Determining ancestry proportions in complex admixture scenarios in South Africa using a novel proxy ancestry selection method

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dc.contributor.authorChimusa, Emile R.
dc.contributor.authorDaya, Michelle
dc.contributor.authorMöller, Marlo
dc.contributor.authorRamesar, Raj
dc.contributor.authorHenn, Brenna M.
dc.contributor.authorVan Helden, Paul D.
dc.contributor.authorMulder, Nicola J.
dc.contributor.authorHoal, Eileen G.
dc.date.accessioned2014-02-11T08:57:55Z
dc.date.available2014-02-11T08:57:55Z
dc.date.issued2013-09
dc.descriptionPublication of this article was funded by the Stellenbosch University Open Access Fund.en_ZA
dc.descriptionThe original publication is available at http://www.plosone.org/en_ZA
dc.description.abstractAdmixed populations can make an important contribution to the discovery of disease susceptibility genes if the parental populations exhibit substantial variation in susceptibility. Admixture mapping has been used successfully, but is not designed to cope with populations that have more than two or three ancestral populations. The inference of admixture proportions and local ancestry and the imputation of missing genotypes in admixed populations are crucial in both understanding variation in disease and identifying novel disease loci. These inferences make use of reference populations, and accuracy depends on the choice of ancestral populations. Using an insufficient or inaccurate ancestral panel can result in erroneously inferred ancestry and affect the detection power of GWAS and meta-analysis when using imputation. Current algorithms are inadequate for multi-way admixed populations. To address these challenges we developed PROXYANC, an approach to select the best proxy ancestral populations. From the simulation of a multi-way admixed population we demonstrate the capability and accuracy of PROXYANC and illustrate the importance of the choice of ancestry in both estimating admixture proportions and imputing missing genotypes. We applied this approach to a complex, uniquely admixed South African population. Using genome-wide SNP data from over 764 individuals, we accurately estimate the genetic contributions from the best ancestral populations: isiXhosa (33%±0:226), {Khomani SAN (31%±0:195), European (16%±0:118), Indian (13%±0:094), and Chinese (7%±0:0488). We also demonstrate that the ancestral allele frequency differences correlate with increased linkage disequilibrium in the South African population, which originates from admixture events rather than population bottlenecks.en_ZA
dc.description.sponsorshipStellenbosch Universityen_ZA
dc.description.sponsorshipMRC Centre for Molecular and Cellular Biology and the DST/NRF Centre of Excellence for Biomedical TB Research
dc.description.sponsorshipCarnegie Corporation Grant and by the Department of Clinical Laboratory Sciences, University of Cape Town
dc.description.versionPublishers' versionen_ZA
dc.format.extent14 p. : col. ill.en_ZA
dc.identifier.citationChimusa, E. R. et al. 2013. Determining ancestry proportions in complex admixture scenarios in South Africa using a novel proxy ancestry selection method. PLoS ONE, 8(9):e73971, doi:10.1371/journal.pone.0073971.en_ZA
dc.identifier.issn1932-6203 (print)
dc.identifier.issn1932-6203 (online)
dc.identifier.otherdoi:10.1371/journal.pone.0073971
dc.identifier.urihttp://hdl.handle.net/10019.1/86131
dc.language.isoen_ZAen_ZA
dc.publisherPLoSen_ZA
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectColoured people -- South Africa -- Ancestryen_ZA
dc.subjectEthnicity
dc.subjectPopulation geneticsen_ZA
dc.subjectPROXYANC
dc.subjectColoured people -- South Africa -- Genetic diversityen_ZA
dc.titleDetermining ancestry proportions in complex admixture scenarios in South Africa using a novel proxy ancestry selection methoden_ZA
dc.typeArticleen_ZA
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