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Data from: Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass

Citation

Blanco-Pastor, José L. et al. (2020), Data from: Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass, Dryad, Dataset, https://doi.org/10.5061/dryad.0p2ngf1xk

Abstract

Germplasm from perennial ryegrass (Lolium perenne L.) natural populations is useful for breeding because of its adaptation to a wide range of climates. Climate-adaptive genes can be detected from associations between genotype, phenotype and climate but an integrated framework for the analysis of these three sources of information is lacking.

We used two approaches to identify adaptive loci in perennial ryegrass and their effect on phenotypic traits. First, we combined Genome-Environment Association (GEA) and GWAS analyses. Then, we implemented a new test based on a Canonical Correlation Analysis (CANCOR) to detect adaptive loci. Furthermore, we improved the previous perennial ryegrass gene set by de novo gene prediction and functional annotation of 39,967 genes.

GEA-GWAS revealed eight outlier loci associated with both an environmental variable and a phenotypic trait. CANCOR retrieved 633 outlier loci associated with two climatic gradients, characterized by cold-dry vs mild-wet winter and long rainy season vs long summer, and pointed out traits putatively conferring adaptation at the extremes of these gradients. Our CANCOR test also revealed the presence of both polygenic and oligogenic climatic adaptations. Our gene annotation revealed that 374 of the CANCOR outlier loci were positioned within or close to a gene. Co-association networks of outlier loci revealed a potential utility of CANCOR for investigating the interaction of genes involved in polygenic adaptations.

The CANCOR test provides an integrated framework to analyze adaptive genomic diversity and phenotypic responses to environmental selection pressures that could be used to facilitate the adaptation of plant species to climate change.