NDLI: Robust Inference of Population Structure for Ancestry Prediction and Correction of Stratification in the Presence of Relatedness

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Robust Inference of Population Structure for Ancestry Prediction and Correction of Stratification in the Presence of Relatedness

Content Provider	PubMed Central
Author	Conomos, Matthew P. Miller, Mike Thornton, Timothy
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), multi-dimensional 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 ten (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.
Related Links	http://dx.doi.org/10.1002/gepi.21896
Ending Page	293
Page Count	18
Starting Page	276
File Format	PDF
ISSN	07410395
e-ISSN	10982272
Journal	Genetic epidemiology
Issue Number	4
Volume Number	39
Language	English
Publisher Date	2015-05-01
Access Restriction	Open
Subject Keyword	Genetics(clinical) Epidemiology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Epidemiology Genetics (clinical)

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