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
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Identifying populations that have recently suffered a severe reduction in size is particularly important for their conservation as they are likely to suffer an increased risk of genetic erosion. We investigated the presence of recent bottlenecks in two wild sorghum populations from different eco-geographical conditions in Kenya employing 18 microsatellite markers. Microsatellite analysis showed high allelic diversity in the two populations, with a mean of 4.11 and 6.94 alleles per locus in the North-West wild sorghum population (NWWSP) and the South-East wild sorghum population (SEWSP), respectively. The mean observed heterozygosity was 0.34 and 0.56 in NWWSP and SEWSP, respectively. A large long-term effective populations size for both populations was observed assuming either an infinite allele model or a stepwise mutation model. There was no apparent loss of genetic variability for either of the populations. Test of heterozygosity excess indicated that a recent bottleneck in the two populations is highly unlikely. Furthermore, analysis of the allele frequency distribution revealed an L-shaped distribution which would not have been observed in case a recent bottleneck had reduced genetic variability in the two populations. The fact that most loci displayed a significant heterozygosity deficiency could be explained by population subdivision and the mixed mating system exhibited by wild sorghum populations. Furthermore, the possibility of a historical expansion of wild sorghum populations and presence of null alleles could not be ruled out.
KeywordsBottleneck Genetic diversity Linkage disequilibrium Microsatellite markers Null allele Sorghum bicolor ssp. verticilliflorum Wild sorghum population
This study was funded by the United States Agency for International Development (USAID) Biotechnology and Biodiversity Interface Program (BBI), the Institute of Plant Breeding and Population Genetics at the University of Hohenheim, Germany, and Germany Academic Exchange Service (DAAD: A0523923). We are grateful to Kenya Agricultural Research Institute and Ben Kanyenji who supervised the collection of genetic materials in full compliance with regulations according to the Convention on Biological Diversity (CBD).
- Chikhi L, Bruford MW (2005) Mammalian population genetics and genomics. In: Ruvinsky A, Marshall-Graves J (eds) Mammalian genomics. CABI Publishing, OxfordGoogle Scholar
- Cornuet JM, Luikart G (1997) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014Google Scholar
- Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38Google Scholar
- Franklin IR (1980) Evolutionary changes in small populations. In: Soulé ME, Wilcox BA (eds) Conservation biology: an evolutionary-ecological perspective. Sinauer Associates, Sunderland, Massachusetts, pp 135–150Google Scholar
- Mann JA, Kimber CT, Miller FR (1983) The origin and early cultivation of sorghums in Africa. Texas Agricultural Experiment Station, Bulletin 1454Google Scholar
- Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New YorkGoogle Scholar
- United Nations Environment Programme Convention on Biological Diversity (UNEPCD) SBSTTA (2003) Monitoring and indicators: designing national-level monitoring programmes and indicators, United NationsGoogle Scholar
- Waples RS (1991) Genetic methods for estimating the effective population size of cetacean populations. In: Hoelzel AR (ed) Genetic ecology of whales and dolphins. International Whaling Commission, Cambridge, pp 279–300 (Spec Issue No 13)Google Scholar