Improving population-specific allele frequency estimates by adapting supplemental data: an empirical Bayes approach

TL;DR

This paper introduces an empirical Bayes method for estimating allele frequencies that adaptively combines data from related samples, reducing estimation error while minimizing bias, especially useful in genome-wide association studies.

Contribution

The paper presents a novel empirical Bayes approach that adaptively pools data from related populations to improve allele frequency estimates with reduced bias and variance.

Findings

Estimator achieves lower mean squared error than pooling or no pooling.

Method reduces bias while maintaining low variance in estimates.

Effective in small sample groups with many genetic markers.

Abstract

Estimation of the allele frequency at genetic markers is a key ingredient in biological and biomedical research, such as studies of human genetic variation or of the genetic etiology of heritable traits. As genetic data becomes increasingly available, investigators face a dilemma: when should data from other studies and population subgroups be pooled with the primary data? Pooling additional samples will generally reduce the variance of the frequency estimates; however, used inappropriately, pooled estimates can be severely biased due to population stratification. Because of this potential bias, most investigators avoid pooling, even for samples with the same ethnic background and residing on the same continent. Here, we propose an empirical Bayes approach for estimating allele frequencies of single nucleotide polymorphisms. This procedure adaptively incorporates genotypes from related samples, so that more similar samples have a greater influence on the estimates. In every example we have considered, our estimator achieves a mean squared error (MSE) that is smaller than either pooling or not, and sometimes substantially improves over both extremes. The bias introduced is small, as is shown by a simulation study that is carefully matched to a real data example. Our method is particularly useful when small groups of individuals are genotyped at a large number of markers, a situation we are likely to encounter in a genome-wide association study.