over Fandisha (A), ETS
1005 (C) and ETS 576 (0) at both significant levels, but not over
accessions AL-70 (F) and Ambajeette (B).
A B C D E F
Figure 6.5 The six sorghum Injera I bidena samples evaluated by the
panellists. (A = Fandisha, B = Ambajeette, C = ETS 1005, D = ETS 576, E =
ETS 2752 and F = AL-70)
Chapter 6 Physicochemical & sensory analyses 137
From the result, it can be said that Ambajeette, ETS 2752 and AL-70
appear to be preferred for making injera, since they scored the lowest
rankings in all the tests. Fandisha was less preferred than the previous
three, but more liked than ETS 1005 and ETS 576, which were found as
least desired for injera making.
6.3.3 Relationships between physicochemical properties and injera quality
The physical properties included in the quality study were 1000 kernel
weight, grain colour and endosperm texture. These traits were variable
over the accessions and their relationships with chemical components
(Table 6.5) and injera quality shown in Table 6.12.
Protein content positively and significantly (r :;: 0.94) correlated with injera
quality. Sorghum injera quality was negatively correlated with tannin
content (r = -0.97) and soft or floury textured endosperm (r = -0.92). In a
previous study, it was reported that porridges made from flours of corneous
sorghums were less sticky than those made from floury sorghums
(Cagampang et al., 1982). Stickiness of cooked sorghum flour was a
function of starch gelatinisation (Hoseney, 1986). Starch in the corneous
endosperm portion of sorghum grain is embedded in protein, which has
been shown to influence starch gelatinisation in sorghum (Chandrashekar
and Kirleis, 1988).
The three accessions that performed the best in the injera quality
evaluation were all intermediate (heterowaxy) types. In relation to amylose
content, high amylose sorghum products dry and become hard upon
cooling. In contrast, the low amylose types are moist and sticky when
cooled under optimum conditions. The intermediate types cook moist and
tender and do not become hard upon cooling (Ring et al., 1982).
Chapter 6 Physicochemical & sensory analyses 138
1.2
1 I [] Tannin content (%)
Cl) • Injera quality score (Scale)
ai
u 0.8 t-- C Endosperm texture (Scale)
Cf)
C Grain clour (Scale)
Cl)
> 0.6 r- - -
+=' .1000 kernel weight (g)
nl
ai
0::: 0.4 r- r- r-
0.2 r- r- r-
0
A B C 0 E F
Sorghum samples
Figure 6.6 Bar graph showing the relationship between tannin content,
injera quality, endosperm texture, grain colour and 1000-kemel weight in six
sorghum samples. (A = Fandisha, B = Ambajeette, C = ETS 1005, D = ETS
576, E = ETS 2752 and F = AL-70)
Table 6.12 Correlation coefficients between injera quality and some
physico-chemical parameters in sorghum.
Parameters Correlation coefficient
Protein 0.94**
Lipid -0.67
Starch 0.16
Amylose -0.11
Tannin -0.97**
1000 kernel weight -0.42
Endosperm texture -0.92**
Grain colour -0.41
** Significant at p = 0.01 probability
Chapter 6 Physicochemical & sensory analyses 139
6.4 Conclusions
Variability in physical and chemical composition was observed among
sorghum accessions sampled. While the flours from a" six accessions
could be made into injera, the differences in chemical compositions and
physical properties of the kernels resulted in differences in its quality. Of
these, lower tannin and high protein contents, white or yellow seed colour,
and intermediate endosperm texture were correlated highly with the quality
of injera. Thus, future selection for best injera quality can combine high
protein content, medium to high amylose content, intermediate endosperm
texture, low tannin content attributes as effective screening criteria.
It is interesting to note that accessions 12 (Ambajeette), 40 (AL-70) and 41
(ETS 2752) had desirable injera-making characteristics. Data from morpho-
agronomical traits grouped these accessions together. However, according
to molecular data, these accessions were genetically distinct and were
clustered in different genetic groups. This indicates that a good basis of
genetic variability exists for the improvement of the sorghum cultivars using
these accessions.
Chapter 6 Physicochemical & sensory analyses 140
REFERENCES
Agrobase, 2000. Agronomix Software Inc., 71 Waterloo St. Winnipeg,
Manitoba R3NOS4, Canada.
Basker, D. 1988. Critical values of differences among rank sums for
muitpie comparisons. Food Technology 42: 80-83.
Beta, T., Rooney, L.W. and Waniska, R.D. 1995. Malting characteristics
of sorghum cultivars. Cereal Chemo 72:533-538.
Buffo, R.A., Weller, C.L. and Parkhurst, A.M. 1997. Optimisation of sulfur
dioxide and lactic acid steeping concentrations for milling of grain
sorghum. Transactions of the American Soc. of Agric. Engineers
40: 1643-1648.
Cagampang, G.B., Griffith, J.E. and Kirleis, A.W. 1982. Modified
adhesion test for measuring stickiness of sorghum porridges. Cereal
Chemo 59:234-3-235.
Central Statistical Authority (CSA), 2000. The Federal Democratic
Republic of Ethiopia: Agricultural sample survey report on area and
production for major crops, Statistical Bulletin No 227. Addis Ababa,
Ethiopia, pp 16-68.
Chandrashekar, A. and Kirleis, A.W. 1988. Influence of protein on starch
gelatinisation in sorghum. Cereal Chemo 65:457-462.
Dendy, D.A.V. 1995. Sorghum and Millets: Chemistry and Technology,
American Association of Cereal Chemists, Inc. St. Paul, Minnesota,
USA.
Dorsey-Redding, C., Hurburgh, C.R.Jr., Johnson, L.A. and Fox, S.R.
1991. Relationships among maize quality factors. Cereal Chemo 68:
602-605.
Folch, J., lees, M. and Sloane-Stanley, G.H. 1957. A simple method for
the isolation and purification of total lipids from animal tissue. J.
Biological Chemo 226: 497-509.
Chapter 6 Physicochemical & sensory analyses 141
Gebrekidan, B., and Gebrehiwot, B. 1982. Sorghum injera preparations
and quality parameters. In: Rooney, L.W. and Murty, D.S. (Eds.).
Proc. Int. symp. on sorghum grain quality. ICRISAT, Patancheru,
AP, India, pp. 55-66.
Gomez, M.I., Obilana, A.B., Martin, D.F., Madzvamuse, M. and Monyo,
E.S. 1997. Quality evaluation of sorghum and pearl millet. ICRISAT,
Patancheru, India.
Hom, C.H., du Preez, J.C. and Kilian, S.G. 1992. Fermentation of grain
sorghum starch by co-cultivation of Schwanniomyces occidentalis
and Saccharomyces cerevisiae. Bioresource Technology 42:27-31.
Hoseney, R.C. 1986. Principles of Cereal Science and Technology. In:
American Assoc. of Cereal Chemists, St. Paul, MN, USA, pp. 47-54.
Hoseney, R.C. 1994. Principles of Cereal Science and Technology
(Second edition). In: American Assoc. of Cereal Chemists, St. Paul,
MN, USA, pp.82-98.
House, L.R., Osmanzai, M., Gomez, M.I., Monyo, E.S. and Gupta, S.C.
1995. Agronomie principles. In: Dendy, D.A.v. (Ed.). Sorghum and
millets: Chemistry and technology. American Association of Cereal
Chemists, St. paul, MN, USA, pp. 27-67.
Hulse, J.H., Laing, E.M. and Pearson, O.E. 1980. Sorghum and the
millets. Their composition and nutritive value. Academic Press, NY.
Klopfenstein, C.F. and R.C. Hoseney. 1995. Nutritional Properties of
Sorghum and Millets. In: Dendy, D.A.v. (Ed.). Sorghum and Millets:
Chemistry and Technology. American Association of Cereal
Chemists, St. Paul, MN, USA, pp.125-184.
Knutson, C.A. and Grove, M.J. 1994. Rapid method for estimation of
amylose in maize starches. Cereal Chemo 71 :469-471.
Lásztity, R, 1996. The chemistry of cereal proteins. Bocaraton, Fla.:CRC
Press.
Martin, F.W. 1984. Handbook of Tropical Crops. CRC Press, Boca Ratan,
FL.
Chapter 6 Physicochemical & sensory analyses 142
Menkir, A., Ejeta, G., Butler, L. and Melakeberhan, A. 1996. Physical
and chemical kernel properties associated with resistance to grain
mold in sorghum. Cereal Chemo 73:613-617.
Murty, D.S. and Kumar, K.A. 1995. Traditional uses of sorghum and
millets. In: Dendy, D.A.V. (Ed.). Sorghum and millets: Chemistry and
technology. American Association of Cereal Chemists, Inc., St. paul,
MN, USA, pp. 185-223.
Ness, 2000. Number Cruncher Statistical Systems, Dr. Jerry L. Hintze,
329 North 1000 East, Kaysville, Utah 84037, Canada.
Ring, S.H., Akingbala, J.O. and Rooney, L.W. 1982. Variation in amylose
content among sorghums. In: Rooney, L.W. and Murty, D.S. (Eds.).
International Symposium on Sorghum Grain Quality. ICRISAT,
Patencheru, India, pp.269-279.
Rooney, L.W., Waniska, R.D. and Subramanian, R. 1997. Overcoming
constraints to utilization of sorghum and millets. In Proceedings of
the International Conference on Genetic Improvement of Sorghum
and Pearl Millet, Sep. 23-27, 1996 (pp. 549-557). Lubbock, TX,
INTSORMIL Pub!. No. 97-5.
Rosenow, D.T. and Dahlberg, J.A. 2000. Collection, conversion, and
utilization of sorghum. In: Smith, C.W. (Ed.). Sorghum: Origin,
history, technology, and production. John Wiley & Sons, Inc. pp.
309-328.
