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

TLDR

Population structure inference from genetic data is essential for genetics studies, yet most methods assume unrelated individuals and fail when relatedness is present. This work introduces PC‑AiR, a method designed to robustly infer ancestry structure even when samples contain related individuals. PC‑AiR first selects a diverse set of unrelated individuals that represent all ancestries, performs PCA on this subset, and then predicts principal components for the remaining participants using genetic similarity. Simulations and HapMap Phase III data show that PC‑AiR markedly outperforms existing approaches, achieving superior ancestry prediction with fewer components and improving stratification correction in association studies.

Abstract

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.