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Robust Inference of Population Structure for Ancestry Prediction and Correction of Stratification in the Presence of Relatedness
Author(s) -
Conomos Matthew P.,
Miller Michael B.,
Thornton Timothy A.
Publication year - 2015
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21896
Subject(s) - population stratification , inference , principal component analysis , population , international hapmap project , genetic genealogy , ancestry informative marker , biology , population genetics , multidimensional scaling , genetic association , genome wide association study , computer science , statistics , genetics , data mining , computational biology , artificial intelligence , machine learning , allele frequency , mathematics , single nucleotide polymorphism , allele , genotype , demography , sociology , gene
ABSTRACT Population structure inference with genetic data has been motivated by a variety of applications in population genetics and genetic association studies. Several approaches have been proposed for the identification of genetic ancestry differences in samples where study participants are assumed to be unrelated, including principal components analysis (PCA), multidimensional scaling (MDS), and model‐based methods for proportional ancestry estimation. Many genetic studies, however, include individuals with some degree of relatedness, and existing methods for inferring genetic ancestry fail in related samples. We present a method, PC‐AiR, for robust population structure inference in the presence of known or cryptic relatedness. PC‐AiR utilizes genome‐screen data and an efficient algorithm to identify a diverse subset of unrelated individuals that is representative of all ancestries in the sample. The PC‐AiR method directly performs PCA on the identified ancestry representative subset and then predicts components of variation for all remaining individuals based on genetic similarities. In simulation studies and in applications to real data from Phase III of the HapMap Project, we demonstrate that PC‐AiR provides a substantial improvement over existing approaches for population structure inference in related samples. We also demonstrate significant efficiency gains, where a single axis of variation from PC‐AiR provides better prediction of ancestry in a variety of structure settings than using 10 (or more) components of variation from widely used PCA and MDS approaches. Finally, we illustrate that PC‐AiR can provide improved population stratification correction over existing methods in genetic association studies with population structure and relatedness.