Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models

TL;DR

This paper introduces the LFMM algorithm, a fast and efficient method for detecting genetic loci associated with environmental gradients, accounting for population structure to reduce false positives in genome scans.

Contribution

The paper presents novel algorithms within the LFMM framework that improve detection of gene-environment associations by modeling population structure with unobserved variables.

Findings

LFMM reduces false positives in genome scans.

LFMM accurately estimates population structure effects.

Application to real data identified genes linked to climate adaptation.

Abstract

Adaptation to local environments often occurs through natural selection acting on a large number of loci, each having a weak phenotypic effect. One way to detect these loci is to identify genetic polymorphisms that exhibit high correlation with environmental variables used as proxies for ecological pressures. Here, we propose new algorithms based on population genetics, ecological modeling, and statistical learning techniques to screen genomes for signatures of local adaptation. Implemented in the computer program "latent factor mixed model" (LFMM), these algorithms employ an approach in which population structure is introduced using unobserved variables. These fast and computationally efficient algorithms detect correlations between environmental and genetic variation while simultaneously inferring background levels of population structure. Comparing these new algorithms with related methods provides evidence that LFMM can efficiently estimate random effects due to population history and isolation-by-distance patterns when computing gene-environment correlations, and decrease the number of false-positive associations in genome scans. We then apply these models to plant and human genetic data, identifying several genes with functions related to development that exhibit strong correlations with climatic gradients.