Abstract
As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress.
Highlights
Arabidopsis thaliana has colonized a wide range of habitats across the world and it is an attractive model for studying the genetic mechanisms underlying environmental adaptation [1]
Genome-wide association or selective-sweep analyses can potentially identify signals of natural selection involved in environmental adaptation, if those can be disentangled from the effects of other population genetic forces acting to change the allele frequencies
A genome-wide association analysis was performed for thirteen climate variables across,215,000 SNPs in 948 A. thaliana accessions from the RegMap collection, representing the native range of the species [1,9]
Summary
Arabidopsis thaliana has colonized a wide range of habitats across the world and it is an attractive model for studying the genetic mechanisms underlying environmental adaptation [1]. The included accessions have adapted to a wide range of different climatic conditions and loci involved in climate adaptation will display genotype by climate-at-sampling-site correlations in these populations. Genome-wide association or selective-sweep analyses can potentially identify signals of natural selection involved in environmental adaptation, if those can be disentangled from the effects of other population genetic forces acting to change the allele frequencies. Correcting for genomic kinship often decreases the power to detect individual adaptive loci, which is likely the reason that no genome-wide significant associations to climate conditions were found in earlier GWAS analyses [1,8]. A number of candidate adaptive loci could despite this be identified using extensive experimental validation [1,2,8], showing how valuable these populations are as a resource for finding the genomic footprint of climate adaptation
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