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Calibrating Population Stratification in Association Analysis

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Statistical Human Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1666))

Abstract

In genetic association studies, it is necessary to correct for population structure to avoid inference bias. During the past decade, prevailing corrections often only involved adjustments of global ancestry differences between sampled individuals. Nevertheless, population structure may vary across local genomic regions due to the variability of local ancestries associated with natural selection, migration, or random genetic drift. Adjusting for global ancestry alone may be inadequate when local population structure is an important confounding factor. In contrast, adjusting for local ancestry can more effectively prevent false positives due to local population structure. To more accurately locate disease genes, we recommend adjusting for local ancestries by interrogating local structure. In practice, locus-specific ancestries are usually unknown and must be inferred. For recently admixed populations with known reference ancestral populations, locus-specific ancestries can be inferred accurately using some hidden Markov model-based methods. However, SNP-wise ancestries cannot be accurately inferred when ancestral population information is not available. For such scenarios, we propose employing local principal components (PCs) to present local ancestries and adjusting for local PCs when testing for gene–phenotype association.

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Acknowledgments

This work was funded in part by NHGRI grant HG003054 to X.Z. and by Tulane’s Committee on Research fellowship (600890) and Carol Lavin Bernick Faculty Grant (632119) to H.Q.

Author information

Authors and Affiliations

  1. Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, 70112, USA

    Huaizhen Qin Ph.D.

  2. Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA

    Huaizhen Qin Ph.D. & Xiaofeng Zhu Ph.D.

Authors
  1. Huaizhen Qin Ph.D.
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  2. Xiaofeng Zhu Ph.D.
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Corresponding author

Correspondence to Huaizhen Qin Ph.D. .

Editor information

Editors and Affiliations

  1. Case Western Reserve University, Cleveland, OH, USA

    Robert C. Elston

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Qin, H., Zhu, X. (2017). Calibrating Population Stratification in Association Analysis. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_21

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  • DOI: https://doi.org/10.1007/978-1-4939-7274-6_21

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7273-9

  • Online ISBN: 978-1-4939-7274-6

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