Serna-Saldivar, and Rooney, L.W. 1995. Structure and chemistry of
sorghum and millets. In: Dendy, D.A.V. (Ed.). Sorghum and millets
chemistry and technology. American Association of Cereal
Chemists, St. Paul, MN, USA, pp. 69-124.
Slover, H.T. and Lanza, E. 1979. Quantitative analysis of food fatty acids
by capillary gas chromatography. Journal of the American Oil
chemists Society. 56: 933-943.
Sridhar, R. and Lakshminarayana, G. 1994. Contents of total lipids and
lipid classes and composition of fatty acids in small millets: Foxtail
Chapter 6 Physicochemical & sensory analyses 143
(Setaria ita/ica), Proso (Panicum mi/iaceum), and Finger (E/eusine
coracana). Cereal Chemo 71 :355-359.
Stemier, A.B.L., Collins, F.I., De Wet, J.M.J. and Harlan, J.R. 1976.
Variation in the levels of lipid components and protein in
ecogeographic races of sorghum bicolour. Biochem. Syst. Eco!'
4:43-45.
Subudhi, P.K., Nguyen, H.T., Gilbert, M.L. and Rosenow, D.T. 2002.
Sorghum improvement: Past achievements and future prospects. In:
Kang, M.S. (Ed.). Crop improvement: Challenges in the Twenty-First
Century. The Haworth Press, Binghamton, NY, pp. 109-159.
Taylor, J.R.N., Dewar, J., Taylor, J. and von Ascheraden, R.F. 1997.
Factors affecting the porrige-making quality of South African
Sorghums. J. Sci. Food Agric. 73:464-470.
Thomas, K.C., Hynes, S.H. and Ingledew, W.M. 1996. Practical and
theoretical considerations in the production of high concentrations of
alcohol by fermentation. Process Biochem. 31 :321-331.
Umeta, M. and Faulks, R.M. 1988. The effect of fermentation on the
carbohydrates in tef (Eragrostis tef). Food Chemistry 27:181-189.
Vavilov, N.1. 1951. The origin, variation, immunity and breeding of
cultivated plants. Chronica Botanica 13:1-366.
Wankhede, O.M., Deshpahde, H.W., Gunjal, B.B., Bhosale, M.M., PaW,
H.B., Gahilod, A.T., Sawate, A.R. and Walde, S.G. 1989. Studies
on physiochemical pasting characteristics and amyolytic
susceptibility of starch from sorghum (S. bicoior (L.) Moench) grains.
Starch/Staerke 41 :123-127.
Wuhib, E. and Tekabe, F. 1987. Sorghum Utilization Project Final Report.
Addis Ababa: EN!. (Proj. No. 3-P-80-0010)
Yang, P. and Seib, P.A. 1995. Low-input wet-milling of grain sorghum for
readily accessible starch and animal feed. Cereal Chemo 72:498-
503.
144
CHAPTER 7
GENERAL CONCLUSIONS
Molecular and morpho-agronomical marker techniques each have distinct
advantages for assessing genetic relationships. Studies that combine the two
approaches can thereby maximize both information content and usefulness. In
this study, combinations of molecular and morpho-agronomical markers, and
evaluation for chemical composition and food quality were undertaken to
provide a comprehensive view of the genetic variation among sorghum
accessions from eastern highlands of Ethiopia.
A total of 45 sorghum accessions were sampled and analysed for molecular
and morpho-agronomical markers. The morpho-agronomic data was obtained
by measuring 10 qualitative and 16 quantitative traits. Eight selective AFLP
and 10 sorghum microsatellite primer pairs were used for DNA amplification
generating a total of 651 and 48 bands, respectively. And also, a chemical
composition determination and sensory analysis were conducted on 13 and
six sorghum samples, respectively.
The phenotypic, chemical and molecular diversity estimates showed a high
level of variation among accessions, and indicated that sorghum accession
populations studied are a mixture of a large number of distinct genotypes.
Based on qualitative data, the estimates of H', individually and pooled over
characters and localities was highest for kurffa challe, AU and Chiro. The
lowest H' were from Doba, Habro and Metta. The average GO estimates from
the individual markers and combined data produced comparable results.
Hence, it is possible to make predictions about the level of variation for one
technique based on the level of the other.
Chapter 7 General conclusions 145
From the cluster and / principal component analyses of the three sets of data
and the combined data matrix, it was observed that though some regions are
clustering together, perfect matching of the dendrograms or the plots is
lacking, which could be due to many reasons, such as the nature of the
genetic basis of variation for agro-morphological, AFLP and mocrosatellite
markers, or may be related to the number of polymorph isms detected with
each marker technique rather than a function of which technique is employed.
Furthermore, the grouping of the accessions into different clusters using the
three different data sets and the combined data was not perfectly related to
either their sites of collection or the same naming, suggesting the existence
variability within the region of collection and same name. Overall, the results
from these studies showed that it is possible to both classify the genetic
diversity of the sorghum accessions for the highest genetic diversity using
AFLP and microsatellite markers. However, AFLP based cluster analysis has
more distinctly differentiated among the accessions than both morpho-
agronomical and microsatellite based clusters. The difference between the
levels of AFLPs and microsatellites in detecting distinctness could be related
to the number of polymorphic bands/ loci analysed.
A significant variability was obtained among the sorghum samples analysed
for chemical components and sensory evaluation, and this shows the potential
to improve the traits through breeding. Protein and tannin contents influenced
the injera-making quality significantly. Three accessions were preferred for
injera-making, and two of these have shown a unique DNA profile in this
study.
146
CHAPTER 8
SUMMARY
Sorghum is the most important cereal crop providing food to millions of people
in the world. It is well known for its adaptation to harsh environments,
specifically to drought and heat stresses, which accounts for its success
throughout the semi-arid regions of the world. Africa, specifically the northeast
quadrant of Africa is believed to be the primary centre of origin and
domestication of the crop. In these parts of Africa, genetic variability is
available both in cultivated races and the wild progenitors. In regard to
sorghum utilization in general, developing countries use it primarily as food,
whereas developed countries use it as feed. A wide variety of traditional foods
are used from sorghum in the semi-arid tropics.
Despite its importance, the genetic characterization of sorghum is very limited.
The accurate estimation of genetic diversity of the species is important for
conservation of valuable resources and possible future use in its improvement.
Farmers' varieties or landraces (locally adapted populations bred through
traditional methods of direct selection) are usually the major sources of genetic
variation. Cultivated sorghums in Ethiopia show diverse morpho-agronomic
diversity, and have not been studied using the recently developed molecular
markers. The aim of this study was to estimate genetic diversity by using DNA
markers, morpho-agronomical traits and food quality attributes in sorghum
accessions.
Forty-five accessions, including landrace collections, breeding materials and
improved cultivars from the eastern highlands of Ethiopia were used. A total of
552 and 43 polymorphic AFLP and microsatellite alleles, respectively were
scored and used to calculate pair-wise genetic distances and clustering. In
addition, 10 qualitative and 16 quantitative traits with 96 variants were scored
147
and used to analyse the genetic distances and clustering. The physical and
chemical composition and food (injera)-making qualities of selected sorghum
samples were also investigated. A high phenotypic, chemical and genetic
variability among the accessions was observed.
The resulting knowledge of genetic distance and discrimination of sorghum
accessions, chemical composition variability and injera-making quality in this
study will contribute towards sorghum improvement programmes in Ethiopia,
and conservation of novel genotypes. It permits an organization of germplasm
resources and identification of parents for crossing blocks. This will enable the
breeder/ improvement scientist to make more scientific based choices. These
findings have shown that both AFLP and microsatellite techniques can be
successfully used and that they are informative in estimation of genetic
diversity and identification of sorghum accessions. The result from morpho-
agronomical traits analysis generally agreed with the molecular marker results
in estimating diversity, hence it can be used in the management of sorghum
genetic resources. A further extensive investigation of Ethiopian sorghum
genetic diversity including wider areas and more samples is recommended.
148
OPSOMMING
Sorghum is die mees belangrike graan gewas wat voedsel verskaf aan
miljoene mense in die wêreld. Dit is bekend vir goeie aanpassing in marginale
omgewings, onder veral hitte en droogte stremming, wat die sukses van die
gewas verklaar dwarsoor semi-ariede areas van die wêreld. Afrika, spesifiek
die noordoostelike kwadrant van Afrika, word beskou as die primêre sentrum
van oorsprong en die plek van eerste verbouing van die gewas. In hierdie dele
van Afrika is genetiese variabiliteit beskikbaar vir beide verboude rasse sowel
as wilde verwantes. Vir sorghum gebruik in die algemeen, word dit in
ontwikkelende lande gebruik primêr as voedsel, en in die ontwikkelde lande as
voer. 'n Wye verskeidenheid tradisionele kos word gemaak van sorghum in die
semi-ariede trope.
Ten spyte van die belangrikheid daarvan, is die genetiese karakterisering van
sorghum baie beperk. Die akkurate vasstelling van genetiese diversiteit van
die spesie is belangrik vir bewaring van waardevolle bronne en moontlike
toekomstige gebruik vir verbetering van die gewas. Boere variëteite of
landrasse (plaaslik aangepaste populasies wat geteel is deur tradisionele
metodes van direkte seleksie) is gewoonlik die hoof bron van genetiese
variasie. Verboude sorghums in Etiopië wys diverse morfo-agronomiese
diversiteit, en is nog nie bestudeer met onlangs ontwikkelde molekulêre
merkers nie. Die doel van hierdie studie was om genetiese diversiteit te bepaal
met DNA merkers, morfo-agronomiese eienskappe en voedsel kwaliteit
eienskappe in sorghum inskrywings.
