Robust identification of local adaptation from allele frequencies

TL;DR

This paper introduces robust Bayesian methods to detect local adaptation by analyzing allele frequency patterns across populations, accounting for population history, gene flow, and sequencing noise, demonstrated through simulations and human data.

Contribution

Develops a Bayesian framework for identifying local adaptation signals from allele frequencies, improving robustness against population structure and sequencing noise.

Findings

Standardized allele frequencies enable powerful correlation tests.

New methods outperform traditional FST-based tests in detecting selection.

Application to human data reveals candidate loci for local adaptation.

Abstract

Comparing allele frequencies among populations that differ in environment has long been a tool for detecting loci involved in local adaptation. However, such analyses are complicated by an imperfect knowledge of population allele frequencies and neutral correlations of allele frequencies among populations due to shared population history and gene flow. Here we develop a set of methods to robustly test for unusual allele frequency patterns, and correlations between environmental variables and allele frequencies while accounting for these complications based on a Bayesian model previously implemented in the software Bayenv. Using this model, we calculate a set of `standardized allele frequencies' that allows investigators to apply tests of their choice to multiple populations, while accounting for sampling and covariance due to population history. We illustrate this first by showing that these standardized frequencies can be used to calculate powerful tests to detect non-parametric correlations with environmental variables, which are also less prone to spurious results due to outlier populations. We then demonstrate how these standardized allele frequencies can be used to construct a test to detect SNPs that deviate strongly from neutral population structure. This test is conceptually related to FST but should be more powerful as we account for population history. We also extend the model to next-generation sequencing of population pools, which is a cost-efficient way to estimate population allele frequencies, but it implies an additional level of sampling noise. The utility of these methods is demonstrated in simulations and by re-analyzing human SNP data from the HGDP populations. An implementation of our method will be available from http://gcbias.org.