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Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass
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 the...
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Published in: | Molecular ecology resources 2021-04, Vol.21 (3), p.849-870 |
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creator | Blanco‐Pastor, José Luis Barre, Philippe Keep, Thomas Ledauphin, Thomas Escobar‐Gutiérrez, Abraham Roschanski, Anna Maria Willner, Evelyn Dehmer, Klaus J. Hegarty, Matthew Muylle, Hilde Veeckman, Elisabeth Vandepoele, Klaas Ruttink, Tom Roldán‐Ruiz, Isabel Manel, Stéphanie Sampoux, Jean‐Paul |
description | 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 environmental variables and phenotypic traits. CANCOR retrieved 633 outlier loci associated with two climatic gradients, characterized by cold‐dry winter versus mild‐wet winter and long rainy season versus 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 analyse adaptive genomic diversity and phenotypic responses to environmental selection pressures that could be used to facilitate the adaptation of plant species to climate change. |
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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 environmental variables and phenotypic traits. CANCOR retrieved 633 outlier loci associated with two climatic gradients, characterized by cold‐dry winter versus mild‐wet winter and long rainy season versus 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 analyse adaptive genomic diversity and phenotypic responses to environmental selection pressures that could be used to facilitate the adaptation of plant species to climate change.</description><identifier>ISSN: 1755-098X</identifier><identifier>EISSN: 1755-0998</identifier><identifier>DOI: 10.1111/1755-0998.13289</identifier><identifier>PMID: 33098268</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Adaptation ; Agricultural sciences ; agriculture ; Annotations ; Breeding ; Climate ; Climate adaptation ; Climate change ; Correlation analysis ; ecological genetics ; Genes ; Genomes ; Genotypes ; Germplasm ; landscape genetics ; Life Sciences ; Loci ; Lolium perenne ; Natural populations ; Outliers (statistics) ; Phenotypes ; Plant species ; Polygenic inheritance ; quantitative genetics ; Rainy season ; Winter</subject><ispartof>Molecular ecology resources, 2021-04, Vol.21 (3), p.849-870</ispartof><rights>2020 John Wiley & Sons Ltd</rights><rights>2020 John Wiley & Sons Ltd.</rights><rights>Copyright © 2021 John Wiley & Sons Ltd</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4469-cdf0b8fce18d706971409e01e1caa043bbc2c6724d66f5c10ff4001255f5d2da3</citedby><cites>FETCH-LOGICAL-c4469-cdf0b8fce18d706971409e01e1caa043bbc2c6724d66f5c10ff4001255f5d2da3</cites><orcidid>0000-0001-8902-6052 ; 0000-0002-7708-1342 ; 0000-0002-5111-9998 ; 0000-0001-7340-3386 ; 0000-0003-1466-138X ; 0000-0002-3012-7972 ; 0000-0002-9196-4707 ; 0000-0003-4790-2725 ; 0000-0002-1012-9399</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33098268$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.inrae.fr/hal-03138493$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Blanco‐Pastor, José Luis</creatorcontrib><creatorcontrib>Barre, Philippe</creatorcontrib><creatorcontrib>Keep, Thomas</creatorcontrib><creatorcontrib>Ledauphin, Thomas</creatorcontrib><creatorcontrib>Escobar‐Gutiérrez, Abraham</creatorcontrib><creatorcontrib>Roschanski, Anna Maria</creatorcontrib><creatorcontrib>Willner, Evelyn</creatorcontrib><creatorcontrib>Dehmer, Klaus J.</creatorcontrib><creatorcontrib>Hegarty, Matthew</creatorcontrib><creatorcontrib>Muylle, Hilde</creatorcontrib><creatorcontrib>Veeckman, Elisabeth</creatorcontrib><creatorcontrib>Vandepoele, Klaas</creatorcontrib><creatorcontrib>Ruttink, Tom</creatorcontrib><creatorcontrib>Roldán‐Ruiz, Isabel</creatorcontrib><creatorcontrib>Manel, Stéphanie</creatorcontrib><creatorcontrib>Sampoux, Jean‐Paul</creatorcontrib><title>Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass</title><title>Molecular ecology resources</title><addtitle>Mol Ecol Resour</addtitle><description>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 environmental variables and phenotypic traits. CANCOR retrieved 633 outlier loci associated with two climatic gradients, characterized by cold‐dry winter versus mild‐wet winter and long rainy season versus 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 analyse adaptive genomic diversity and phenotypic responses to environmental selection pressures that could be used to facilitate the adaptation of plant species to climate change.