A window into local adaptation

How organisms adapt to their environment is not only a central topic of evolutionary biology but also a pressing question in the light of recent global change. Unravelling the genetic basis of these local adaptations can help to predict the response of a population to an increase in temperature or the more frequent occurrence of droughts. A popular approach to study the genes that drive local adaptation is the analysis of genotype-environment associations (GEA), testing the correlation of genomic features (typically single-nucleotide polymorphisms, SNPs) and environmental conditions. In this issue of Molecular Ecology Resources, Booker et al. (Molecular Ecology Resources, 2023) present a new approach to GEA, introducing genomic window analysis. They combine the information of neighbouring SNPs instead of analysing each SNP independently, therefore gaining power for detecting genomic signals of environmental adaptation. Using simulations of local adaptation to a heterogeneous environment as well as previously published real data from a natural population of lodgepole pine, they prove the superiority of their method over several established GEA approaches, especially in the case of small sample sizes. Leveraging the information present in closely linked genomic sites, Booker et al. (Molecular Ecology Resources, 2023) take genotype-environment association studies to the next level.

Automated summary

This article presents a new approach to genotype-environment association (GEA) studies called genomic window analysis, which combines the information of neighboring single-nucleotide polymorphisms (SNPs) to enhance the detection of genomic signals of environmental adaptation. The method is proven to be superior to several established GEA approaches, especially in cases with small sample sizes, through simulations and real data analysis.