Genetic Diversity of Wild Sorghum (Sorghum bicolor ssp. verticilliflorum
(L.) Moench) Germplasm from Ethiopia as Revealed by ISSR Markers
Ethiopia is one of the center of origin and diversity of Sorghum
bicolor (L.) Moench. The genus Sorghum has been divided into three
subspecies, namely, bicolor, verticilliflorum and drummondii.
The study of the genetic diversity of the wild species of sorghum significantly
contributes to the improvement of Sorghum. The aim of study was to investigate
the genetic diversity of wild Sorghum from Ethiopia using Inter-Simple
Sequence Repeat markers (ISSR) markers. ISSR were used to estimate genetic diversity
among and within 12 populations of wild sorghum that were collected from different
ecological zones of Ethiopia. Seven selected ISSR primers yielded 43 reproducible
bands from 96 individuals, eight individuals representing each population. Jimma
1 population exhibited highest gene diversity of 0.2885. The least gene diversity
(0.1192) was observed in East Wollega 1. Shannon diversity index also showed
the same diversity pattern where Jimma 1 population showed the highest value
(0.4067) and the least Shannon diversity index was exhibited by East Wollega
1 population (0.1757). Analysis of molecular variance showed that within population
variation was higher (54.68%) than among population variation (45.32%). Un-weighted
pair group method with arithmetic mean (UPGMA) based dendrogram showed Gambella
1 and Jimma 2 populations showed highest similarity. Individual based UPGMA
clustering of overall analysis showed five major clusters and some of individuals
were clustered with respect to their populations except few intermixed individuals
from other populations. In two dimensional (2 D) Principal coordinate analysis
(PCO), most of West Wollega 1 and West Wollega 2 individuals formed their separate
group except few individuals from other populations mixed with them.
Received: May 09, 2013;
Accepted: June 25, 2013;
Published: September 19, 2013
Sorghum bicolor (L.) Moench is divided into three subspecies (ssp.),
namely, bicolor (the domesticated forms) verticilliflorum (the
closest wild relative of the domesticated spp.) and drummondii (the weedy
forms which are hybrids between wild relatives and cultivated forms) (Doggett,
1988). Subspecies bicolor have four botanical races, bicolor, kafir,
caudatum, durra and guinea and ten intermediate races (Harlan
and de Wet, 1972). The four wild races, verticilliflorum, arundinaceum,
virgatum and aethiopicum are recognized in the subspecies Verticilliflorum
(James et al., 2012). Subspecies, Verticilliflorum
differ from each other by leaves, inflorescence and ecogeographical distribution
of the plant. Verticilliflorum is widely spread race in sub-Saharan Africa
(De Wet and Huckabay, 1967; De
Wet et al., 1970; De Wet, 1978).
The depletion of wild sorghum populations in Ethiopia is due to the growing
human population pressure and recurrent drought that forced farmers to cultivate
the most marginal lands formerly occupied by wild sorghum and its habitat is
used for agriculture, grazing, human settlement, construction, etc. The increase
in human population and consequently increase in agricultural activities are
the main threat that affected the distribution of wild sorghum in Ethiopia (Ayana
et al., 2000). In addition, farmers= practices in relation to their
management of sorghum wild relatives largely contributed to the decrease in
wild populations (Ayana et al., 2000; Tesso
et al., 2008). Hence, the conservation of these wild sorghum populations
should be given due attention. For effective conservation of these wild sorghum
populations, Evaluation of its genetic diversity and frequent monitoring of
its genetic change is needed (Muraya et al., 2010).
The methods of genetic diversity study extend from analysis of morphological
character to biochemical and molecular traits (Muthusamy
et al., 2008). Traditional classification mainly based on morphological
characteristics. However, these traits may not be distinct and usually require
growing plants to maturity prior to identification. Morphological characters
are unstable because they are easily influenced by environment. Wild sorghum
has a great potential both from economic and ecological perspectives. However,
there have been only few researches on its genetic diversity. Ayana
et al. (2000) studied the molecular genetic diversity of wild sorghum
from Ethiopia using RAPD whereas Adugna and Bekele (2013)
studied phenotypic diversity. Moreover, the former study using RAPD was carried
out by pooling samples of accessions and no report on within accessions genetic
diversity. The objective of present study is to investigate the genetic diversity
of wild sorghum from Ethiopia using ISSR markers.