Vyf en veertig inskrywings, insluitend landras versamelings, teelmateriaal en
vebeterde cultivars van die oostelike hooglande van Etiopië is gebruik. 'n
Totaal van 552 en 43 polimorfiese AFLP en mikrosatelliet allele,
onderskeidelik, is geëvalueer en gebruik om paarsgewyse genetiese afstande
en groepering te bepaal. Verder is 10 kwalitatiewe en 16 kwantitatiewe
149
eienskappe van 96 variante geëvalueer om genetiese afstande en
groeperings te bepaal. Die fisiese en chemiese samestelling en voedsel
(/njera) kwaliteit van geselekteerde sorghum monsters is ook bepaal. 'n Hoë
fenotipiese, chemiese en genetiese variabiliteit is tussen inskrywings gesien.
Die data gegenereer in terme van genetiese afstande en diskriminasie van
sorghum inskrywings, chemiese samestelling variabiliteit en injere-
bakkwaliteit in hierdie studie sal bydra tot sorghum verbeterings programme in
Etiopië, sowel as die bewaring van unieke genotipes. Dit dra by tot die
organisasie van kiemplasma bronne en die identifisering van ouers vir
kruisings blokke. Dit sal die teler meer wetenskaplike keuses toelaat. Hierdie
bevindings het getoon dat beide AFLP en mikrosatelliet tegnieke suksesvol
gebruik kan word en dat hulle bruikbaar is vir die bepaling van genetiese
diversiteit en identifiksaie van sorghum inskrywings. Die resultate van die
morfo-agronomiese eienskap analise het in die algemeen ooreengestem met
molekulêre merkers in die bepaling van diversiteit, dus kan dit gebruik word in
die bestuur van sorghum genetiese bronne. 'n Verdere uitgebreide ondersoek
na Etiopiese sorghum genetiese diversiteit, insluitend wyer areas en meer
inskrwyings, word aanbeveel.
150
Appendix I AFLP fragment scores for primer combination Eco/+ACA /
Mse/+CAA
Fragment size (bp)
ACC. 60 62 64 69 71 77 80 82 86 87 89 93 95 96 100 102 105 106 108
1 1 1
2 0 0 0 00 0 0
3 0
0
4 0 0
0
5 00 0 0
6 0 11 0 0
7 0 01 1 1 1 1 0 0 0
8 0 10 0 0 0 0 1 1
9 0 00
10 0 00 1
11 0 10
12 0 1 01
13 1 01 11 1 1
14 0 1 00 1 1 1 0 0 1 0
15 1 1 0 00 0 0 1 0 0 1
16 1 0 00 0 1 0
17 0 0 00 1 0 0
18 01 0 00 1 1 0 1 1 0
19 10 0 00 1 0 0 1 0 1 0
20 0 1 11 0 0 1 0 1 1 0 1
21 1 0 00 1 1 1 0 0 0
22 0 0 00 0 0 0 1 1
23 0 0 00 1 0 1 1 0
24 0 11 1 1 0 1 1 0
25 0 00 0 0 0 0
26 0 10 0 0
27 0 00 0 0 1
28 01 00 0 1 1 0 1 1 1
29 1 0 00 0 0 0 0 0 1 0 0
30 1 0 01 0 0 0 1 0 1 1
31 00 00 0 1 1 0 0 0
32 0 0 00 0 0 0
33 0 00 0 0 0 1
34 0 01 0 0 1 0 0 0
35 0 11 0 0 1 1 0 1 1
36 0 10 0 0 0 0 0 0 0
37 0 00 0 0 0 1 1 0
38 01 1 1 0 0
39 01 00 0 0 1 0 0
40 0 00 0 0 0 0 0
41 00 0 1 1 0 1 1
42 00 0 0 1 0 0
43 0 00 0 0 0 1
44 0 0 10 0 0
45 00 0
0
151
Appendix I cent.
Fragment size (bp)
ACC. 113 117 119 123 125 127 129 133 135 137 140 143 145 147 150 153 155 157 160
1 o o o o 1 o 1
2 o o o o o 1o 1o 11 1 1 1 o 1 1
3 o o o o 1 o o o 1 o o 1
4 o o o o o o o o 1 1 o
5 o o o o o 1 1 o o 1 o
6 1 o o o o 1 o o o o
7 o o o o o o o o 1 1
8 1 o 1 o o o o 1 o o
9 o o o o 1 o o o 1 1
10 1 1 o o 1 o 1 1
11 o o 1 1 1 o 1 1 1 o
12 1 o o o o o o o o o 1
13 o 1 1 1 o 1 1 1 1 o 1
14 o o o o o o o o o o
15 1 0 1 o o o
16 o 0 o 1 o 1 o 1 1 1
17 o 0 o 0 o 0 .1 o o 0 o
18 1 1 o 1 1 1 1 o o
19 0 o 1 001 o 0 o o o
20 o 0 100 o o o 1 o
21 o 0 o 0 o 0 1 1 1
22 o 1 o 0 1 1 1 o 0 010
23 1 1 o 0 o 0 o 1 o o 1
24 o 0 o 0 o 0 o 1 1 0 o 0
25 o 0 1 1 o 1 001 1
26 o 0 o 0 o 0 000 o
27 o 0 o 1 o 0 000 1 0
28 o 0 o 0 o 0 010 1 0 0
29 1 0 000 o 0 000 o 1 1
30 o 0 o 0 o 0 1 000 o 0
31 o 0 o 0 1 1 o 100 o 1
32 o 0 o 0 o o 0 1 0
33 1 0 o 1 1 1 010 o 0
34 o 1 0 o 0 1 101 o
35 o o 1 1 o 0 1 010 1 0
36 1 0 000 1 0 1 o 101 o 1
37 o 010 1 1 0 1 o 0 1 0
38 1 100 o 0 1 1 1 0 1 0
39 1 o 1 o o 100 o
40 o 1 1 0 1 o 0
41 o 1 o o 0
42 1 0 o 1 1 0 o 1 0 1
43 o 0 o o o 1 0
44 1 1 o o
45 o o
152
Appendix I cant.
Fragment size (bp)
ACC. 161 163 167 169 173 175 177 181 186 19a 192 194 195 198 zoo 2a2 2a4 2a6 2a9 213
1 1 a a 1 a
a a a a a a 1a 1 a a a 12 1 a a a 1 1 a o 1 a
3 1 a a 1 a 1 a a a a a a 1 a
4 a a a 1 a 1 a a 1 a a a a 1
5 a a 1 a a a a a a 1 a a a
6 a a a a a a 1 a a a a a 1 a
7 a 1 a a a a a a a a 1 a 1
8 a a a 1 a a a a 1 a 1 a a 1 a
9 1 a a a a a a a a a a a
1a a a a a a 1 a a a 1 1 1
11 1 1 1 1 1 a a a 1 a a 1 a 1 a a 1
12 a a a a a a a a a a a a a a a a
13 1 a 1 a 1 a a 1 a a 1 a
14 a a 1 a a a 1 a a a 1 a 1 a a 1
15 1 a a 1 a a a a a 1 a a a a 1
16 1 a a 1 1 1 a a a a a a a
17 a a a a 1 a 1 a a a 1 a a a
18 a a 1 a 1 a a a a a 1 a 1
19 a 1 a a 1 a 1 a 1 a a a 1 a a 1 a 1
2a a a 1 1 1 a a a a a 1 a a a a
21 a 1 a a 1 a a a a a 1 a a 1 a 1 1
22 1 a a a a a a a a a a a a 1 a a a
23 a 1 1 a a 1 a a a a a a a 1 a
24 1 a a a 1 a a a a a a a a 1 a
25 a 1 1 a 1 1 a a a a
26 1 a a a 1 a a 1 1 a a a a
27 a a a a 1 a a a a a a o a a
28 1 a a 1 a a a 1 a a a 1 a a 1 1
29 a 1 1 a a a a 1 1 1 a 1 a a a
so a 1 a 1 a 1 a 1 a a o a a a
31 a a a a 1 a a a a a 1 a 1 1 a a 1
32 1 a a a a 1 a a a a 1 a
33 a 1 a a 1 a a a a a a
34 1 a 1 1 a 1 a a 1 1 1 a a a
35 1 a a a 1 1 1 a a 1 a a a 0 a 1 a
36 a a a 1 a a a a a a 1 a 1 a a
37 1 a a a 1 a a a 1 a a a
38 a a a 1 1 a 1 a a a a 1
39 a a a a a a a o 1 a a 1 a a a
4a a a a 1 1 1 a 1 a
41 a a a 1 1 1 a a o 1 a
42 a a a a a 1 1 a 1 a a 1 a
43 a a a 1 a a o o a a 1
44 a a a o a a
45 a o a
153
Appendix I cant.
