Genetic variability of sorghum landraces from lower Eastern Kenya based on simple sequence repeats ( SSRs ) markers

1 School of Agriculture and Enterprise Development, Department of Agricultural Science and Technology, Kenyatta University, P.O. Box 43844-00100, Nairobi, Kenya 2 School of Agriculture and Veterinary Sciences, Department of Dry Land Agriculture, South Eastern Kenya University, P.O. Box 170-90200, Kitui, Kenya. 3 Department of Biotechnology and Biochemistry, Kenyatta University, P.O. Box 43844-00100, Nairobi, Kenya. 4 International Maize and Wheat Improvement Centre CIMMYT Kenya, P.O. Box 1041-00621, Nairobi, Kenya.


INTRODUCTION
Sorghum (Sorghum bicolor (L.) Moench) is one of the important staple crops for the world's poorest and food insecure people in the semi-arid regions of the world (Timu et al., 2012;Rohrbach et al., 2002;Doggett, 1988).In Kenya, sorghum is an important food crop and dietary staple in the country's arid and semi-arid lands which account for over 80% of the total land area.Sorghum is uniquely cultivated due to its tolerance to drought, water logging, saline-alkali infertile soils and high temperature (Taylor, 2003).It has for a long time been considered as a crop of the resource-poor small-scale farmers and is grown predominately in arid and semi-arid lands (ASALs) (USAID, 2010).
All subspecies of S. bicolor are inter-fertile under sympatric conditions, leading to a continuum of wilddomesticate complex forms that have been documented to occur in many sorghum growing parts of Africa (Mutegi et al., 2010;Tesso et al., 2008;Dogget, 1988;Dogget and Majisu, 1968).Therefore, a wide genetic diversity is expected in the landraces of cultivated sorghum in Africa.
In lower Eastern Kenya, a wide diversity of sorghum landraces is cultivated under diverse agro-climatic conditions and practices by subsistence farmers (Muui et al., 2013;Mutegi et al., 2010).Farmers maintain sorghum landraces unique in their adaptation, grain yield, quality of harvested products, biotic stress resistance and in postharvest processing (Muui et al., 2013).
Sorghum with diverse morpho-types have been reported in many of the sorghum growing regions of Africa, often as indistinct races of S. bicolor that form a crop-wild-weed complex (Ejeta and Grenier, 2005;de Wet, 1978).Lower Eastern Kenya has a diverse of sorghum seed colorations, an indication of a possibility of early existence of crop-wild-weed complex of sorghum (Muui et al., 2013).Seed exchange among resource-poor small-scale farmers is a contributing factor to high variation among sorghum landraces (Tulole et al., 2009).
Farmers grow a mixture of several sorghum landraces per field (Muui et al., 2013;Barnaud et al., 2007).Over time, outcrossing occurs in sorghum though variable among different landraces (Barnaud et al., 2008).Also, selection exerted by farmers is a key parameter for determining the fate of new genetic combinations from the outcrossing events and thus in the patterns of genetic differentiation among landraces.Landraces perform well under sub-optimal conditions as they are well adapted to local stresses and possesses farmers' preferable traits (Bantilan et al., 2004;Setimela et al., 2004).It is, therefore, necessary to study the genetic relationships of these landraces and identify traits to be incorporated in the released varieties.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 germplasm as possible sources of genes that can improve the performance of cultivars, and for determining the uniqueness and distinctness of the genetic constitution of genotypes (Subudhi et al., 2002).
Levels and patterns of diversity within and between cultivated and wild sorghum gene pools have been reported (Cui et al., 1995;Deu et al., 1995;Casa et al., 2005).In Kenya, studies were done to establish the extent and direction of introgression between cultivated Muui et al. 265 and wild sorghum relatives (Mutegi et al., 2010).However, the extent of genetic diversity within and between landraces grown by farmers in different agroecological zones of lower Eastern Kenya has not been established.Our study therefore applied microsatellite markers to analyze cultivated sorghum sampled from different growing regions in lower Eastern Kenya, in order to elucidate patterns of diversity.The objective of the study was to determine the genetic relationships and thus establish the potential for landraces as sources of breeding material for future sorghum improvement.