MATERIALS AND METHODS
Plant material: Twelve accessions of wild sorghum that were collected
from different ecological zones of Ethiopia (Fig. 1) were
obtained from Institute of Biodiversity Conservation (IBC), Ethiopia. Eight
individuals from each accession were planted in glasshouse to collect young
leaves for DNA extraction.
DNA extraction and PCR amplification: Total genomic DNA was isolated
by using modified CTAB method of Wang et al. (1996)
with minor modifications. Quality of the DNA was checked by electrophoresis
of the samples on 1% agarose gel and staining with ethidium bromide. DNA concentration
was determined by nanodrop.
A total of 20 primers (Sigma-Aldrich) were screened to get the primers that
are polymorphic and reproducible. Two individuals were selected from each population
to screen the primers. A total of seven polymorphic and reproducible ISSR primers
were selected (Table 1). The polymerase chain reaction was
conducted in a TC-412 version 34.11 block 96H0.2 mL Thermocycler. PCR amplification
was carried out in a 25 μL volume containing 1 μL of 20 ng μL-1
template DNA, 17.97 μL H2O, 100 mM of 0.2 μL dNTPs, 2.5
μL of 10x buffer (25 mM of MgCl2, 100 mm of Tirs-Cl, 500 mM
KCl, pH at 9.1, 0.1% Triton), 2.5 μL of 25 mM MgCl2, 0.5 μL
primer (20 pmol μLG1) and 0.33 μL (5 U) Taq Polymerase.
The amplification program was initial denaturation at 94°C for 5 min, denaturation
at 94°C, primer annealing at (45/49°C) depending on primers for 1 min
followed by 2 min extension at 72°C with a final extension of 7 min at 72°C.
||Map of Ethiopia showing the collection sites of wild sorghum
A-3: West Gojam, A-6: East Wollega 1, A-7: East Wollega 2, A-8: East Wollega
3, A-15: West Wellega 1, A-16: West Wellega 2, A-18: Gambella 1, A-19: Gambella
2, A-21: Jimma 1, A-22: Jimma 2 and A-23: Jimma 3
||Primers selected and used, their sequence, annealing temperature
(°C), amplification pattern, number of bands scored and repeat motives
|Single-letter abbreviations for mixed base positions: Y: (C,T),
inter simple sequence repeat (ISSR)
The amplified DNA fragments (10 μL) was mixed with 2 μL of 6x loading
dye and separated by electrophoresis on 1.7% agarose in 1x TAE buffer. DNA ladder
of 100 bp was used to estimate molecular weight. Electrophoresis was conducted
for 3 h at constant voltage of 100 V. DNA was stained with 50 μL of 10
mg mL-1 Ethidium Bromide in 250 mL of double distilled H2O
for 30 min and washed for 30 min and the picture was taken using gel documentation
Data scoring and analysis: ISSR bands were scored visually/manually
for each individual sample from the gel photograph. The bands were recorded
as discrete characters, presence 1 or absence 0 and ? for missing
data. Data analysis was conducted using only the polymorphic bands. Different
softwares were used for data analysis. POPGENE version1.32 software (Yeh
et al., 1997) was used to calculate genetic diversity for each population
as number of polymorphic loci (NPL), Percent Polymorphism (PP), Gene Diversity
(GD) and Shannon diversity index (I). Analysis of molecular variance (AMOVA)
was used to calculate variation among and within populations using Areliquin
version 3.01 (Excoffier et al., 2006). NTSYS-
pc version 2.02 (Rohlf, 2000) and Free Tree 0.9.1.50
(Pavlicek et al., 1999) software=s were used to
calculate Jaccard=s similarity coefficient. The Unweighted Pair Group Method
with Arithmetic mean (UPGMA) (Sneath and Sokal, 1973)
was used to analyze and compare the population and generate phenogram using
NTSYS- pc version 2.02 (Rohlf, 2000). The Neighbor Joining
(NJ) method (Saitou and Nei, 1987; Studier
and Keppler, 1988) was used to compare individual genotypes and evaluate
patterns of genotype clustering using Free Tree 0.9.1.50 Software (Pavlicek
et al., 1999).
To further examine the patterns of variation among individual samples on 3D,
a principal coordinated analysis (PCO) was performed based on Jaccard=s coefficient
(Jaccard, 1908). The calculation of Jaccard=s coefficient
was made with PAST software version 1.18 (Hammer et al.,
2001). The first three axes were used to plot the three dimensional PCO
with STATISTICA version 6.0 software (Hammer et al.,
2001; Statistica Soft, 2001).