Fragment size (bp)
ACC. 217 219 221 224 228 230 232 236 238 241 247 251 254 258 264 266 270 272 276
1 0 o o o o
2 0 o oo oo o 1o oo o o1 o o o o 1
3 0 o o o 1 o o 1 o o o 1 o o
4 0 o o 1 o o o o 1 o o o 1 1
5 0 o o o 1 o o 1 o o o o o
6 0 1 o o 1 1 o o o 1 1 o o
7 0 o 1 o 1 1 o 1 1 o o 1 o o
8 1 o 1 o o o o 1 o o 1 o o o o o o
9 0 o o o o 1 1 o o o o 1 1 o
10 1 1 1 o 1 1 o o 1 1 1 o 1 o o o o
11 0 o o o o o o 1 o o o o 1 o o 1 o
12 0 o o o o o o o o o o o o o o o
13 1 o 0 100 o 1 1 0 o 0
14 0 o 0 1 100 010 o 0 010
15 1 o o 000 000 o 0 o 1 0 0
16 0 o o o 0 1 1 1 1 1 0 1 1 0
17 0 o o o o 0 o 0 o 000
18 0 o 1 o 1 0 o 0 o 1 0 0
19 0 o 0 1 o 100 1 o 1 o 0 1 0 1 0
20 1 o 1 o 001 o o 0 o 0 1 0 0 0
21 0 o 0 1 0 010 1 0 1 0 1 0
22 0 o 0 o 0 o o 0 o 0 o 0 0 0
23 0 o 0 o 1 0 1 1 0 00010
24 0 o 0 o 010 1 1 o 0 000
25 o 000 o 0 o o 0
26 1 1 1 1 001 o 0 1 o 0
27 0 o 0 o 100 1 0 1 0 o 1 0
28 0 o o 000 o o 0 o 0 0
29 1 1 0 1 001 1 0 1 1 0 1 1
30 0 o 1 o o 0 100 1 0 000 0
31 0 o 0 100 000 o 0 10110
32 0 o 0 1 000 1 0 o 0 o 000
33 0 o o o 0 o 1 1 0 100
34 o 0 1 o 0 1 1 o 0 011
35 o o o 0 1 010 1 0 1 0 1 0
36 1 o o 001 o o 0 o 0 o 0 0 0
37 0 o o 100 1 o o 0 010
38 o 1 1 001 o o 1 1 o 0 100
39 o 0 o 100 000 o 0 o 0
40 o o 000 000 o 0 o 1 0 1
41 o o o 0 1 1 1 1 010
42 1 100 010 o 1 o 0
43 o 001 o 0 o o 1 0 0
44 o o 0 o 000
45 o o 0 o 000
154
Appendix I cant.
Fragment size (bp)
ACC. 279 283 287 294 300 303 305 309 319 325 330 336 342 346 352 355 358
100 o 0 o 0 0
0 o 0o o 02 0 0 100 o o 01 o 0 o 0
300 o 0 o 0 0 o 0 o 0 o 0
4 0 0 o 0 o 0 1 0 o 0 o
5 0 1 o 0 010 o 0 o 0 o
600 1 0 1 0 1 1 0 1 1 1 1 1 1
7 0 1 o 0 000 1 1 0 100 0 1 0 0
8 1 0 o 0 o 0 010 o 0 1 0 010
900 o 0 o 0 o 0 o 0 o 0
10 0 0 o 1 100 o 0 1 0 1 1 1 0 0
11 0 0 o 0 o 0 0 100 100 0 1 0 0
12 0 0 o 0 o 0 0 000 000 0 000
13 1 0 1 1 o 0 0 1 1 001 o 0
14 0 0 o 0 000 100 100 0 1 0 0
15 1 0 o 0 o 0 010 o 0 1 0 010
16 0 o 0 1 1 0 o 1 o 0 o 0
17 0 o 0 000 o o 0 o 0
18 0 1 1 0 1 0 1 0 100 1 0 0
19 0 0 o 0 010 o 0 100 1 1 0 0
20 1 0 o 0 100 o 0 o 1 1 0 010
21 0 0 o 1 000 1 0 000 o 0
22 0 0 o 0 010 o 0 o 1 o 0
23 0 0 o 0 000 o o 0 o 0
24 0 o 0 000 o o 0 o 0
25 0 o 1 o 0 o o 0
26 1 1 o 0 010 1 101 o 0
27 0 0 1 0 1 1 0 o 1 o 1 0 o 0
28 0 o 0 000 1 0 o 0 o 0
29 0 o 1 o 1 1 o 1 011 o 0
30 0 1 o 0 000 1 0 000 o 0
31 0 0 o 0 000 o 0 000 o 0
32 0 o 0 o 0 o 0 000 o 0
33 0 1 o 0 o 0 o 1 o 0 1 1
34 1 0 o 0 o 1 1 o o 0 o 0
35 0 0 1 0 000 1 1 0 100 1 010
36 0 0 o 0 000 000 o 0 1 0 000
37 0 1 0 000 1 0 100 1 1 0 0
38 0 1 1 0 o 0 1 0 o 0 o
39 1 1 o 0 100 o 0 o 1 1 0 o 1
40 0 0 o 0 o 1 o 1 o 0 1 0
41 0 1 0 o 1 0 o 0 o 1
42 0 1 o 0 o 1 1 100 100 1 0 0
43 0 0 o 0 o 0 o 0 001 010
44 0 o 0 o 0 o o 0 o
45 o 0 o o o 0 o
155
Appendix I cant.
Fragment size (bp)
ACC. 364 366 367 372 375 388 393 403 411 418 432 447 454 465 473 480 487 493
1 o o o o o
2 o o 0o o o o 0o o 0 o 0o 0011 o 0 o 0
o 0013 1 o o o o 0 o o 0 o 0
4 o o 0o 1 1 o o 0 o o 0 o 0
5 o o o 0o 1 o o 0 o o 0 o 0
6 o o 0o o o o o o 1 o 0
7 1 o 1 0o o o o 0 1 0 o 0 1 o 0 o 0
8 o o o o o o o 0 o 0 o o 0 100
9 1 o o o o o o 0 o o 0 o 0
10 001o 1 o o 1 o 1 0 1 o 0 1 o 0
11 1 o o 0o o o o o 0 1 0 o 0 o o 0
12 o o 000o o o o o 0 o 0 o 0 o o 0
13 o 000o 1 o o o 0 o 1 0 o 0
14 o 0011 o o o o o 0 1 0 o 0 1 o 0
15 o 0101 o o o o o 1 o o o o 0
16 100o o o 1 o o 1 o 1 1 0
17 o o 0o o o o o 0 o o 0 o 0
18 o 0o o 1 o o 1 1 0 o 0 o 0
19 o 0o 1 o 1 o o o 0 1 0 o 0 1 o 0
20 1 o 010o o o o o 1 o 1 o 1 o o 1
21 100o 1 o o o o o 0 o o 0 1 o 0
22 o 010o o o o o o 0 o o 0 o o 0
23 o 000o o o o 0 o o 0 o 0
24 o o o 0o o o 0 o o 0 o 0
25 o 0o o 1 o o 1 0 o o 0 o 0
26 o 0o o o o o o 0 o o 0
o o27 o 0o o o o 0 o o 0 o 0
28 o o 0o o o o 0 o o 0 o 0
29 o 0o o 1 o o o o 0 1 0
30 o 0o t o 1 o o o 0 o o 0 o 0
31 o o 01 o o o o o 0 o o 0 o 0
32 o 0o o 1 o o o 1 0 o o 0 o 0 o 0
33 o 1 o o 1 o o 0 o o 0 o 0
34 o o 0o 1 o o o 0 o o 0 o 0 o 0
35 o o 1 1 o o o 0 1 0 o 0 o 0
o 01036 1 o o o o o 1 o 0 o o 0 000
37 o 1 1 o o 1 0 1 0 o 0 1 o 0 o 0
38 o 1 o o o o o o 0 o 1 o o 0 1 1 1
39 o o o o 1 o 1 o 0 o o o
40 o 000o o o o 0 o 1 1 o 1 000
41 1 o 1 1 1 0 o 0 o 0 o
42 o o 1 1 1 o 0 1 1 o 0 1 o 0 o 1 1
43 1 o o o o o o 0 o 0 o 1 o o 0 1 0 0
44 o o 1 o 0 o 1 o 0 o 0 o 1
45 o o o o 0 o 0 o 0 o 0 o 0
156
Appendix II Amplified fragments score for 10 microsatellite loci: sb1-1 0, sb4-
15, sb4-22, sb4-32, sb5-85, sb5-236, sb6-36, sb6-57, sb6-84
and sb6-342.
Fragment size (bp)
sb 1-10 sb4-15 sb4-22
Acc 110 275 304 510 135 254 314 330 347 405 492
1 0 1 1 0 1 0 1 1 0 0 0
2 0 1 1 0 1 1 0 0 0 0 0
3 0 1 0 0 1 0 0 1 0 0 0
4 0 1 0 0 1 0 0 1 0 0 0
5 0 1 0 0 1 0 0 1 0 0 0
6 0 0 1 0 1 0 1 0 0 0 0
7 0 0 1 0 1 0 0 1 0 0 0
8 0 1 1 0 1 0 0 1 1 0 0
9 0 1 1 0 1 0 0 1 0 0 0
10 0 0 1 0 1 0 0 1 0 0 0
11 0 1 0 0 1 0 0 1 0 0 0
12 0 1 0 0 1 0 0 0 1 0 0
13 1 0 0 0 1 0 0 1 0 0 0
14 0 0 1 0 1 0 0 0 1 0 0
15 0 0 0 1 1 0 0 1 0 0 0
16 0 1 0 0 1 0 0 1 0 0 0
17 0 0 1 0 1 0 0 1 0 0 0
18 0 0 1 0 1 0 0 0 1 0 0
19 0 1 0 0 1 0 0 0 1 0 0
20 0 1 0 0 1 0 0 0 1 0 0
21 0 1 0 0 1 0 0 1 0 0 0
22 0 1 0 0 1 0 0 1 0 0 0
23 0 0 1 0 1 0 0 1 0 0 0
24 0 0 0 1 1 0 0 1 1 0 0
25 0 1 0 1 1 0 0 1 1 0 0
26 0 1 0 0 1 0 0 1 1 0 0
27 0 1 0 0 1 0 0 1 0 0 0
28 0 0 0 1 1 0 0 1 0 0 0
29 0 1 0 0 1 0 0 1 0 0 0
30 0 0 0 1 1 0 0 1 0 0 0
31 0 1 0 0 1 0 1 0 0 0 0
32 0 0 0 1 1 0 1 0 0 0 0
33 0 0 0 1 1 0 1 0 0 0 0
34 0 0 1 0 1 0 1 0 0 0 0
35 0 1 0 0 1 0 1 0 1 0 0
36 0 1 0 0 1 0 1 0 0 0 0
37 0 1 0 0 1 0 0 0 1 0 0
38 0 1 0 0 1 0 1 0 0 0 0
39 0 1 0 0 1 0 0 0 1 0 0
40 0 1 0 0 1 0 0 1 0 0 0
41 0 1 0 0 1 0 1 0 0 0 0
42 0 1 0 0 1 0 0 0 1 0 0
43 0 1 1 0 1 0 0 0 1 0 0
44 0 1 0 0 1 0 0 0 1 1 1
45 0 1 0 0 1 0 0 0 1 1 1
157
Appendix II cant.