</description><subject>Adaptation</subject><subject>Agricultural sciences</subject><subject>agriculture</subject><subject>Annotations</subject><subject>Breeding</subject><subject>Climate</subject><subject>Climate adaptation</subject><subject>Climate change</subject><subject>Correlation analysis</subject><subject>ecological genetics</subject><subject>Genes</subject><subject>Genomes</subject><subject>Genotypes</subject><subject>Germplasm</subject><subject>landscape genetics</subject><subject>Life Sciences</subject><subject>Loci</subject><subject>Lolium perenne</subject><subject>Natural populations</subject><subject>Outliers (statistics)</subject><subject>Phenotypes</subject><subject>Plant species</subject><subject>Polygenic inheritance</subject><subject>quantitative genetics</subject><subject>Rainy season</subject><subject>Winter</subject><issn>1755-098X</issn><issn>1755-0998</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqFkb1PwzAQxS0EouVjZkOWmBja2o7jJGNVFYpUYAGJzXKdC3WV2sFOi_rf45LSlVvu9PS7p9M7hG4oGdJYI5ql6YAURT6kCcuLE9Q_KqfHOf_ooYsQVoQIUmT8HPWSJKpM5H0EE2WdNVrVWDvvoVatcTZgD1uImipV05ot4Nppg5UtcbME69pdY3RkQhNZCLh1WNdmrVrAxuIGPFhr4rrfwadXIVyhs0rVAa4P_RK9P0zfJrPB_PXxaTKeDzTnohjosiKLvNJA8zIjosgoJwUQClQrRXiyWGimRcZ4KUSVakqqihNCWZpWaclKlVyi-853qWrZ-HiR30mnjJyN53KvkYQmOS-SLY3sXcc23n1tILRy5TbexvMk4zE_kQrGIjXqKO1dCB6qoy0lcv8CuQ9Z7gOXvy-IG7cH381iDeWR_8s8AmkHfJsadv_5yefpS2f8A-E4kSo</recordid><startdate>202104</startdate><enddate>202104</enddate><creator>Blanco‐Pastor, José Luis</creator><creator>Barre, Philippe</creator><creator>Keep, Thomas</creator><creator>Ledauphin, Thomas</creator><creator>Escobar‐Gutiérrez, Abraham</creator><creator>Roschanski, Anna Maria</creator><creator>Willner, Evelyn</creator><creator>Dehmer, Klaus J.</creator><creator>Hegarty, Matthew</creator><creator>Muylle, Hilde</creator><creator>Veeckman, Elisabeth</creator><creator>Vandepoele, Klaas</creator><creator>Ruttink, Tom</creator><creator>Roldán‐Ruiz, Isabel</creator><creator>Manel, Stéphanie</creator><creator>Sampoux, Jean‐Paul</creator><general>Wiley Subscription Services, Inc</general><general>Wiley/Blackwell</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7SS</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-8902-6052</orcidid><orcidid>https://orcid.org/0000-0002-7708-1342</orcidid><orcidid>https://orcid.org/0000-0002-5111-9998</orcidid><orcidid>https://orcid.org/0000-0001-7340-3386</orcidid><orcidid>https://orcid.org/0000-0003-1466-138X</orcidid><orcidid>https://orcid.org/0000-0002-3012-7972</orcidid><orcidid>https://orcid.org/0000-0002-9196-4707</orcidid><orcidid>https://orcid.org/0000-0003-4790-2725</orcidid><orcidid>https://orcid.org/0000-0002-1012-9399</orcidid></search><sort><creationdate>202104</creationdate><title>Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass</title><author>Blanco‐Pastor, José Luis ; 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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 environmental variables and phenotypic traits. CANCOR retrieved 633 outlier loci associated with two climatic gradients, characterized by cold‐dry winter versus mild‐wet winter and long rainy season versus 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. 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subjects | Adaptation Agricultural sciences agriculture Annotations Breeding Climate Climate adaptation Climate change Correlation analysis ecological genetics Genes Genomes Genotypes Germplasm landscape genetics Life Sciences Loci Lolium perenne Natural populations Outliers (statistics) Phenotypes Plant species Polygenic inheritance quantitative genetics Rainy season Winter |
title | Canonical correlations reveal adaptive loci and phenotypic responses to climate in perennial ryegrass |
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