IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0073971.html
   My bibliography  Save this article

Determining Ancestry Proportions in Complex Admixture Scenarios in South Africa Using a Novel Proxy Ancestry Selection Method

Author

Listed:
  • Emile R Chimusa
  • Michelle Daya
  • Marlo Möller
  • Raj Ramesar
  • Brenna M Henn
  • Paul D van Helden
  • Nicola J Mulder
  • Eileen G Hoal

Abstract

: Admixed 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 , ‡Khomani SAN , European , Indian , and Chinese . 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. Nomenclature: The collective term for people of mixed ancestry in southern Africa is “Coloured,” and this is officially recognized in South Africa as a census term, and for self-classification. Whilst we acknowledge that some cultures may use this term in a derogatory manner, these connotations are not present in South Africa, and are certainly not intended here.

Suggested Citation

  • Emile R Chimusa & Michelle Daya & Marlo Möller & Raj Ramesar & Brenna M Henn & Paul D van Helden & Nicola J Mulder & Eileen G Hoal, 2013. "Determining Ancestry Proportions in Complex Admixture Scenarios in South Africa Using a Novel Proxy Ancestry Selection Method," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0073971
    DOI: 10.1371/journal.pone.0073971
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0073971
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0073971&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0073971?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jian Li & Yan-fang Guo & Yufang Pei & Hong-Wen Deng, 2012. "The Impact of Imputation on Meta-Analysis of Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-7, April.
    2. Joseph K. Pickrell & Nick Patterson & Chiara Barbieri & Falko Berthold & Linda Gerlach & Tom Güldemann & Blesswell Kure & Sununguko Wata Mpoloka & Hirosi Nakagawa & Christfried Naumann & Mark Lipson &, 2012. "The genetic prehistory of southern Africa," Nature Communications, Nature, vol. 3(1), pages 1-6, January.
    3. Nick Patterson & Alkes L Price & David Reich, 2006. "Population Structure and Eigenanalysis," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-20, December.
    4. Alkes L Price & Agnar Helgason & Snaebjorn Palsson & Hreinn Stefansson & David St. Clair & Ole A Andreassen & David Reich & Augustine Kong & Kari Stefansson, 2009. "The Impact of Divergence Time on the Nature of Population Structure: An Example from Iceland," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-10, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sam Tallman & Maria das Dores Sungo & Sílvio Saranga & Sandra Beleza, 2023. "Whole genomes from Angola and Mozambique inform about the origins and dispersals of major African migrations," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Gyaneshwer Chaubey & Anurag Kadian & Saroj Bala & Vadlamudi Raghavendra Rao, 2015. "Genetic Affinity of the Bhil, Kol and Gond Mentioned in Epic Ramayana," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-11, June.
    3. Estavoyer, Maxime & François, Olivier, 2022. "Theoretical analysis of principal components in an umbrella model of intraspecific evolution," Theoretical Population Biology, Elsevier, vol. 148(C), pages 11-21.
    4. Hyosik Jang & Ian M Ehrenreich, 2012. "Genome-Wide Characterization of Genetic Variation in the Unicellular, Green Alga Chlamydomonas reinhardtii," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    5. Xiaofeng Cai & Xuepeng Sun & Chenxi Xu & Honghe Sun & Xiaoli Wang & Chenhui Ge & Zhonghua Zhang & Quanxi Wang & Zhangjun Fei & Chen Jiao & Quanhua Wang, 2021. "Genomic analyses provide insights into spinach domestication and the genetic basis of agronomic traits," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    6. Lee, Anthony J. & Hibbs, Courtney & Wright, Margaret J. & Martin, Nicholas G. & Keller, Matthew C. & Zietsch, Brendan P., 2017. "Assessing the accuracy of perceptions of intelligence based on heritable facial features," Intelligence, Elsevier, vol. 64(C), pages 1-8.
