IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v87y2013icp62-74.html
   My bibliography  Save this article

Genotype imputation in a coalescent model with infinitely-many-sites mutation

Author

Listed:
  • Huang, Lucy
  • Buzbas, Erkan O.
  • Rosenberg, Noah A.

Abstract

Empirical studies have identified population-genetic factors as important determinants of the properties of genotype-imputation accuracy in imputation-based disease association studies. Here, we develop a simple coalescent model of three sequences that we use to explore the theoretical basis for the influence of these factors on genotype-imputation accuracy, under the assumption of infinitely-many-sites mutation. Employing a demographic model in which two populations diverged at a given time in the past, we derive the approximate expectation and variance of imputation accuracy in a study sequence sampled from one of the two populations, choosing between two reference sequences, one sampled from the same population as the study sequence and the other sampled from the other population. We show that, under this model, imputation accuracy—as measured by the proportion of polymorphic sites that are imputed correctly in the study sequence—increases in expectation with the mutation rate, the proportion of the markers in a chromosomal region that are genotyped, and the time to divergence between the study and reference populations. Each of these effects derives largely from an increase in information available for determining the reference sequence that is genetically most similar to the sequence targeted for imputation. We analyze as a function of divergence time the expected gain in imputation accuracy in the target using a reference sequence from the same population as the target rather than from the other population. Together with a growing body of empirical investigations of genotype imputation in diverse human populations, our modeling framework lays a foundation for extending imputation techniques to novel populations that have not yet been extensively examined.

Suggested Citation

  • Huang, Lucy & Buzbas, Erkan O. & Rosenberg, Noah A., 2013. "Genotype imputation in a coalescent model with infinitely-many-sites mutation," Theoretical Population Biology, Elsevier, vol. 87(C), pages 62-74.
  • Handle: RePEc:eee:thpobi:v:87:y:2013:i:c:p:62-74
    DOI: 10.1016/j.tpb.2012.09.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580912000950
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tpb.2012.09.006?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yu-Fang Pei & Jian Li & Lei Zhang & Christopher J Papasian & Hong-Wen Deng, 2008. "Analyses and Comparison of Accuracy of Different Genotype Imputation Methods," PLOS ONE, Public Library of Science, vol. 3(10), pages 1-7, October.
    2. Peter Donnelly, 2008. "Progress and challenges in genome-wide association studies in humans," Nature, Nature, vol. 456(7223), pages 728-731, December.
    3. Yongtao Guan & Matthew Stephens, 2008. "Practical Issues in Imputation-Based Association Mapping," PLOS Genetics, Public Library of Science, vol. 4(12), pages 1-11, December.
    4. Susanna Atwell & Yu S. Huang & Bjarni J. Vilhjálmsson & Glenda Willems & Matthew Horton & Yan Li & Dazhe Meng & Alexander Platt & Aaron M. Tarone & Tina T. Hu & Rong Jiang & N. Wayan Muliyati & Xu Zha, 2010. "Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines," Nature, Nature, vol. 465(7298), pages 627-631, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jewett, Ethan M. & Rosenberg, Noah A., 2014. "Theory and applications of a deterministic approximation to the coalescent model," Theoretical Population Biology, Elsevier, vol. 93(C), pages 14-29.