Fragment size (bp)
sb4-32 sb5-85
Ace 112 174 190 240 293 357 439 497 533 109 159 186 215 336 408 510
1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0
2 00 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1
3 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0
4 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0
5 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1
6 0 0 0 0 1 0 1 0 0 1 1 0 1 0 1 1
7 0 0 0 1 1 1 1 1 1 1 1 0 1 0 0 1
8 0 0 0 1 1 1 1 1 1 1 0 0 1 0 0 1
9 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1
10 0 0 0 1 1 1 1 1 1 1 0 0 1 0 0 1
11 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1
12 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1
13 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1
14 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1
15 0 0 1 1 1 1 1 1 1 1 0 1 1 0 0 1
16 0 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1
17 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1
18 0 0 0 1 1 0 0 1 0 1 1 0 0 0 0 0
19 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0
20 0 0 1 1 0 0 0 1 1 1 0 0 1 0 0 0
21 0 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0
22 0 0 1 1 0 1 0 1 1 1 1 1 1 0 0 0
23 0 0 1 1 1 0 0 1 1 1 1 1 1 0 0 0
24 0 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0
25 0 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0
26 0 1 0 1 1 0 0 1 0 1 0 0 1 0 0 0
27 0 0 0 1 0 0 1 1 0 1 1 0 1 0 0 0
28 0 0 1 1 1 0 0 0 0 1 0 0 1 0 0 0
29 0 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0
30 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0
31 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 0
32 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0
33 0 0 1 1 1 0 0 0 0 1 0 1 1 0 0 0
34 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0
35 1 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0
36 1 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0
37 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0
38 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0
39 0 0 1 1 1 0 1 0 1 1 0 0 1 0 0 0
40 0 1 0 1 1 0 1 0 1 1 0 0 1 0 0 0
41 0 1 0 1 1 0 1 0 1 1 0 0 1 0 0 0
42 0 1 0 1 1 0 1 1 1 1 1 0 1 0 0 0
43 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0
44 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0
45 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0
158
Appendix II cant.
Fragment size (bp)
sb5-236 sb6-36 sb6-57
Acc
110 185 207 265 165 183 198 249 279 292 310 320 343
1 1 1 0 0 0 0 1 0 0 0 0 1 0
2 0 1 0 0 0 0 1 0 0 0 0 1 0
3 1 1 0 0 0 0 1 0 0 0 0 1 0
4 0 0 1 0 1 0 0 0 0 0 0 0 1
5 0 0 1 0 0 0 1 0 0 0 0 0 1
6 0 0 1 0 1 0 0 0 0 1 0 0 0
7 0 0 1 0 1 0 1 0 0 0 0 0 1
8 0 0 1 0 1 0 1 0 1 0 1 1 0
9 0 1 0 0 1 0 0 0 0 0 1 1 0
10 0 1 0 1 1 0 0 0 0 0 1 1 0
11 0 1 0 0 1 0 0 0 0 1 1 0 0
12 0 1 0 0 0 1 0 0 0 1 0 1 0
13 0 1 0 0 0 0 1 0 0 1 0 0 0
14 0 1 0 0 0 0 1 0 0 1 0 1 1
15 0 0 1 1 0 1 0 -0 0 1 0 1 0
16 0 1 0 1 0 0 1 0 0 1 0 0 0
17 0 1 0 1 0 1 0 0 0 1 0 0 1
18 0 1 0 0 0 1 0 0 0 1 0 0 0
19 0 1 0 0 0 1 0 0 0 0 0 1 0
20 0 0 1 0 0 0 1 0 0 0 0 1 0
21 0 1 0 0 0 0 0 1 0 0 0 1 0
22 0 1 0 0 0 0 0 0 1 0 0 0 1
23 0 1 0 0 0 0 0 1 0 0 0 0 1
24 1 1 1 0 0 0 0 1 0 0 0 0 1
25 1 1 1 0 0 0 0 1 1 1 1 0 1
26 0 1 0 0 0 0 0 1 1 0 0 0 1
27 0 1 0 0 0 0 0 0 1 0 0 0 1
28 0 0 1 0 0 0 0 1 0 0 0 0 1
29 1 1 0 0 0 0 0 0 1 0 0 0 1
30 0 0 1 0 0 0 0 1 0 0 0 1 1
31 1 0 0 1 0 1 0 0 0 0 0 1 0
32 1 0 1 1 1 0 0 0 0 0 0 1 0
33 1 0 1 1 1 0 0 0 0 0 1 0 0
34 1 0 0 1 0 0 1 0 0 0 1 0 0
35 1 0 0 1 1 0 0 0 0 0 1 0 0
36 1 0 0 1 1 0 0 0 0 1 0 0 0
37 1 1 0 0 1 0 0 0 0 1 0 0 0
38 1 0 0 1 1 0 0 0 0 1 0 0 0
39 1 0 1 1 1 0 0 0 0 1 0 0 0
40 0 0 1 1 0 0 1 0 0 0 1 0 0
41 1 0 0 1 0 0 1 0 0 1 0 0 0
42 0 0 1 0 0 0 1 0 0 1 0 0 0
43 0 1 1 1 1 0 1 0 1 0 1 0 0
44 1 1 1 1 1 0 0 0 0 1 0 0 0
45 1 0 1 0 1 0 0 0 0 1 0 0 0
159
Appendix II Cant.
Fragment size (bp)
sb6-84 sb6-342
Acc
173 200 295 254 280 382 544 634
1 0 1 1 0 1 1 0 1
2 0 1 1 0 1 0 0 1
3 0 1 0 0 1 0 0 1
4 0 0 1 0 0 1 1 1
5 0 0 1 0 1 0 0 1
6 0 0 1 0 1 1 0 1
7 0 0 1 0 1 1 0 0
8 0 1 0 0 1 1 0 1
9 0 1 0 0 0 1 0 1
10 1 0 0 0 0 1 0 1
11 1 0 0 0 1 0 0 1
12 1 0 0 0 1 1 0 1
13 1 0 0 0 1 1 0 1
14 1 0 0 1 0 0 0 1
15 1 0 0 0 1 1 0 1
16 0 1 0 0 1 0 0 1
17 0 1 0 0 1 1 0 1
18 1 0 0 0 1 0 0 1
19 1 0 0 0 1 1 0 1
20 1 0 0 0 1 0 0 1
21 0 1 0 0 1 0 0 1
22 0 1 0 0 1 1 0 0
23 0 1 0 0 0 0 0 1
24 0 1 0 0 0 1 0 1
25 0 1 0 0 0 1 0 1
26 0 1 0 0 1 1 1 1
27 0 1 0 0 1 1 1 1
28 0 1 0 0 1 1 1 1
29 0 1 0 0 0 1 1 1
30 1 1 0 0 0 1 0 1
31 1 1 0 0 0 1 0 1
32 1 0 0 0 0 1 0 1
33 1 0 0 0 0 1 0 1
34 1 0 0 0 0 1 0 1
35 1 0 0 0 0 1 0 1
36 1 0 0 0 0 1 0 1
37 1 0 0 0 1 0 0 1
38 1 0 0 0 0 1 0 1
39 1 0 0 0 0 1 0 1
40 0 1 0 0 0 1 0 1
41 0 1 0 0 1 1 (3 1
42 0 1 0 0 1 1 0 1
43 1 1 0 0 1 1 0 1
44 0 1 0 0 0 1 0 1
45 0 1 0 0 0 1 0 1
160
Appendix III Average values calculated for 16 quantitative traits in 45
sorghum accessions.