MATERIALS AND METHODS
This research was conducted at the lower Eastern Kenya which extends between 38° 15 E and 39° 30 E as well as 1° N and 3° S.
The study covered four regions of lower eastern varying in agroclimatology, namely Mbeere, Makueni, Kitui, and Mutomo, which are major sorghum growing areas in Kenya.The regions range from zone IV (semi humid to semi arid) to zone V (semi arid) (Jaetzold and Schmidt, 1983).The Mbeere and Kitui sites are classified as Lower Midland (LM) with some regions in transitional zone towards Upper Midland (UM).Makueni and Mutomo sites are classified as LM (Jaetzold et al., 2006).
Forty four sorghum landraces collected from farmers in the study region and four commercially released cultivars were used to assess the genetic diversity (Table 1).Seeds for each accession were sown in trays containing soil and seedlings raised under standard glasshouse conditions at International Crops Research Institute for Semi-Arid Tropics (ICRISAT), World Agroforestry campus, Nairobi between July and September, 2012.Leaves were taken from two weeks old individual plants, from each accession.
DNA extraction was done using cetyl trimethyl ammonium bromide (CTAB) according to Mace et al. (2003).The quality of genomic DNA was assessed using agarose (0.8%) gel and quantification was done using a spectrophotometer nanodrop according to Mace et al. (2003).Polymerase chain reaction (PCR) amplifications were performed in 60 µl reaction mixture.Twenty primers of known sequence were used in amplification of the 48 samples (Billot et al., 2012).The amplification was carried out using the profile developed by Folkertsma et al. (2005).The PCR product was then loaded on 2% agarose gel and DNA fragments were visualized by illumination device with UV light.The success of amplification was indicated by the presence of one or two sharp *Corresponding author.E-mail: catherinemuui@gmail.com Author(s) agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License

SSR Polymorphism
Polymorphism among the 48 sorghum genotypes was assessed with 20 SSR markers.All the 20 SSR markers used were polymorphic across the 48 sorghum genotypes with PIC value ranging from 0.04 to 0.81 with a mean of 0.49.Of the 20 markers, 65% were highly polymorphic with a value greater than 0.5 indicating their usefulness in discriminating the genotypes (Table 2).Heterozygosity values of the 20 SSR markers ranged from 0.00 to 0.04, with a mean of 0.01 suggesting that each detected a single genetic locus and that each of the sorghum accession used was stable.The markers revealed a total of 98 alleles with a range between 2 (gpsb067, mSbCIR24 and Xcup53) and 10 (Xtxp320) alleles and an average of 5.05 alleles per primer pair.
The gene diversity ranged from 0.04 to 0.83 with a mean value of 0.53.All possible allele combinations found in the 48 accessions ranged from 2 to 10, while the major allele frequency ranged from 0.32 to 0.98 (Table 2).The weighted neighbour-joining clustering-based dendrogram generated using dissimilarity indices clustered the sorghum accessions into four major groups (Figure 2).Cluster 1 comprised of genotypes from Kitui (west and central), Mbeere (Siakago and Kiritiri), Makueni (Kibwezi, Makindu and Kiboko) and Seredo (commercial variety).The genotypes varied in color from white, brown white, brown, red, black red and purple, but distributed across the four regions.The cluster had three subgroups with genotype black red from mutomo very distinct from other genotypes.Cluster 2 had genotypes from Mbeere (Kiritiri, Siakago), Makueni (Kibwezi), KARI Mtama 1 and Serena commercially released varieties.The cluster had four subgroups with genotypes 7 and 8 from Mbeere Kiritiri grouped together distinctly.Cluster 3 had genotypes from Makueni (Makindu, Kibwezi), Mbeere (Kiritiri, Ishiara, Siakago), Kitui (west and central) and Mutomo; and the commercial variety Gaddam.The cluster had five sub groups with seed color varying greatly.Cluster 4 consists of three genotypes from Mbeere (Kiritiri), Makueni (Kiboko) and Kitui central.Genotype 26 from Mbeere (Kiritiri) and 24 from Kitui central had a closer relationship than with 25, though in the same cluster (Figure 2).Genetic diversity among accessions was also confirmed by scatter plot derived through PCoA (Figure 3).Forty percent of the accessions were clustered in the right portions of the plot, while 60% accessions were clustered in the left portion of the plot (axes1/2).Groupings were similar to those detected by cluster analysis where the genotypes were clearly separated across the region except for the genotype 9 from Makueni (Makindu) and genotype 11 from Mbeere (Kiritiri) which had an overlap.Genotypes 20 from Makueni (Kiboko) and 33 from Mbeere (Kiritiri) followed by genotypes 27 from Mbeere (Siakago) and 44 from Kitui central were far much apart from other genotypes; an indication of maximum dissimilarity.Genotypes 11, 13 and 3 formed a solidarity group implying high relatedness in the genetic makeup.This was also observed in genotypes 10 from Makueni (Kibwezi), 22 from Kitui central, 28 from Mutomo, 3 (commercial), 30 from Kiboko and 4 from Mbeere Siakago (Figure 3).
AMOVA showed significant (P = 0.05) differences among the various genotypes evaluated (Table 3).There was a greater variance (91.61%) represented by individuals within populations, while the variance between the groups was less (2.75%) with least variance (1.14%) expressed by the individuals.