    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. Marttinen Pekka & Gillberg Jussi & Havulinna Aki & Corander Jukka & Kaski Samuel, 2013. "Genome-wide association studies with high-dimensional phenotypes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 413-431, August.
    2. Hideki Yoshida & Ko Hirano & Kenji Yano & Fanmiao Wang & Masaki Mori & Mayuko Kawamura & Eriko Koketsu & Masako Hattori & Reynante Lacsamana Ordonio & Peng Huang & Eiji Yamamoto & Makoto Matsuoka, 2022. "Genome-wide association study identifies a gene responsible for temperature-dependent rice germination," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Hanne De Kort & Sylvain Legrand & Olivier Honnay & James Buckley, 2022. "Transposable elements maintain genome-wide heterozygosity in inbred populations," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Yuxuan Duan & Hongliang Zheng & Haoran Wen & Di Qu & Jingnan Cui & Chong Li & Jingguo Wang & Hualong Liu & Luomiao Yang & Yan Jia & Wei Xin & Shuangshuang Li & Detang Zou, 2022. "Identification of Candidate Genes for Salt Tolerance at the Germination Stage in Japonica Rice by Genome-Wide Association Analysis," Agriculture, MDPI, vol. 12(10), pages 1-15, October.
    5. Charles-Elie Rabier & Philippe Barre & Torben Asp & Gilles Charmet & Brigitte Mangin, 2016. "On the Accuracy of Genomic Selection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
    6. Joshua C Randall & Thomas W Winkler & Zoltán Kutalik & Sonja I Berndt & Anne U Jackson & Keri L Monda & Tuomas O Kilpeläinen & Tõnu Esko & Reedik Mägi & Shengxu Li & Tsegaselassie Workalemahu & Mary F, 2013. "Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits," PLOS Genetics, Public Library of Science, vol. 9(6), pages 1-19, June.
    7. Muhammad Saeed & Farhan Ullah & Liaqat Shah & Waqas Ahmad & Murad Ali & Fazal Munsif & Ahmad Zubair & Muhammad Ibrahim & Syed Mushtaq Ahmed Shah & Hammad Uddin & Chen Can & Si Hongqi & Ma Chuanxi, 2022. "Identification of Three Novel QTLs Associated with Yellow Rust Resistance in Wheat ( Triticum aestivum L.) Anong-179/Khaista-17 F 2 Population," Sustainability, MDPI, vol. 14(12), pages 1-15, June.
    8. Joanna L Davies & Jean-Baptiste Cazier & Malcolm G Dunlop & Richard S Houlston & Ian P Tomlinson & Chris C Holmes, 2012. "A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-9, December.
    9. Bergersen Linn Cecilie & Glad Ingrid K. & Lyng Heidi, 2011. "Weighted Lasso with Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-29, August.
    10. Ganwen Zhang & Jianini Zhao & Jieru Wang & Guo Lin & Lin Li & Fengfei Ban & Meiting Zhu & Yangjun Wen & Jin Zhang, 2024. "An Improved Expectation–Maximization Bayesian Algorithm for GWAS," Mathematics, MDPI, vol. 12(13), pages 1-14, June.
    11. Yasuhiro Sato & Rie Shimizu-Inatsugi & Kazuya Takeda & Bernhard Schmid & Atsushi J. Nagano & Kentaro K. Shimizu, 2024. "Reducing herbivory in mixed planting by genomic prediction of neighbor effects in the field," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. Qian Li & Xuebing Ying & Yashu Yang & Wei Gao, 2024. "Genetic Diversity and Genome-Wide Association Study of Pleurotus pulmonarius Germplasm," Agriculture, MDPI, vol. 14(11), pages 1-19, November.
    13. Jason Flannick & Joshua M Korn & Pierre Fontanillas & George B Grant & Eric Banks & Mark A Depristo & David Altshuler, 2012. "Efficiency and Power as a Function of Sequence Coverage, SNP Array Density, and Imputation," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-13, July.
    14. Minghui Kang & Haolin Wu & Huanhuan Liu & Wenyu Liu & Mingjia Zhu & Yu Han & Wei Liu & Chunlin Chen & Yan Song & Luna Tan & Kangqun Yin & Yusen Zhao & Zhen Yan & Shangling Lou & Yanjun Zan & Jianquan , 2023. "The pan-genome and local adaptation of Arabidopsis thaliana," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Hou-Feng Zheng & Jing-Jing Rong & Ming Liu & Fang Han & Xing-Wei Zhang & J Brent Richards & Li Wang, 2015. "Performance of Genotype Imputation for Low Frequency and Rare Variants from the 1000 Genomes," PLOS ONE, Public Library of Science, vol. 10(1), pages 1-10, January.
    16. Mathew J Barber & Lara M Mangravite & Craig L Hyde & Daniel I Chasman & Joshua D Smith & Catherine A McCarty & Xiaohui Li & Russell A Wilke & Mark J Rieder & Paul T Williams & Paul M Ridker & Aurobind, 2010. "Genome-Wide Association of Lipid-Lowering Response to Statins in Combined Study Populations," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-10, March.
    17. Paul T Williams, 2012. "Quantile-Specific Penetrance of Genes Affecting Lipoproteins, Adiposity and Height," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-8, January.
    18. Cecilia M Lindgren & Iris M Heid & Joshua C Randall & Claudia Lamina & Valgerdur Steinthorsdottir & Lu Qi & Elizabeth K Speliotes & Gudmar Thorleifsson & Cristen J Willer & Blanca M Herrera & Anne U J, 2009. "Genome-Wide Association Scan Meta-Analysis Identifies Three Loci Influencing Adiposity and Fat Distribution," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-13, June.
    19. Weihua Shou & Dazhi Wang & Kaiyue Zhang & Beilan Wang & Zhimin Wang & Jinxiu Shi & Wei Huang, 2012. "Gene-Wide Characterization of Common Quantitative Trait Loci for ABCB1 mRNA Expression in Normal Liver Tissues in the Chinese Population," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-10, September.

    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:eee:thpobi:v:87:y:2013:i:c:p:62-74. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    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.