10.5061/DRYAD.0P2NGF1XK
Blanco-Pastor, José L.
0000-0002-7708-1342
National Research Institute for Agriculture, Food and Environment
Barre, Philippe
National Research Institute for Agriculture, Food and Environment
Keep, Thomas
National Research Institute for Agriculture, Food and Environment
Ledauphin, Thomas
National Research Institute for Agriculture, Food and Environment
Escobar-Gutiérrez, Abraham
National Research Institute for Agriculture, Food and Environment
Roschanski, Anna Maria
Institute of Plant Genetics and Crop Plant Research
Willner, Evelyn
Institute of Plant Genetics and Crop Plant Research
Dehmer, Klaus
Institute of Plant Genetics and Crop Plant Research
Hegarty, Matthew
Institute of Biological, Environmental and Rural Sciences
Muylle, Hilde
Instituut voor Landbouw en Visserijonderzoek
Veeckman, Elisabeth
Instituut voor Landbouw en Visserijonderzoek
Vandepoele, Klaas
Ghent University
Ruttink, Tom
Instituut voor Landbouw en Visserijonderzoek
Roldán-Ruiz, Isabel
Instituut voor Landbouw en Visserijonderzoek
Manel, Stéphanie
French National Centre for Scientific Research
Sampoux, Jean-Paul
National Research Institute for Agriculture, Food and Environment
Data from: Canonical correlations reveal adaptive loci and phenotypic
responses to climate in perennial ryegrass
Dryad
dataset
2020
2020-09-09T00:00:00Z
2020-09-09T00:00:00Z
en
591628193 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.