Evaluation of redundancy analysis to identify signatures of local adaptation.
Identifieur interne : 000F17 ( Main/Exploration ); précédent : 000F16; suivant : 000F18Evaluation of redundancy analysis to identify signatures of local adaptation.
Auteurs : Thibaut Capblancq [France] ; Keurcien Luu [France] ; Michael G B. Blum [France] ; Eric Bazin [France]Source :
- Molecular ecology resources [ 1755-0998 ] ; 2018.
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Abstract
Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This study aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. In addition, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., ). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the north-western American coast.
DOI: 10.1111/1755-0998.12906
PubMed: 29802785
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In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This study aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. In addition, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., ). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the north-western American coast.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29802785</PMID><DateCompleted><Year>2018</Year><Month>12</Month><Day>17</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>17</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1755-0998</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>6</Issue><PubDate><Year>2018</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Molecular ecology resources</Title><ISOAbbreviation>Mol Ecol Resour</ISOAbbreviation></Journal><ArticleTitle>Evaluation of redundancy analysis to identify signatures of local adaptation.</ArticleTitle><Pagination><MedlinePgn>1223-1233</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1111/1755-0998.12906</ELocationID><Abstract><AbstractText>Ordination is a common tool in ecology that aims at representing complex biological information in a reduced space. In landscape genetics, ordination methods such as principal component analysis (PCA) have been used to detect adaptive variation based on genomic data. Taking advantage of environmental data in addition to genotype data, redundancy analysis (RDA) is another ordination approach that is useful to detect adaptive variation. This study aims at proposing a test statistic based on RDA to search for loci under selection. We compare redundancy analysis to pcadapt, which is a nonconstrained ordination method, and to a latent factor mixed model (LFMM), which is a univariate genotype-environment association method. Individual-based simulations identify evolutionary scenarios where RDA genome scans have a greater statistical power than genome scans based on PCA. By constraining the analysis with environmental variables, RDA performs better than PCA in identifying adaptive variation when selection gradients are weakly correlated with population structure. In addition, we show that if RDA and LFMM have a similar power to identify genetic markers associated with environmental variables, the RDA-based procedure has the advantage to identify the main selective gradients as a combination of environmental variables. To give a concrete illustration of RDA in population genomics, we apply this method to the detection of outliers and selective gradients on an SNP data set of Populus trichocarpa (Geraldes et al., ). The RDA-based approach identifies the main selective gradient contrasting southern and coastal populations to northern and continental populations in the north-western American coast.</AbstractText><CopyrightInformation>© 2018 John Wiley & Sons Ltd.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Capblancq</LastName><ForeName>Thibaut</ForeName><Initials>T</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-5024-1302</Identifier><AffiliationInfo><Affiliation>CNRS, LECA UMR 5553, Univ. Grenoble Alpes, Grenoble, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Luu</LastName><ForeName>Keurcien</ForeName><Initials>K</Initials><AffiliationInfo><Affiliation>CNRS, TIMC-IMAG UMR 5525, Univ. Grenoble Alpes, Grenoble, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Blum</LastName><ForeName>Michael G B</ForeName><Initials>MGB</Initials><AffiliationInfo><Affiliation>CNRS, TIMC-IMAG UMR 5525, Univ. Grenoble Alpes, Grenoble, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bazin</LastName><ForeName>Eric</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>CNRS, LECA UMR 5553, Univ. Grenoble Alpes, Grenoble, France.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>ANR-15-CE02-0004</GrantID><Agency>Agence Nationale de la Recherche</Agency><Country></Country></Grant><Grant><GrantID>ANR-15-IDEX-02</GrantID><Agency>Agence Nationale de la Recherche</Agency><Country></Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>06</Month><Day>17</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Mol Ecol Resour</MedlineTA><NlmUniqueID>101465604</NlmUniqueID><ISSNLinking>1755-098X</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000220" MajorTopicYN="Y">Adaptation, Biological</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D056808" MajorTopicYN="N">Biostatistics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D019295" MajorTopicYN="N">Computational Biology</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="N">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D056426" MajorTopicYN="N">Genetic Loci</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014644" MajorTopicYN="Y">Genetic Variation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005828" MajorTopicYN="N">Genetics, Population</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D023281" MajorTopicYN="N">Genomics</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005838" MajorTopicYN="N">Genotype</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020641" MajorTopicYN="N">Polymorphism, Single Nucleotide</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">biological adaptation</Keyword><Keyword MajorTopicYN="N">environmental variables</Keyword><Keyword MajorTopicYN="N">genome scans</Keyword><Keyword MajorTopicYN="N">multivariate analysis</Keyword><Keyword MajorTopicYN="N">redundancy analysis</Keyword><Keyword MajorTopicYN="N">selection</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2018</Year><Month>02</Month><Day>05</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2018</Year><Month>05</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2018</Year><Month>05</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2018</Year><Month>5</Month><Day>29</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2018</Year><Month>12</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2018</Year><Month>5</Month><Day>27</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">29802785</ArticleId><ArticleId IdType="doi">10.1111/1755-0998.12906</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li></region><settlement><li>Grenoble</li></settlement></list><tree><country name="France"><region name="Auvergne-Rhône-Alpes"><name sortKey="Capblancq, Thibaut" sort="Capblancq, Thibaut" uniqKey="Capblancq T" first="Thibaut" last="Capblancq">Thibaut Capblancq</name></region><name sortKey="Bazin, Eric" sort="Bazin, Eric" uniqKey="Bazin E" first="Eric" last="Bazin">Eric Bazin</name><name sortKey="Blum, Michael G B" sort="Blum, Michael G B" uniqKey="Blum M" first="Michael G B" last="Blum">Michael G B. Blum</name><name sortKey="Luu, Keurcien" sort="Luu, Keurcien" uniqKey="Luu K" first="Keurcien" last="Luu">Keurcien Luu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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