No OF LN LL LW LA 1NL LLS PHI PL PW NPS HWI GYPP TSWI TP KNPP
1 94.0 9.9 60.0 7.3 324.9 20.8 17.3 185.0 11.1 8.5 56.5 87.4 60.2 28.0 69.0 2151.1
2 93.0 9.3 59.2 7.1 311.8 20.8 16.0 192.0 10.7 8.8 56.3 94.4 65.6 30.0 69.5 2186.7
3 98.0 13.1 72.2 8.7 469.2 28.9 23.4 250.5 16.9 10.3 85.3 121.5 84.6 29.0 69.6 2915.5
4 94.0 13.8 89.0 11.8 781.2 24.3 20.9 308.0 12.9 10.7 138.6 179.2 123.6 28.3 69.0 4367.1
5 106.0 13.3 89.2 10.8 719.6 24.0 19.0 251.3 13.6 10.6 116.3 123.7 87.6 31.7 70.8 2762.2
6 100.0 13.9 80.4 11.4 684.7 27.5 22.8305.013.9 9.6 66.5 152.6 107.3 20.7 70.3 5183.1
7 111.0 14.7 89.1 11.7 775.4 26.3 20.0 330.5 14.9 9.1 82.1 131.7 96.8 23.3 73.5 4152.8
8 92.0 13.8 86.2 10.0 643.9 21.9 21.0 240.0 17.5 9.4 83.0 169.0 115.6 26.7 68.4 4328.8
9 97.0 13.2 76.2 9.3 529.4 30.4 23.7 285.0 18.2 11.2 94.6 176.7 124.3 27.3 70.4 4553.1
10 113.0 12.2 61.6 8.3 381.9 26.3 20.5 285.0 12.4 10.7 103.1 180.6 138.5 33.7 76.7 4109.5
11 96.0 11.3 78.4 9.4 547.6 23.6 22.2 315.5 15.4 10.7 109.2 167.7 124.2 28.3 74.1 4389.1
12 94.0 12.2 72.2 8.2 442.3 22.4 22.0 275.0 17.2 11.3 69.2 146.5 113.3 25.0 77.3 4530.0
13 91.0 9.5 58.7 7.2 313.5 24.7 17.0 215.5 11.8 10.3 96.0 83.5 64.3 23.0 77.0 2795.2
14 94.0 14.0 72.6 8.9 482.7 23.6 20.5 290.5 20.1 9.4 87.8 176.4 114.3 24.8 64.8 4607.3
15 109.0 13.3 83.8 12.7 791.7 24.2 21.2 220.3 26.0 12.9 71.9 161.2 108.2 26.7 67.1 4052.4
16 92.0 11.4 63.9 8.1 386.6 22.0 21.0 265.0 17.5 12.0 84.7 121.0 91.7 29.7 75.8 3087.5
17 112.0 12.0 84.3 11.1 699.0 25.8 20.5 290.0 17.5 10.2 71.5 152.1 96.7 20.3 63.6 4762.1
18 106.0 11.5 83.0 11.1 685.1 26.5 21.0 285.0 18.0 8.7 87.7 155.3 101.7 29.0 65.5 3505.9
19 105.0 12.0 70.7 11.8 620.6 25.1 23.4 294.0 19.3 11.7 93.7 187.3118.226.0 63.1 4546.2
20 109.0 13.0 80.0 12.5 747.0 26.1 24.3 260.0 21.0 12.5 101.3 196.7 128.1 24.3 65.1 5272.0
21 104.0 11.5 64.7 10.7 515.7 24.3 20.0 270.0 24.7 11.3 81.0 128.7 89.7 20.0 69.7 4483.5
22 93.0 11.5 68.0 9.5 482.6 19.1 18.0 275.0 17.6 8.6 82.3 141.0 104.5 26.7 74.1 3913.9
23 108.0 11.7 80.2 11.3 674.0 28.1 20.5 270.0 15.2 9.6 61.9 167.5 133.0 24.7 79.4 5384.6
24 89.0 10.3 66.4 9.9 488.6 22.7 19.2 230.0 17.5 10.2 70.0 131.6 102.0 18.0 77.5 5668.9
25 98.0 11.0 61.3 9.5 435.2 28.3 22.9 263.0 22.0 11.7 76.2 168.7 122.7 24.0 72.8 5112.5
26 98.0 11.5 78.7 10.5 618.8 28.0 24.7 290.0 23.0 8.3 84.7 153.3 102.6 25.7 66.9 3992.6
27 92.0 10.5 80.7 9.3 560.4 26.6 19.5 285.0 25.1 11.0 76.6 153.2 99.4 26.7 64.9 3721.0
28 106.0 11.5 77.3 9.8 567.6 26.4 23.4 295.0 20.7 12.6 79.0 115.7 87.3 20.0 75.4 4362.5
29 94.0 13.0 80.8 11.1 667.0 25.5 21.9 290.0 20.9 12.8 82.6 175.2 109.5 24.0 62.5 4563.3
30 105.0 9.1 67.7 9.3 467.8 23.0 21.3 220.0 21.3 9.5 75.2 106.7 74.2 27.0 69.6 2749.6
31 90.0 11.0 73.0 9.0 490.8 22.4 19.5 296.0 23.2 8.6 112.3 163.0 104.6 28.0 64.2 3735.7
32 89.0 9.7 70.0 8.8 461.7 30.4 22.8 310.0 18.1 10.5 64.3 84.5 61.7 20.0 73.0 3082.5
33 100.0 10.0 73.0 8.3 454.2 29.7 21.9 316.0 20.7 10.2 63.6 90.0 63.4 19.5 70.4 3248.7
34 101.0 11.4 62.7 9.1 425.1 28.5 20.9 320.0 19.5 12.9 100.3 125.3 87.5 27.7 69.8 3157.0
35 97.0 12.3 77.3 10.1 580.3 25.6 20.0 295.0 16.0 11.4 127.6 187.6 125.5 25.0 66.9 5021.6
36 101.0 14.4 72.7 10.9 589.2 25.9 22.3 320.0 17.5 11.7 111.5 160.1 112.0 30.7 69.9 3646.6
37 98.0 13.6 67.4 10.2 511.0 23.3 23.0 330.0 15.6 11.5 134.5 156.6 118.9 27.3 75.3 4354.6
38 106.0 12.4 71.5 9.6 512.7 24.8 20.2 300.0 16.8 8.9 102.5 170.6 114.7 31.3 67.2 3663.3
39 86.0 9.0 81.0 8.8 530.6 25.8 20.8 275.0 25.0 10.1 65.7 83.9 61.3 20.3 73.1 3019.7
40 95.0 10.5 77.5 10.2 587.6 24.8 22.6 340.0 21.2 9.0 89.3 141.3 109.6 30.0 77.6 3654.7
41 93.0 11.5 75.3 10.3 576.8 23.1 20.7 315.0 17.6 9.6 90.7 161.7 124.7 30.0 77.1 4156.0
42 90.0 10.0 65.3 9.6 466.1 25.4 20.6 295.5 16.9 10.8 98.7 166.2 126.2 31.0 75.9 4070.7
43 99.0 13.4 72.8 11.2 606.4 30.2 24.8 360.0 14.8 9.4 125.1 175.7 143.0 31.0 81.4 4612.6
44 101.0 12.6 69.7 10.1 525.9 30.1 22.9 355.0 14.1 9.7 132.0 196.5 148.0 33.0 75.3 4486.1
45 103.0 12.5 64.9 9.6 465.4 26.7 24.9 310.0 14.4 9.7 120.7 191.4 143.3 28.7 74.8 4886.8
161
Appendix IV Binary scores of qualitative traits for 45 sorghum accessions.
Leaf mid rib colour Panicle exsertion Panicle compactness & shape Awn
Acc 1 2 3 1 2 3 4 1 4 6 8 9 10 0 1
1 o o o o 1
2 o o oo o oo o o o o 1 1 o1 o o o o 1 o 1 o
3 o o o o 1 o o o o o
o 1o o o 14 o 1 o o o o o
o o o 1 o 15 o o o o 1 o o o o 1
o o o 1 o6 o o o o o o 1
7 o o oo o1 o o o o o o
8 o oo o o oo o o o 1 o
9 o oo o o oo 1 o o o o
o o o oo o10 1 1 o o o o o o o 1 o
11 o o 1 o o o o o o o o 1
12 o o oo o 1 o o o 1
o o o13 o oo oo o 1 o o o
o o 1 oo o o14 o o o o 1
o oo o o o15 o 1 o o o 1 o o
o o o o16 o o o 1 o o o o o 1 o
17 o o o1 o o o o o o o o o
18 o o o o 1 o o o o 1
19 o oo o o o1 o o o o o
20 o 1 oo o oo o 1 o o o o o 1 o o
21 o o o 1 o o o 1 o o o
o oo o o22 1 o 1 o o o 1
o oo o o o23 1 o o o o o o
24 o o 1 o o1 o o o 1 o o o 1 o
25 oo o o o1 o o o o 1
o o26 o o oo o1 o o o o o o 1
o oo o oo 127 1 o o o o 1
28 o o o o o o1 1 o o o o 1 o
29 oo oo o o1 o o o o o o
o 1 oo o o30 o o o 1 o o o o o o
31 o o o o 1o o o 1 o
o oo oo 1o o·32 o o o o o
o oo o33 o o o 1 o o
34 o o oo o 1o o o 1 o o o o
35 o oo o o oo o o 1 o o 1 o
36 o oo o oo o 1 o o o o 1
37 o o oo o oo o 1 o o 1 o o
o o o38 1 o o 1 o o o o o o
39 o o o 11 o oo 1 o o o 1 o
40 o o o oo o 11 o o o o o
o o o o41 1 o o o o o o o
42 o oo o oo o 1 o o o o 1
43 o o oo oo o o o o o o o o
44 o o o o o o o o o
45 o oo o oo o o o o o o o
162
Appendix IV cant.
Glume colour Grain covering Grain colour
Acc
1 2 3 4 5 6 1 3 1 2 3 4 5 7 9
1 1 o o o o
2 o o o 1o o 1o oo o o o oo o1 1 o 1 o o o o o
3 o o 1 o o o o o o o 1 o o o
4 o o o o 1 o o o o o o o o 1
5 o o 1 o o o o o o o o o o 1
6 o 1 o o o o 1 o 1 o o o o o o
7 o 1 o o o o o 1 o 1 o o o o o
8 o o 1 o o o o o o o 1 o o o
9 o 1 o o o o o 1 o o o o o
o o10 o o o o o o o 1 o o o o
11 o o o o o o o o o o o 1 o
12 o o o o o o o 1 o o o o o
13 o o o o o o o o o o o o
14 o o o o o 1 o o o o o o o
15 o o 1 o o o o 1 o o 1 o o o o
16 o 1 o o o o 1 o o 1 o o o o o
17 o o o o 1 o o 1 o o o o o o
18 o o o 1 o o o o o 1 o o o o
19 o o o o 1 o 1 o o o o o 1 o o
20 1 o o o o o 1 o 1 o o o o o o
21 o o o o o o 1 o o o o o o 1
22 o o o o o o o o o o o o
23 o o o o o o o o 1 o o o o
24 o o o o o o o o o o o 1 o
25 o o o o 1 o o o o o o 1 o o
26 o o 1 o o o o 1 o o o o o 1
27 o1 o o o o o o 1 o o o o o o
28 o o o 1 o o o o o 1 o o o o
29 o 1 o o o o 1 o o 1 o o o o o
30 1 o o o o o o 1 o o o o o o
31 o o 1 o o o o o o 1 o o o o
32 o o o o o o o o o o o o
33 o o o o o 1 o o o o o 1 o o
34 1 o o o o o o 1 o o o o o o
35 o o 1 o o o o o o o 1 o o o
36 o o o 1 o o o o o 1 o o o o
37 1 o o o o o o 1 o o o o o o
38 o o o o 1 o o o o o o o o
39 o o o o o o o o 1 o o o o
40 o o 1 o o o o 1 o o o o o o
41 1 o o o o o o 1 o o o o o o
42 o o 1 o o o o o o o o o o
43 o o 1 o o o o o o 1 o o o o
44 o o o o 1 o o 1 o o o o o o
45 o o o o o o o o o o o o
163
Appendix IV cant.