Banding patterns of the ISSR primers: The size of the bands amplified
was in the range of 200 and 800 bp but most of them have size range between
300 and 500 bp. A total of 43 clear and reliable bands were scored. Primer 810
produced the eight polymorphic bands which is the largest number whereas primers
873 and 880 exhibited five polymorphic bands which is the least. Primers 824,
825 and 827 produced six polymorphic bands whereas primer 841 produced seven
polymorphic bands. Average number of bands amplified per primer was 6.143.
Genetic diversity: The highest genetic diversity was exhibited by Jimma
1 (A-21) population with gene diversity value of 0.2885, followed by Gambella
2 (A-19) population with gene diversity value of 0.2594. The least gene diversity
was exhibited by East Wollega 2 (A-7) population. Shannon diversity index also
showed the same diversity pattern where Jimma 1 and Gambella 2 populations showed
the highest value with 0.4067 and 0.3694, respectively and the least Shannon
diversity index was exhibited by East Wollega 2 population with the value of
0.1757 (Table 2).
Analysis of molecular variance: Analysis of molecular variance (AMOVA)
was carried out on overall ISSR data scored from wild sorghum populations without
grouping. AMOVA without grouping the populations revealed that higher percentage
of variation (54.68%) is attributed to the within population variation while
the remaining variation is due to among population variation which was 45.32%.
Clustering analysis: UPGMA cluster analysis formed two clusters, I and
II. Cluster I further formed two sub clusters and cluster II formed three sub
clusters. In addition, three populations, West Gojam (A-3), West Wollega 1 (A-15)
and West Wollega 2 (A-16) failed to cluster with other groups. While from the
rest nine populations seven were grouped together, East Wollega 1 (A-6), Gambella
1 (A-18), Gambella 2 (A-19), Jimma 1 (A-21), Jimma 2 (A-22), Jimma 3 (A-23)
and East Shewa (A-24) and the remaining two populations, East Wollega 2 (A-7)
and East Wollega 3 (A-8) formed separate group.
|| Number of polymorphic loci (NPL), percent polymorphism (PP),
genetic diversity (GD) and Shannon index (I) for each population
|| Pairwise Jaccard similarity coefficient based comparisons
among 12 populations of wild sorghum
|A-3: West Gojam, A-6: East Wollega , A-7: East Wollega , A-8:
East Wollega , A-15: West Wellega, A-16: West Wellega, A-18: Gambella 1,
A-19: Gambella 2, A-21: Jimma , A-22: Jimma , A-23: Jimma , A-24: East Shewa
||UPGMA based dendrogram for 12 populations of wild sorghum
collected from different ecological zones of Ethiopia A-3: West Gojam, A-6:
East Wollega 1, A-7: East Wolleg 2a, A-8: East Wollega 3, A-15: West Wellega
1, A-16: West Wellega 2, A-18: Gambella 1, A-19: Gambella 2, A-21: Jimma
1, A-22: Jimma 2, A-23: Jimma 3, A-24: East Shewa
Gambella 1 (A-18), Jimma 2 (A-22) were not separated (Fig. 2).
On the other hand, individual based NJ clustering of overall analysis showed
individuals were clustered with respect to their populations except few intermixed
individuals from other populations (Fig. 3). Pairwise Jaccard
similarity coefficient-based comparisons of the 12 populations showed Gambella
1 (A-18) and Jimma 2 (A-22) are closest with similarity coefficient of 0.815,
whereas West Gojam (A-3) and East Wollega 2 (A-8) are the most distant populations
with similarity coefficient of 0.45 (Table 3).
