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Calibrating Population Stratification in Association Analysis

Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Genome-wide association studies Migration Random genetic drift Natural selection Admixed populations Global ancestry Local ancestries Local principal components Hidden Markov algorithms Fine mapping 

Notes

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.

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Global Biostatistics and Data ScienceTulane University School of Public Health and Tropical MedicineNew OrleansUSA
  2. 2.Department of Population and Quantitative Health SciencesCase Western Reserve University School of MedicineClevelandUSA

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