Stalk juiciness Plant colour Endosperm texture
Acc 0 J 1 2 5 7 9
1 0 0 1 0 0
2 0 0 0 0 1
3 0 0 1 0 0 1
4 0 1 0 0 0
5 0 1 0 0 1 0
6 0 0 1 0 0
7 0 1 0 0 0
8 0 0 0 0
9 0 0 1 0 0
10 0 0 0 1 0
11 0 0 0 1 0
12 0 0 0 0
13 0 0 0 0
14 0 0 1 0 0 1
15 0 1 0 0 1 0
16 0 0 1 0 0
17 0 0 0 1 0
18 0 0 0 0 1
19 0 0 0 0
20 0 0 0 1 0
21 0 0 0 0 1
22 0 0 0 0
23 0 0 0 0
24 0 0 0 1 0
25 0 0 0 0
26 0 0 0 0 1
27 0 0 0 1 0
28 0 0 0 1 0
29 0 0 1 0 0
30 0 0 0 0
31 0 0 1 0 1 0
32 0 1 0 0 0
33 0 0 0 0
34 1 0 0 0 0
35 0 1 0 0 0 1
36 0 0 0 0
37 0 0 0 0
38 0 0 0 0
39 0 0 0 1 0
40 0 0 1 0 0
41 1 0 0 1 1 0 0
42 0 1 1 0 0 0 1
43 0 0 0 0
44 1 0 0 0 1 0
45 0 0 0 0
164
Appendix V Binary scores of quantitative traits for 45 sorghum accessions.
OF LN LL
Ace 86-89 90-92 93-95 96-101 106-112 9:10 11:12 13 - 14 50 -60 61 -70 71-80 81-90
1 o o 1 o
2 o o 1o o o oo 1 o o o1 1 o o 1
o o o o3 o o 1 o o o o
4 o o 1o o1 o o o o o o o
5 o o o o 1 o o o o o 1
6 o o o 1 o o o o o o
7 o o o o 1 o o o o o
8 o 1 o o o o o o
o o9 o o 1o 1 o o o 1 o
10 o 1o o oo o 1 o o o 1
o o o o11 o 1 o o o o
o o12 oo 1 o o o 1 o o
13 o o 1 o1 o o o 1 o o 1 o
14 oo oo 1 o o o o o o 1
15 o oo o o 1 o o 1 o o
16 oo 11 o o o o o o 1 o o
17 o o o o o o o o
18 oo o o o o o o o
o o19 1o o o o 1 o o 1 o
20 o oo o o o o 1 o o 1
21 o o oo o 1 o o o
22 oo oo 1 o o o o o 1 o
23 o oo o o 1 o 1 o o o 1
24 o1 o o o o 1 o o o o
25 o o oo o o o o 1 o
26 o oo o 1 o o o o o 1
27 oo 1 o o o o o o o
o o28 1o o o 1 o 1 o o o 1
29 o oo 1 o o o o 1 o o o 1
30 o o o o 1 1 o o o o
31 o o1 o o o o 1 o o o 1
32 o o1 o o o o o o 1 o o
33 o o o o o o o o 1
34 o oo o o o o o 1 o
35 oo o o o o 1 o o o
36 o o oo 1 o o o o o 1
37 o oo o 1 o o o o 1 o
38 o oo o o 1 o 1 o o o 1 o
39 1 o o o o 1 o o o o o
40 o o o o o o o o
41 o oo 1 o o o 1 o o o 1
42 oo 1 o o o 1 o o o 1 o o
43 o o o o o o 1 o o 1
44 oo o o 1 o o 1 o o o
45 o oo o o o o o o o
165
Appendix V cant.
Acc LW LA 1NL LSL
401- 601- 701-
7-8 9-10 11-13 300-400 600 700 800 19-24 25-27 28-30 16-18 19-23.1 23.2-25
1 1 o o o o o 1 0 o 1 o o
2 1 o o 1 o o o 1 0 o 1 o o
3 0 1 o o 1 o o o 0 1 o o 1
4 0 o o o o o o o o
5 0 o o o o 1 1 0 o o o
6 0 o o o 1 o o 0 1 o o
7 0 o 1 o o o 1 o 1 o o o
8 0 1 o o o 1 o 1 0 o o 1 o
9 0 1 o o 1 o o o 0 1 o o 1
10 1 o o 1 o o o o 1 o o o
11 0 1 o o o o o o o o
12 1 o o o 1 o o 1 0 o o 1 o
13 1 o o 1 o o o o 1 o 1 o o
14 0 1 o o 1 o o o o o o
15 0 o 1 o o o 1 1. 0 o o o
16 1 o o 1 o o o 1 0 o o o
17 0 o o o o o o o o
18 0 o o o o o o o o
19 0 o o o 1 o o o o o 1
20 0 o o o o 1 o 1 o o o 1
21 0 o 1 o o o o o o 1 o
22 0 1 o o 1 o o 1 0 o 1 o o
23 0 o 1 o o 1 o o 0 1 o o
24 0 o o o o 1 0 o o o
25 0 1 o o 1 o o o 0 o 1 o
26 0 o o o o o 0 1 o o 1
27 0 o o o o o o o 1 o
28 0 1 o o 1 o o o o o o 1
29 0 o 1 o o 1 o o 1 o o o
30 0 o o o o o o o o
31 0 o o o o 1 0 o o o
32 0 1 o o o o o 0 o o
33 1 o o o o o o 0 1 o o
34 0 o o o o o 0 1 o o
35 0 1 o o o o o 1 o o o
36 0 o 1 o o o o 1 o o o
37 0 o o o o 1 0 o o o
38 0 o o o o o o o o
39 0 o o o o o o o o
40 0 o o o o o 1 o o o
41 0 o o o o o o o o
42 0 1 o o 1 o o o 1 o o 1 o
43 0 o 1 o o 1 o o 0 o o 1
44 0 o o o o o 0 1 o 1 o
45 0 o o o o o o o o
166
Appendix V cont.
PHt PL PW
Acc 185- 200- 300- 323-
195 294 8.6-322 10.5-373 10-12 13-16 17-20 21-25 8-8.5 10 11.5 12-14
1 o o o 1 o
2 o o o 1 oo o o1 o 1 o o o 1 o o o
3 o 1 o o o o 1 o o 1 o o
4 o o 1 o o o o o o o
5 o 1 o o o o o o o 1 o
6 o o 1 o o o o o o o
7 o o o 1 o 1 o o o o o
8 o o o o o o o 1 o o
9 o o o o o 1 o o o o
10 o 1 o o o o o o o o
11 o o 1 o o 1 o o o o o
12 o o o o o 1 o o o 1 o
13 o o o 1 o o o o o o
14 o o o o o 1 o o 1 o o
15 o o o o o o 1 o o o 1
16 o o o o o o o o o 1
17 o o o o o o o o 1 o
18 o o o o o o o 1 o o
19 o o o o o 1 o o o o
20 o o o o o o o o o 1
21 o o o o o o 1 o o 1 o
22 o o o o o 1 o o 1 o o
23 o o o o 1 o o o 1 o o
24 o o o o o 1 o o 1 o o
25 o o o o o o o o o 1
26 o o o o o o 1 o o o
27 o o o o o o 1 o o 1 o
28 o o o o o 1 o o o o
29 o o o o o o o o o 1
30 o o o o o o o o o
31 o 1 o o o o o 1 o 1 o o
32 o o o o o 1 o o o 1 o
33 o o o o o o 1 o 1 o o
34 o o 1 o o o 1 o o o o 1
35 o 1 o o o 1 o o o o 1 o
36 o o 1 o o o 1 o o o o
37 o o o 1 o o o o o o 1
38 o o 1 o o o 1 o o o o
39 o 1 o o o o o o o o
40 o o o o o o 1 o o o
41 o o 1 o o o 1 o o 1 o o
42 o 1 o o o o 1 o o o 1 o
43 o o o o o o o o o
44 o o o 1 o o o o o o
45 o o o o o o o o o
167
Appendix V cant.
NPB HWt GYPP
Acc
50-77 80-100 101-135 80-100 110-125 130-150 160-200 60-75 80-100 105-120 125-150
1 1 o o 1 o o
o o 1 o2 o o o o1 1 o o 1 o o o
3 o 1 o o 1 o o o 1 o o
4 o o o o o 1 o o 1 o
5 o o 1 o 1 o o o 1 o o
6 1 o o o o o o o 1 o
7 o o o o 1 o o 1 o o
8 o o o o o o o o
9 o 1 o o o o o o 1 o
10 o o 1 o o o o o o 1
11 o o 1 o o o 1 o o o
12 1 o o o o 1 o o o 1 o
13 o o 1 o o o 1 o o o
14 o 1 o o o o o o o
15 1 o o o o o 1 o o 1 o
16 o 1 o o 1 o o o o o
17 1 o o o o o o o o
18 o o o o 1 o o 1 o o
19 o 1 o o o o o o 1 o
20 o o 1 o o o 1 o o o 1
21 o o o o o o o o
22 o 1 o o o 1 o o o 1 o
23 o o o o o 1 o o o 1
24 o o o o o o 1 o o
25 1 o o o o o 1 o o 1 o
26 o 1 o o o o o o o
27 o o o o 1 o o o o
28 1 o o o 1 o o o 1 o o
29 o o o o o 1 o o 1 o
30 1 o o o 1 o o 1 o o o
31 o o 1 o o o 1 o o 1 o
32 o o o o o 1 o o o
33 1 o o 1 o o o 1 o o o
34 o 1 o o 1 o o o 1 o o
35 o o o o o o o o 1
36 o o o o o 1 o o o
37 o o 1 o o 1 o o o o
38 o o 1 o o o 1 o o 1 o
39 1 o o 1 o o o 1 o o o
40 o o o o 1 o o o 1 o
41 o o o o o o o o
42 o 1 o o o o o o o
43 o o o o o o o o
44 o o o o o o o o
45 o o o o o o o o
168
Appendix V cant.