||Neighbor-joining analysis of 96 individuals of wild sorghum
amplified by seven ISSR primers A-3: West Gojam, A-6: East Wollega 1, A-7:
East Wollega 2, A-8: East Wollega 3, A-15: West Wellega 1, A-16: West Wellega
2, A-18: Gambella zone 1, A-19: Gambella zone 2, A-21: Jimma 1, A-22: Jimma
2, A-23: Jimma 3 and A-24: East Shewa
Principal coordinate analysis: The first three coordinates of the PCO
having eigen values of 7.3199, 4.1255 and 3.0232 with variance of 19.595, 11.044
and 8.093%, respectively were used to reveal the grouping of individuals using
two and three coordinates. In 2D, most of West Wollega 1 (A-15) and West Wollega
2 (A-16) individuals formed their separate group except few individuals mixed
with them. In 3D, most of East Wollega 2 (A-7), West Wollega 2 (A-16) Gambella
1 (A-18) individuals tended to form their own grouping and the rest were inter-mixed
with each other and formed separate group with other populations. Except populations
from West Gojjam, PCO in 2D showed similar result with that of NJ and UPGMA
||2D representation of principal coordinate analysis of genetic
relationships among 96 individuals of 12 wild sorghum populations
The present study generated 43 loci using seven primers where 42 of the loci
(93.33%) were polymorphic. The percent polymorphism for each primer ranged from
40-100%. This is in agreement with previous studies on the cultivated sorghum
with 84.0% polymorphism reported by Fang et al. (2008).
In the present study, primer 880 (penta-nucleotide repeat), showed the least
percent polymorphism. Fang et al. (2008) reported
among 22 primers selected for cultivated sorghum analysis, di-nucleotide repeats
were found to be more abundant in sorghum.
When ecological zone based analysis was considered, Jimma 1 (A-21) showed highest
percent polymorphism (62.22%) followed by Gambella 2 (A-19), (57.78%). Both
gene diversity and Shannon diversity index exhibited the same pattern of diversity.
Jimma 1 (A-21) and Gambella 2 (A-19) populations showed higher diversity and
East Wollega 2 (A-7) East Shewa (A-24) and Gambella 1 (A-18) populations showed
less gene diversity and Shannon diversity index. Previous study by Ayana
et al. (2000) on wild sorghum from Ethiopia using RAPD marker indicated
that populations from Jimma area exhibited 0.47% Shannon diversity index and
those from Gambella showed 0.5 gene diversity. The present study agrees with
this previous one as populations from these two areas exhibited higher Shannon
diversity index and gene diversity. The difference in gene diversity among population
is due to evolutionary forces that include genetic drift, mutation and selection
(Papa, 2005). Process of migration is driving the changes
in gene frequencies in natural populations.
Gene flow affects genetic diversity in populations of crop landraces and progenitors/the
wild relatives (Gepts and Papa, 2003). With regard
to genetic diversity, the wild progenitors of crops are potentially containing
more diversity than their respective crops. Domestication has induced marked
bottlenecks in genetic diversity of crops (Gepts, 1993).
Higher levels of genetic diversity in the wild compared to its cultivated congener
which suggests that the process of domestication reduced levels of genetic variation
in sorghum. Wild sorghum is thus a potential source of novel genes for broadening
the genetic base of cultivated sorghum (Evan, 2009).
But in our study in some areas such as East Wollega 2 (A-7) the genetic diversity
is very low probably due to the reduction in wild sorghum population as a result
of farm land expansion.
Wild sorghum species grow in and around sorghum crop fields in all regions
of Ethiopia (Tesso et al., 2008). Generally,
gene flow, migration and genetic drift may be the possible reason for difference
in genetic diversity. Samples from East Wollega 2 (A-7), East Shewa (A-24) and
Gambella 1 (A-18) showed low genetic diversity. This could be due to the reason
that samples were collected from cultivated sorghum field. Geographic differences
in the distribution of genetic diversity are common. Populations may differ
with respect to all aspects of diversity and show variation in the number of
alleles, the identity of those alleles and the effect they have on the characteristics
in the population (Rao and Hodgkin, 2002).
AMOVA analysis resulted in higher genetic diversity within populations (54.68%)
and lower genetic diversity among populations (45.32%). Genetic diversity analysis
of wild sorghum by RAPD (Ayana et al., 2000)
showed similar result with the present study that is within population diversity
is higher than among population. Within population variation is greater due
to (1) Selection for adaptation to microclimatic conditions, (2) The evolutionary
advantage of broad genetic variation in a long-lived species growing under varying
weather and climatic conditions and (3) High gene flow from other populations,
mainly via., the long-distance transfer of pollen (Eriksson,
1998). Migration of individuals (e.g., seeds) or gametes (e.g., pollen)
between populations counteracts this divergence between populations, causing
a reduction in the genetic diversity among populations and increasing the level
of genetic diversity within populations (Papa, 2005).
