Genome‐environment association methods comparison supports omnigenic adaptation to ecological niche in malaria vector mosquitoes

Abstract

The concept of a fundamental ecological niche is central to questions of geographic distribution, population demography, species conservation, and evolutionary potential. However, robust inference of genomic regions associated with evolutionary adaptation to particular environmental conditions remains difficult due to the myriad of potential confounding processes that can generate heterogeneous patterns of variation across the genome. Here, we interrogate the potential role of genome environment association (GEA) testing as an initial step in building an understanding of the genetic basis of ecological niche. We leverage publicly available genomic data from the Anopheles gambiae 1000 Genomes (Ag1000g) Consortium to test the ability of multiple analytically unique GEA methods to handle confounding patterns of genetic variation, control false positive rates, and discern associations with broadly relevant climate variables from random allele frequency patterns throughout the genome. We found evidence supporting the ability of commonly implemented GEA methods to account for confounding patterns of spatial and genetic variation, and control false positive rates. However, we fail to find evidence supporting the ability of GEA tests to reject signals of adaptation to randomly simulated environmental variables, indicating that discerning between true signals of genome environment adaptation and genome environment correlations resulting from alternative evolutionary processes, remains challenging. Because signals of environmental adaptation are so diffuse and confounded throughout the genome, we argue that genomic adaptation to ecological niche is likely best understood under an omnigenic model wherein highly interconnected, genome-wide gene regulatory networks shape genomic adaptation to key environmental conditions.

Using genome scans to identify genes used repeatedly for adaptation

“…If environmental variables covary with a pattern of isolation by distance, many alleles will exhibit associations with environmental variation and simple GEA analyses may often result in false positives. There are multiple methods that build population structure into GEA analyses (reviewed in Hoban et al 2016), but these can result in false negatives if the true drivers of adaptation have spatial patterns in allele frequency that align with population structure (DeRaad et al 2021). However, under the assumption that most genes in the genome are not contributing to adaptation, the rank-order of GEA summary statistics (e.g.…”

Seeing the whole picture: What molecular ecology is gaining from whole genomes

“…There are a variety of confounding factors that generate a heterogeneous pattern of variation across the genome and thereby create a challenge in the robust identification of genomic regions associated with certain adaptive traits. DeRaad et al (2021) utilized a genome‐environment association (GEA) testing approach to leverage publicly available Anopheles gambiae genomes to differentiate true signals of genome environment adaptation from background variation resulting from alternative evolutionary processes. Colicchio et al (2021) utilized similar GEA approaches using whole‐genome re‐sequencing data from Mimulus guttatus populations to identify candidate genes associated with climatic variables in specialized metabolism, drought resistance, and development.…”

Editorial 2022

“…The special issue also includes a suite of papers work that use WGS to uncover the genetic basis of adaptive traits including immune response (Batley et al 2021; Moreno Santillán et al, 2021) and environmental heterogeneity (Nunez et al, 2021). Multiple studies take advantage of the comprehensive genomic data to examine polygenic adaptations (Montejo‐Kovacevich et al, 2021; Pereira et al, 2021), while others use WGS to conduct genotype environment association (GEA) analysis, revealing a fine‐scale look at genomic regions involved in adaptation to particular environmental variables (Colicchio et al, 2021; DeRaad et al, 2021). As the editors write in their conclusion, “the contributions presented in this special issue herald a new era in molecular ecology, as we anticipate that a burgeoning and widespread application of WGS will provide unprecedented insights into consequential questions in ecology and evolution,” (Taylor, Jensen, et al, 2021).…”