    7. Thompson Katherine L. & Linnen Catherine R. & Kubatko Laura, 2016. "Tree-based quantitative trait mapping in the presence of external covariates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(6), pages 473-490, December.
    8. Jacobo Pardo-Seco & Alberto Gómez-Carballa & Jorge Amigo & Federico Martinón-Torres & Antonio Salas, 2014. "A Genome-Wide Study of Modern-Day Tuscans: Revisiting Herodotus's Theory on the Origin of the Etruscans," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-11, September.
    9. Ilja M Nolte & Chris Wallace & Stephen J Newhouse & Daryl Waggott & Jingyuan Fu & Nicole Soranzo & Rhian Gwilliam & Panos Deloukas & Irina Savelieva & Dongling Zheng & Chrysoula Dalageorgou & Martin F, 2009. "Common Genetic Variation Near the Phospholamban Gene Is Associated with Cardiac Repolarisation: Meta-Analysis of Three Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-10, July.
    10. Hoicheong Siu & Li Jin & Momiao Xiong, 2012. "Manifold Learning for Human Population Structure Studies," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-18, January.
    11. Elodie Persyn & Richard Redon & Lise Bellanger & Christian Dina, 2018. "The impact of a fine-scale population stratification on rare variant association test results," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.
    12. Andre Krumel Portella & Afroditi Papantoni & Catherine Paquet & Spencer Moore & Keri Shiels Rosch & Stewart Mostofsky & Richard S Lee & Kimberly R Smith & Robert Levitan & Patricia Pelufo Silveira & S, 2020. "Predicted DRD4 prefrontal gene expression moderates snack intake and stress perception in response to the environment in adolescents," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    13. Lindsay Fernández-Rhodes & Jennifer R Malinowski & Yujie Wang & Ran Tao & Nathan Pankratz & Janina M Jeff & Sachiko Yoneyama & Cara L Carty & V Wendy Setiawan & Loic Le Marchand & Christopher Haiman &, 2018. "The genetic underpinnings of variation in ages at menarche and natural menopause among women from the multi-ethnic Population Architecture using Genomics and Epidemiology (PAGE) Study: A trans-ethnic ," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-21, July.
    14. Peña-Malavera Andrea & Bruno Cecilia & Fernandez Elmer & Balzarini Monica, 2014. "Comparison of algorithms to infer genetic population structure from unlinked molecular markers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 391-402, August.
    15. Chi-Chun Liu & David Witonsky & Anna Gosling & Ju Hyeon Lee & Harald Ringbauer & Richard Hagan & Nisha Patel & Raphaela Stahl & John Novembre & Mark Aldenderfer & Christina Warinner & Anna Di Rienzo &, 2022. "Ancient genomes from the Himalayas illuminate the genetic history of Tibetans and their Tibeto-Burman speaking neighbors," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    16. Abhinav Thakral & John JW. Lee & Tianzhichao Hou & Katrina Hueniken & Tom Dudding & Mark Gormley & Shama Virani & Andrew Olshan & Brenda Diergaarde & Andrew R. Ness & Tim Waterboer & Karl Smith-Byrne , 2024. "Smoking and alcohol by HPV status in head and neck cancer: a Mendelian randomization study," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    17. Gad Abraham & Michael Inouye, 2014. "Fast Principal Component Analysis of Large-Scale Genome-Wide Data," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-5, April.
    18. Zhao Huaqing & Rebbeck Timothy R. & Mitra Nandita, 2012. "Analyzing Genetic Association Studies with an Extended Propensity Score Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-24, October.
    19. Diana Chang & Alon Keinan, 2014. "Principal Component Analysis Characterizes Shared Pathogenetics from Genome-Wide Association Studies," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-14, September.
    20. Morten Dybdahl Krebs & Gonçalo Espregueira Themudo & Michael Eriksen Benros & Ole Mors & Anders D. Børglum & David Hougaard & Preben Bo Mortensen & Merete Nordentoft & Michael J. Gandal & Chun Chieh F, 2021. "Associations between patterns in comorbid diagnostic trajectories of individuals with schizophrenia and etiological factors," Nature Communications, Nature, vol. 12(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0073971. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.