UPGMA group dendrograms showed samples from East Wollega 1 (A-6), Jimma 1 (A-21),
Jimma 2 (A-22), Jimma 3 (A-23), Gambella 1 (A-18), Gambella 2 (A-19) and East
Shewa (A-24) clustered together. Samples from imma2 (A-22) and Gambella 1 (A-18)
merged together and were not separated and (Ayana et
al., 2000) also obtained similar results. This may be due to samples
from these two different areas have the same origin. In addition, samples from
East Wollega 2 (A-7) and East Wollega 3 (A-8) also clustered together. However,
samples from West Gojam (A-3) West Wellaga1 (A-15) and West Wollega 2 (A-16)
formed separate groups.
Both UPGMA and NJ indicated that most of the individuals clustered within their
groups except few individuals which were distributed throughout other populations.
These cosmopolitan alleles may be due to ancestral characters shared by most
population through common descent from the progenitor of Sorghum bicolor.
Wild sorghum has a great potential both from economic and ecological perspectives.
However, continued pressure due to expansion of agricultural land, grazing and
human settlement have negatively affected the genetic diversity of the species.
Therefore, researchers, policy makers and other stakeholders need to come-up
with sustainable conservation strategies.
From the 12 populations, samples from Jimma 1 (A-21) and Gambella 2 (A-19)
populations showed higher gene diversity and Shannon diversity index while a
populations from East Wollega 1 (A-7) has the least gene diversity and Shannon
diversity index. Analysis of molecular variance showed that the higher proportion
of genetic variation was attributed to within population than among population.
UPGMA based dendrogram for 12 populations of wild sorghum from 12 different
ecological zone of Ethiopia by using seven ISSR primers showed samples from
Gambella 1(A-18) and Jimma 2 (A-22) populations were closely related than others
and in addition Pairwise Jaccard Similarity Coefficient-based comparisons of
the 12 populations showed Gambella 1 (A-18) and Jimma 2 (A-22) are closest whereas
West Gojam (A-3) and East Wollega 2 (A-7) are the most distant populations.
This study was supported by Bioresources Innovations Network for Eastern Africa
Development (BIO-INNOVATE) Program funded by SIDA, Sweden. The authors acknowledge
Institute of Biodiversity Conservation, Ethiopia, for providing the sorghum
germplasm used in this study. The authors also acknowledge Dr. Amsalu Ayana
for his comments during designing of the experiment.
1: Adugna, A. and E. Bekele, 2013. Geographical distribution and phenotypic diversity of wild/weedy sorghum [Sorghum bicolour (L.) Moench] in Ethiopia: Implications for germplasm conservation and crop-wild gene flow. Plant Genet. Res., 11: 68-76.
2: Ayana, A., E. Bekele and T. Bryngelsson, 2000. Genetic variation in wild sorghum (Sorghum bicolor ssp. Verticilliflorum (L.) Moench) germplasm from Ethiopia assessed by Random Amplified Polymorphic DNA (RAPD) Hereditas, 132: 249-254.
CrossRef | Direct Link |
3: De Wet, J.M.J., 1978. Systematic and evolution of Sorghum sect. Sorghum (Gramineae). Am. J. Bot., 65: 477-484.
Direct Link |
4: De Wet, J.M.J. and J.P. Huckabay, 1967. The origin of Sorghum bicolor II: Distribution and domestication. Evolution, 21: 787-802.
Direct Link |
5: De Wet, J.M.J., J.R. Harlan and E.G. Price, 1970. Origin of variability in the spontanea complex of Sorghum bicolor. Am. J. Bot., 57: 704-707.
Direct Link |
6: Doggett, H., 1988. Sorghum. Wiley, New York, USA
7: Eriksson, G., 1998. Evolutionary forces influencing variation among populations of Pinus sylvestris. Silva Fennica, 32: 173-184.
Direct Link |
8: Excoffier, L., G. Laval and S. Schneider, 2006. Computational and molecular population genetics lab (CMPG). Institute of Zoology University of Berne Baltzerstrasse 63012 Bern Switzerland.
9: Gepts, P. and R. Papa, 2003. Possible effects of (trans)gene flow from crops on the genetic diversity from landraces and wild relatives. Environ. Biosafety. Res., 2: 89-103.
