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      Population-specific common SNPs reflect demographic histories and highlight regions of genomic plasticity with functional relevance

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          Abstract

          Background

          Population differentiation is the result of demographic and evolutionary forces. Whole genome datasets from the 1000 Genomes Project (October 2012) provide an unbiased view of genetic variation across populations from Europe, Asia, Africa and the Americas. Common population-specific SNPs (MAF > 0.05) reflect a deep history and may have important consequences for health and wellbeing. Their interpretation is contextualised by currently available genome data.

          Results

          The identification of common population-specific (CPS) variants (SNPs and SSV) is influenced by admixture and the sample size under investigation. Nine of the populations in the 1000 Genomes Project (2 African, 2 Asian (including a merged Chinese group) and 5 European) revealed that the African populations (LWK and YRI), followed by the Japanese (JPT) have the highest number of CPS SNPs, in concordance with their histories and given the populations studied. Using two methods, sliding 50-SNP and 5-kb windows, the CPS SNPs showed distinct clustering across large genome segments and little overlap of clusters between populations. iHS enrichment score and the population branch statistic (PBS) analyses suggest that selective sweeps are unlikely to account for the clustering and population specificity. Of interest is the association of clusters close to recombination hotspots. Functional analysis of genes associated with the CPS SNPs revealed over-representation of genes in pathways associated with neuronal development, including axonal guidance signalling and CREB signalling in neurones.

          Conclusions

          Common population-specific SNPs are non-randomly distributed throughout the genome and are significantly associated with recombination hotspots. Since the variant alleles of most CPS SNPs are the derived allele, they likely arose in the specific population after a split from a common ancestor. Their proximity to genes involved in specific pathways, including neuronal development, suggests evolutionary plasticity of selected genomic regions. Contrary to expectation, selective sweeps did not play a large role in the persistence of population-specific variation. This suggests a stochastic process towards population-specific variation which reflects demographic histories and may have some interesting implications for health and susceptibility to disease.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1471-2164-15-437) contains supplementary material, which is available to authorized users.

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          Most cited references60

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          The genetic structure and history of Africans and African Americans.

          Africa is the source of all modern humans, but characterization of genetic variation and of relationships among populations across the continent has been enigmatic. We studied 121 African populations, four African American populations, and 60 non-African populations for patterns of variation at 1327 nuclear microsatellite and insertion/deletion markers. We identified 14 ancestral population clusters in Africa that correlate with self-described ethnicity and shared cultural and/or linguistic properties. We observed high levels of mixed ancestry in most populations, reflecting historical migration events across the continent. Our data also provide evidence for shared ancestry among geographically diverse hunter-gatherer populations (Khoesan speakers and Pygmies). The ancestry of African Americans is predominantly from Niger-Kordofanian (approximately 71%), European (approximately 13%), and other African (approximately 8%) populations, although admixture levels varied considerably among individuals. This study helps tease apart the complex evolutionary history of Africans and African Americans, aiding both anthropological and genetic epidemiologic studies.
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            Rare and common variants: twenty arguments.

            Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.
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              Fine-scale recombination rate differences between sexes, populations and individuals.

              Meiotic recombinations contribute to genetic diversity by yielding new combinations of alleles. Recently, high-resolution recombination maps were inferred from high-density single-nucleotide polymorphism (SNP) data using linkage disequilibrium (LD) patterns that capture historical recombination events. The use of these maps has been demonstrated by the identification of recombination hotspots and associated motifs, and the discovery that the PRDM9 gene affects the proportion of recombinations occurring at hotspots. However, these maps provide no information about individual or sex differences. Moreover, locus-specific demographic factors like natural selection can bias LD-based estimates of recombination rate. Existing genetic maps based on family data avoid these shortcomings, but their resolution is limited by relatively few meioses and a low density of markers. Here we used genome-wide SNP data from 15,257 parent-offspring pairs to construct the first recombination maps based on directly observed recombinations with a resolution that is effective down to 10 kilobases (kb). Comparing male and female maps reveals that about 15% of hotspots in one sex are specific to that sex. Although male recombinations result in more shuffling of exons within genes, female recombinations generate more new combinations of nearby genes. We discover novel associations between recombination characteristics of individuals and variants in the PRDM9 gene and we identify new recombination hotspots. Comparisons of our maps with two LD-based maps inferred from data of HapMap populations of Utah residents with ancestry from northern and western Europe (CEU) and Yoruba in Ibadan, Nigeria (YRI) reveal population differences previously masked by noise and map differences at regions previously described as targets of natural selection.
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                Author and article information

                Contributors
                ananyo.choudhury@wits.ac.za
                scott.hazelhurst@wits.ac.za
                ayton@cbio.uct.ac.za
                ovokeraye@gmail.com
                shaun.aron@wits.ac.za
                junaid@sanbi.ac.za
                mahjoubeh@sanbi.ac.za
                Nicola.Mulder@uct.ac.za
                nicki@sanbi.ac.za
                michele.ramsay@wits.ac.za
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                6 June 2014
                6 June 2014
                2014
                : 15
                : 1
                : 437
                Affiliations
                [ ]Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
                [ ]Division of Human Genetics, National Health Laboratory Service, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [ ]School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
                [ ]Department Clinical Laboratory Sciences, Computational Biology Group, IDM, University of Cape Town, Cape Town, South Africa
                [ ]South African National Bioinformatics Institute/Medical Research Council of South Africa Bioinformatics Unit, University of the Western Cape, Bellville, South Africa
                Article
                6211
                10.1186/1471-2164-15-437
                4092225
                24906912
                04b4ec22-e2fe-40b3-bcb1-f1833813487a
                © Choudhury et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 November 2013
                : 19 May 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Genetics
                Genetics

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