DISCUSSION
The genetic diversity among the sorghum accessions used in this study was high as indicated by PIC and gene diversity values.The PIC of a SSR marker gives an idea about the discriminatory power of that marker by taking into account the number of alleles detected and their relative frequencies (Smith et al., 2000).Markers with PIC more than 0.5 are efficient in discriminating genotypes and extremely useful in detecting the polymorphism rate at a particular locus (DeWoody et al., 1995).
Sorghum is primarily an inbreeding species resulting in a low level of observed heterozygosity, but the gene pool as a whole maintains a high level of allelic variation.The   (Ghebru et al., 2002).The low heterozygosity was a clear indication the genotypes were homozygous and thus a high level of stability in the population.Genetic distances among the 48 genotypes varied greatly indicating a wide diversity.Genotype Karuge 2 and Karuge 1 both from Mbeere (Kiritiri) had a low genetic distance (0.15).Though, the seed color and geographical location of the two accessions was identical, the two are totally different genetically.The results in this study suggest that diversity of the landraces were structured more on geographical locations and on seed colorations than agro-ecological conditions.Reports of other studies in sorghum accessions have shown grouping primarily on the basis of origin, and clustering within groups as driven by racial classification (Sharma

Figure 1 .
Figure 1.Map showing the study area in lower Eastern Kenya.

Figure 2 .
Figure 2. Tree constructed based on 20 polymorphic sorghum SSR markers using the simple matching dissimilarity index and weighted neighbor joining clustering for the 48 sorghum accessions.

Figure 3 .
Figure 3. Plot of the axes 1 and 2 of the principal coordinate analysis based on the dissimilarity of 20 SSR markers for 44 sorghum accessions obtained from farmers in lower eastern Kenya and 4 improved varieties.

Table 1 .
Reference numbers for 44 landraces and 4 improved sorghum seed accessions used in diversity experiment.
bands within the size range of up to 100 bp.Simple sequence repeats (SSR) was done by loading the PCR products for DNA fragments denaturation and size fractioning using capillary electrophoresis.Fragment size fractioning was done using ABI 3730 automatic DNA sequencer (Perkin Elmer-Applied Biosystems).Genemapper software Version 4.0 (Perkin Elmer-Applied Biosystems) was applied to size peak patterns, using the internal ROX 400 HD size standard and for allele calling.

Table 2 .
Characteristics of 20 SSR markers across 44 sorghum landraces and 4 commercial varieties.

Table 3 .
Analysis of molecular variation (AMOVA) of 4 sorghum improved varieties and 44 landraces from farmers in different locations of Mbeere, Mutomo, Kitui and Makueni based on 20 SSR markers.