PubMed | Direct Link |
10: Gepts, P., 1993. The Use of Molecular and Biochemical Markers in Crop Evolution Studies. In: Evolutionary Biology, Hecht, M.K. (Edn.). Plenum Press, New York, pp: 51-94
11: Hammer, O., D.A.T. Harper and P.D. Ryan, 2001. PAST: Paleontological statistics software package for education and data analysis. Paleontol. Electron., 4: 1-9.
Direct Link |
12: Harlan, J.R. and J.M.J. de Wet, 1972. A simplified classification of cultivated sorghum. Crop Sci., 12: 172-176.
Direct Link |
13: Jaccard, P., 1908. Nouvelles researchers Sur la distribution florale. Bull. Soc. Vaud. Sci. Nat., 44: 223-270.
14: James, A.O., E. Mutegi, S. de Villiers, D.W. Jeffrey and K.M. Manjit, 2012. Morphological variation in the wild-weedy complex of Sorghum Bicolour in situ in western Kenya: Preliminary evidence of crop-to-wild gene flow? Int. J. Plant Sci., 173: 507-515.
Direct Link |
15: Muraya, M.M., F. Sagnard and H.K. Parzies, 2010. Investigation of recent population bottlenecks in Kenyan wild sorghum populations (Sorghum bicolor (L.) Moench ssp. verticilliflorum (Steud.) De Wet) based on microsatellite diversity and genetic disequilibria. Genet. Resour. Crop. Evol., 57: 995-1005.
Direct Link |
16: Evan, M., 2009. Crop-to-wild geneflow: Environmental risk assessment for the release of genetically modified sorghum in Kenya. Ph.D. Thesis, University of the Free State, Bloemfontein, South Africa.
17: Muthusamy, S., S. Kanagarajan and S. Ponnusamy, 2008. Efficiency of RAPD and ISSR marker system in accessing variation of rice bean (Vigna umbellate) land race. Electron. J. Biotechnol., Vol. 11.
Direct Link |
18: Papa, R., 2005. Gene flow and introgression between domesticated Crops and their wild relatives. The Role of Biotechnology, Turin, Italy
19: Pavlicek, A., S. Hrda and J. Flegr, 1999. Free-Tree-freeware program for construction of phylogenetic trees on the basis of distance data and bootstrap/jackknife analysis of the tree robustness. Application in the RAPD analysis of genus Frenkelia. Folia Biol. (Praha), 45: 97-99.
PubMed | Direct Link |
20: Rao, V.R. and T. Hodgkin, 2002. Genetic diversity and conservation and utilization of plant genetic resources. Plant Cell Tissue Org. Cult., 68: 1-19.
Direct Link |
21: Rohlf, F.J., 2000. NTSYS-PC, Version 2.11T. Exeter Software, New York, USA
22: Saitou, N. and M. Nei, 1987. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol., 4: 406-425.
CrossRef | PubMed | Direct Link |
23: Sneath, P.H.A. and R.R. Sokal, 1973. Numerical Taxonomy. W.H. Freeman and Company, San Francisco, USA., ISBN: 0-7167-0697-0
24: Statistica Soft, 2001. Statistica Data Analysis System. Statistica Soft, Inc., USA
25: Studier, J.A. and K.J. Keppler, 1988. A note on the neighbor-joining algorithm of Saitou and Nei. Mol. Biol. Evol., 5: 729-731.
PubMed | Direct Link |
26: Tesso, T., I. Kapran, C. Grenier, A. Snow and P. Sweeney et al., 2008. The potential for crop-to-wild gene flow in sorghum in Ethiopia and Niger: A geographic survey. Crop Sci., 48: 1425-1431.
Direct Link |
27: Wang, M., B.J. Oppedijk, X. Lu, B. van Duijn and R.A. Schilperoort, 1996. Apoptosis in barley aleurone during germination and its inhibition by abscisic acid. Plant Mol. Biol., 32: 1125-1134.
CrossRef | Direct Link |
28: Fang, X.E., Q. Chen, L.P. Yin and W. Wang, 2008. Application of ISSR in genetic relationship analysis of sorghum species. Acta. Agronomi Sin., 34: 1480-1483.
CrossRef | Direct Link |
29: Yeh, F.C., R.C. Yang, T.B.J. Boyle, Z.H. Ye and J.X. Mao, 1997. POPGENE, The user friendly software for population genetic analysis. Molecular Biology and Biotechnology Center. University of Alberta, Canada.