The objective of this study was to investigate the population structure of village chickens found in the five agro-ecological zones of Zimbabwe. Twenty-nine microsatellites were genotyped for chickens randomly selected from 13 populations, including the five eco-zones of Zimbabwe (n = 238), Malawi (n = 60), Sudan (n = 48) and six purebred lines (n = 180). A total of 280 alleles were observed in the 13 populations. Forty-eight of these alleles were unique to the Zimbabwe chicken ecotypes. The average number (+/-SD) of alleles/locus was 9.7 +/- 5.10. The overall heterozygote deficiency in the Zimbabwe chickens (F(IT) +/- SE) was 0.08 +/- 0.01, over 90% of which was due to within-ecotype deficit (F(IS)). Small Nei's standard genetic distances ranging from 0.02 to 0.05 were observed between Zimbabwe ecotypes compared with an average of 0.6 between purebred lines. The structure software program was used to cluster individuals to 2 = K = 7 assumed clusters. The most probable clustering was found at K = 6. Ninety-seven of 100 structure runs were identical, in which Malawi, Sudan and purebred lines split out as independent clusters and the five Zimbabwe ecotypes clustered into one population. The within-ecotype marker-estimated kinships (mean = 0.13) differed only slightly from the between-ecotype estimates. Results from this study lead to a rejection of the hypothesis that village chickens are substructured across agro-ecological zones but indicated high genetic diversity within the Zimbabwe chicken population.
This study sought to assess mitochondrial DNA (mtDNA) diversity and phylogeographic structure of chickens from five agro-ecological zones of Zimbabwe. Furthermore, chickens from Zimbabwe were compared with populations from other geographical regions (Malawi, Sudan and Germany) and other management systems (broiler and layer purebred lines). Finally, haplotypes of these animals were aligned to chicken sequences, taken from GenBank, that reflected populations of presumed centres of domestication. A 455-bp fragment of the mtDNA D-loop region was sequenced in 283 chickens of 14 populations. Thirty-two variable sites that defined 34 haplotypes were observed. In Zimbabwean chickens, diversity within ecotypes accounted for 96.8% of the variation, indicating little differentiation between ecotypes. The 34 haplotypes clustered into three clades that corresponded to (i) Zimbabwean and Malawian chickens, (ii) broiler and layer purebred lines and Northwest European chickens, and (iii) a mixture of chickens from Zimbabwe, Sudan, Northwest Europe and the purebred lines. Diversity among clades explained more than 80% of the total variation. Results indicated the existence of two distinct maternal lineages evenly distributed among the five Zimbabwean chicken ecotypes. For one of these lineages, chickens from Zimbabwe and Malawi shared major haplotypes with chicken populations that have a Southeast Asian background. The second maternal lineage, probably from the Indian subcontinent, was common to the five Zimbabwean chicken ecotypes, Sudanese and Northwest European chickens as well as purebred broiler and layer chicken lines. A third maternal lineage excluded Zimbabwean and other African chickens and clustered with haplotypes presumably originating from South China.
BackgroundThe detection of selection signatures in breeds of livestock species can contribute to the identification of regions of the genome that are, or have been, functionally important and, as a consequence, have been targeted by selection.MethodsThis study used two approaches to detect signatures of selection within and between six cattle breeds in South Africa, including Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), Bonsmara (n = 44), Angus (n = 31) and Holstein (n = 29). The first approach was based on the detection of genomic regions in which haplotypes have been driven towards complete fixation within breeds. The second approach identified regions of the genome that had very different allele frequencies between populations (FST).Results and discussionForty-seven candidate genomic regions were identified as harbouring putative signatures of selection using both methods. Twelve of these candidate selected regions were shared among the breeds and ten were validated by previous studies. Thirty-three of these regions were successfully annotated and candidate genes were identified. Among these genes the keratin genes (KRT222, KRT24, KRT25, KRT26, and KRT27) and one heat shock protein gene (HSPB9) on chromosome 19 between 42,896,570 and 42,897,840 bp were detected for the Nguni breed. These genes were previously associated with adaptation to tropical environments in Zebu cattle. In addition, a number of candidate genes associated with the nervous system (WNT5B, FMOD, PRELP, and ATP2B), immune response (CYM, CDC6, and CDK10), production (MTPN, IGFBP4, TGFB1, and AJAP1) and reproductive performance (ADIPOR2, OVOS2, and RBBP8) were also detected as being under selection.ConclusionsThe results presented here provide a foundation for detecting mutations that underlie genetic variation of traits that have economic importance for cattle breeds in South Africa.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0173-x) contains supplementary material, which is available to authorized users.
Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. This study investigated genetic diversity and the population structure among six cattle breeds in South African (SA) including Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), Bonsmara (n = 44), Angus (n = 31), and Holstein (n = 29). Genetic diversity within cattle breeds was analyzed using three measures of genetic diversity namely allelic richness (AR), expected heterozygosity (He) and inbreeding coefficient (f). Genetic distances between breed pairs were evaluated using Nei's genetic distance. Population structure was assessed using model-based clustering (ADMIXTURE). Results of this study revealed that the allelic richness ranged from 1.88 (Afrikaner) to 1.73 (Nguni). Afrikaner cattle had the lowest level of genetic diversity (He = 0.24) and the Drakensberger cattle (He = 0.30) had the highest level of genetic variation among indigenous and locally-developed cattle breeds. The level of inbreeding was lower across the studied cattle breeds. As expected the average genetic distance was the greatest between indigenous cattle breeds and Bos taurus cattle breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. The results of this study provided useful insight regarding genetic structure of SA cattle breeds.
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