TSWt TP KNPP
Acc
20-22 24-26 27-29 30-34 60-70 71-75 76-85 20 30 40 50
1 o o 1 o o
o o oo o2 o o1 o o o o o
3 o o 1 o o o 1 o o o
4 o o 1 o 1 o o o o 1 o
5 o o o 1 o 1 o 1 o o o
6 o o o 1 o o o o o 1
7 1 o o o o 1 o o o o
8 o o o o o o o o
9 o o 1 o 1 o o o o o
10 o o o 1 o o 1 o o o
11 o o 1 o o 1 o o o o
12 o 1 o o o o o o 1 o
13 1 o o o o o 1 1 o o o
14 o 1 o o o o o o o
15 o o 1 o 1 o o o o 1 o
16 o o o 1 o o 1 o 1 o o
17 1 o o o o o o o 1 o
18 o o 1 o o o o 1 o o
19 o 1 o o o o o o 1 o
20 o 1 o o o o o o o 1
21 1 o o o 1 o o o o 1 o
22 o o 1 o o 1 o o 1 o o
23 o 1 o o o o o o o
24 1 o o o o o 1 o o o 1
25 o o o o 1 o o o o 1
26 o 1 o o o o o o o
27 o o 1 o 1 o o o 1 o o
28 o o o o 1 o o o o
29 o 1 o o o o o o 1 o
30 o o o o o 1 o o o
31 o o 1 o 1 o o o o o
32 o o o o o o o o
33 1 o o o o o o o o
34 o o 1 o o o o 1 o o
35 o 1 o o o o o o o 1
36 o o o 1 1 o o o 1 o o
37 o o 1 o o 1 o o o 1 o
38 o o o 1 1 o o o o o
39 1 o o o o o o 1 o o
40 o o o o o o 1 o o
41 o o o o o o o o
42 o o o o o o o o
43 o o o 1 o o 1 o o o
44 o o o 1 o o o o o
45 o o o o o o o o
169
Appendix VI An example of the pair-wise genetic distances estimated between
some of the accessions based on morho-agronomical data.
First Second Actual Dendrogram Actual Percent
Row Row Distance Distance Difference Difference
2 0.354 0.354 0.000 0
3 0.629 0.628 0.001 0.14
4 0.629 0.628 0.001 0.14
5 0.661 0.628 0.033 5.01
6 0.629 0.628 0.001 0.14
7 0.677 0.628 0.049 7.2
8 0.612 0.628 -0.016 -2.6
9 0.612 0.628 -0.016 -2.6
10 0.645 0.628 0.017 2.67
11 0.645 0.628 0.017 2.67
12 0.595 0.628 -0.033 -5.57
13 0.540 0.509 0.031 5.68
14 0.645 0.628 0.017 2.67
15 0.629 0.628 0.001 0.14
16 0.612 0.628 -0.016 -2.6
17 0.645 0.628 0.017 2.67
18 0.645 0.628 0.017 2.67
19 0.677 0.628 0.049 7.2
20 0.629 0.628 0.001 0.14
21 0.645 0.628 0.017 2.67
1 22 0.595 0.628 -0.033 -5.57
1 23 0.677 0.628 0.049 7.2
1 24 0.645 0.628 0.017 2.67
1 25 0.677 0.628 0.049 7.2
26 0.661 0.628 0.033 5.01
27 0.595 0.628 -0.033 -5.57
28 0.692 0.628 0.064 9.24
29 0.629 0.628 0.001 0.14
30 0.520 0.628 -0.108 -20.73
31 0.612 0.628 -0.016 -2.6
32 0.629 0.628 0.001 0.14
33 0.577 0.628 -0.051 -8.82
34 0.604 0.628 -0.024 -4.05
35 0.692 0.628 0.064 9.24
36 0.661 0.628 0.033 5.01
37 0.629 0.628 0.001 0.14
38 0.661 0.628 0.033 5.01
39 0.629 0.628 0.001 0.14
40 0.645 0.628 0.017 2.67
41 0.595 0.628 -0.033 -5.57
42 0.707 0.628 0.079 11.15
43 0.677 0.628 0.049 7.2
44 0.661 0.628 0.033 5.01
45 0.677 0.628 0.049 7.2
170
Appendix VII An example of the pair-wise genetic distances estimated between
some of the accessions based on AFLP data.
First Second Actual Dendrogram Actual Percent
Row Row Distance Distance Difference Difference
2 0.445 0.459 -0.014 -3.06
3 0.429 0.424 0.004 1
4 0.477 0.477 0.000 0.01
5 0.413 0.413 0.000 0
6 0.570 0.532 0.039 6.78
7 0.522 0.532 -0.010 -1.83
8 0.703 0.656 0.047 6.7
9 0.501 0.622 -0.121 -24.09
10 0.642 0.622 0.021 3.24
11 0.566 0.580 -0.014 -2.52
12 0.654 0.656 -0.002 -0.38
13 0.578 0.622 -0.043 -7.47
14 0.612 0.622 -0.010 -1.57
15 0.677 0.656 0.021 3.07
16 0.624 0.622 0.002 0.35
17 0.640 0.622 0.018 2.81
18 0.641 0.622 0.019 3.03
19 0.606 0.622 -0.016 -2.57
20 0.698 0.656 0.042 6.01
21 0.608 0.622 -0.014 -2.32
22 0.632 0.622 0.011 1.72
23 0.628 0.622 0.007 1.04
24 0.624 0.622 0.002 0.35
25 0.636 0.622 0.014 2.25
26 0.637 0.622 0.016 2.47
27 0.613 0.622 -0.008 -1.32
28 0.648 0.622 0.026 4.09
29 0.659 0.622 0.038 5.7
30 0.637 0.622 0.015 2.38
31 0.608 0.622 -0.014 -2.32
32 0.662 0.622 0.040 6.1
33 0.582 0.580 0.002 0.27
34 0.628 0.622 0.007 1.04
35 0.651 0.622 0.029 4.5
36 0.702 0.656 0.046 6.53
37 0.690 0.622 0.069 9.94
38 0.663 0.656 0.007 1.09
39 0.697 0.656 0.041 5.83
40 0.685 0.656 0.029 4.2
41 0.624 0.615 0.009 1.44
42 0.669 0.622 0.047 7.06
43 0.698 0.656 0.042 6.01
44 0.708 0.656 0.052 7.38
45 0.706 0.656 0.050 7.04
171
Appendix VIII An example of the pair-wise genetic distances estimated between
some of the accessions based on microsatellites data
First Second Actual Dendrogram Actual Percent
Row Row Distance Distance Difference Difference
2 0.305 0.305 0.000 0
3 0.403 0.430 -0.027 -6.69
4 0.647 0.624 0.023 3.5
5 0.550 0.624 -0.074 -13.55
6 0.647 0.624 0.023 3.5
7 0.629 0.624 0.004 0.71
1 8 0.591 0.611 -0.020 -3.44
1 9 0.571 0.611 -0.040 -7.07
10 0.629 0.611 0.018 2.84
11 0.647 0.611 0.036 5.57
12 0.647 0.611 0.036 5.57
13 0.591 0.611 -0.020 -3.44
14 0.647 0.611 0.036 5.57
15 0.715 0.611 0.104 14.59
16 0.591 0.611 -0.020 -3.44
17 0.665 0.611 0.054 8.09
18 0.665 0.611 0.054 8.09
19 0.629 0.611 0.018 2.84
20 0.610 0.611 -0.001 -0.15
21 0.506 0.611 -0.105 -20.79
1 22 0.591 0.611 -0.020 -3.44
1 23 0.647 0.611 0.036 5.57
24 0.699 0.611 0.088 12.58
25 0.699 0.611 0.088 12.58
26 0.610 0.611 -0.001 -0.15
27 0.571 0.611 -0.040 -7.07
28 0.629 0.611 0.018 2.84
29 0.571 0.611 -0.040 -7.07
30 0.629 0.611 0.018 2.84
31 0.610 0.644 -0.034 -5.51
32 0.665 0.644 0.021 3.18
33 0.682 0.644 0.038 5.63
34 0.550 0.644 -0.094 -17.05
1 35 0.699 0.644 0.055 7.91
1 36 0.682 0.644 0.038 5.63
37 0.665 0.644 0.021 3.18
38 0.665 0.644 0.021 3.18
39 0.699 0.644 0.055 7.91
40 0.610 0.611 -0.001 -0.15
41 0.550 0.611 -0.061 -11.11
1 42 0.665 0.611 0.054 8.09
1 43 0.682 0.611 0.071 10.42
44 0.647 0.644 0.003 0.53
45 0.647 0.644 0.003 0.53
172
Appendix IX Partial view of the sensory evaluation of the injera. (Ethiopian
and Eritrean students evaluating sorghum injera at the Sensory
Laboratory, UFS, South Africa).