A tale of two matrices: multivariate approaches in evolutionary biology

Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (γ) which describes the individual fitness surface. The second is the genetic variance–covariance matrix (G) that influences the multivariate response to selection....

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of evolutionary biology 2007-01, Vol.20 (1), p.1-8
1. Verfasser: BLOWS, M. W.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 8
container_issue 1
container_start_page 1
container_title Journal of evolutionary biology
container_volume 20
creator BLOWS, M. W.
description Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (γ) which describes the individual fitness surface. The second is the genetic variance–covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element‐by‐element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of γ and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.
doi_str_mv 10.1111/j.1420-9101.2006.01164.x
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_68412959</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>68412959</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4834-9f1342321d2ddffa72b73a454359c25e8a798cdb9e1e6d59ddf3a6462afb09f13</originalsourceid><addsrcrecordid>eNqNkD1PwzAQhi0EolD4C8gTW4LtOG6MxFCqUkCVWEBis5zkAq6cusRJP_49Dq1g5ZY7yc97Pj0IYUpiGupmEVPOSCQpoTEjRMSEUsHj7RE6-304DjOhJCKCvg_QufcLQgKUpqdoQEeMSJmJMzQb41ZbwK7C7cbhWreNKcDf4rqzrVnrxugWsF6tGqeLT_DYLDGsne1a45a62eHcOOs-dhfopNLWw-WhD9Hbw_R18hjNX2ZPk_E8KniW8EhWNOEsYbRkZVlVesTyUaJ5ypNUFiyFTI9kVpS5BAqiTGWAEi24YLrKSR8eouv93nDQVwe-VbXxBVirl-A6r0TGKZOpDGC2B4vGed9ApVaNqcPFihLVS1QL1btSvSvVS1Q_EtU2RK8Of3R5DeVf8GAtAHd7YGMs7P69WD1P7_sp-QaKVIB5</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>68412959</pqid></control><display><type>article</type><title>A tale of two matrices: multivariate approaches in evolutionary biology</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Access via Wiley Online Library</source><source>Oxford University Press Journals All Titles (1996-Current)</source><source>Alma/SFX Local Collection</source><creator>BLOWS, M. W.</creator><creatorcontrib>BLOWS, M. W.</creatorcontrib><description>Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (γ) which describes the individual fitness surface. The second is the genetic variance–covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element‐by‐element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of γ and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.</description><identifier>ISSN: 1010-061X</identifier><identifier>EISSN: 1420-9101</identifier><identifier>DOI: 10.1111/j.1420-9101.2006.01164.x</identifier><identifier>PMID: 17209986</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Biological Evolution ; Data Interpretation, Statistical ; genetic constraints ; genetic variance‐covariance matrix ; Genetic Variation ; Multivariate Analysis ; nonlinear selection ; Quantitative Trait, Heritable ; Selection, Genetic ; stabilizing selection</subject><ispartof>Journal of evolutionary biology, 2007-01, Vol.20 (1), p.1-8</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4834-9f1342321d2ddffa72b73a454359c25e8a798cdb9e1e6d59ddf3a6462afb09f13</citedby><cites>FETCH-LOGICAL-c4834-9f1342321d2ddffa72b73a454359c25e8a798cdb9e1e6d59ddf3a6462afb09f13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fj.1420-9101.2006.01164.x$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fj.1420-9101.2006.01164.x$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,782,786,1419,27931,27932,45581,45582</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17209986$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>BLOWS, M. W.</creatorcontrib><title>A tale of two matrices: multivariate approaches in evolutionary biology</title><title>Journal of evolutionary biology</title><addtitle>J Evol Biol</addtitle><description>Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (γ) which describes the individual fitness surface. The second is the genetic variance–covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element‐by‐element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of γ and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.</description><subject>Biological Evolution</subject><subject>Data Interpretation, Statistical</subject><subject>genetic constraints</subject><subject>genetic variance‐covariance matrix</subject><subject>Genetic Variation</subject><subject>Multivariate Analysis</subject><subject>nonlinear selection</subject><subject>Quantitative Trait, Heritable</subject><subject>Selection, Genetic</subject><subject>stabilizing selection</subject><issn>1010-061X</issn><issn>1420-9101</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkD1PwzAQhi0EolD4C8gTW4LtOG6MxFCqUkCVWEBis5zkAq6cusRJP_49Dq1g5ZY7yc97Pj0IYUpiGupmEVPOSCQpoTEjRMSEUsHj7RE6-304DjOhJCKCvg_QufcLQgKUpqdoQEeMSJmJMzQb41ZbwK7C7cbhWreNKcDf4rqzrVnrxugWsF6tGqeLT_DYLDGsne1a45a62eHcOOs-dhfopNLWw-WhD9Hbw_R18hjNX2ZPk_E8KniW8EhWNOEsYbRkZVlVesTyUaJ5ypNUFiyFTI9kVpS5BAqiTGWAEi24YLrKSR8eouv93nDQVwe-VbXxBVirl-A6r0TGKZOpDGC2B4vGed9ApVaNqcPFihLVS1QL1btSvSvVS1Q_EtU2RK8Of3R5DeVf8GAtAHd7YGMs7P69WD1P7_sp-QaKVIB5</recordid><startdate>200701</startdate><enddate>200701</enddate><creator>BLOWS, M. W.</creator><general>Blackwell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>200701</creationdate><title>A tale of two matrices: multivariate approaches in evolutionary biology</title><author>BLOWS, M. W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4834-9f1342321d2ddffa72b73a454359c25e8a798cdb9e1e6d59ddf3a6462afb09f13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Biological Evolution</topic><topic>Data Interpretation, Statistical</topic><topic>genetic constraints</topic><topic>genetic variance‐covariance matrix</topic><topic>Genetic Variation</topic><topic>Multivariate Analysis</topic><topic>nonlinear selection</topic><topic>Quantitative Trait, Heritable</topic><topic>Selection, Genetic</topic><topic>stabilizing selection</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>BLOWS, M. W.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of evolutionary biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>BLOWS, M. W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A tale of two matrices: multivariate approaches in evolutionary biology</atitle><jtitle>Journal of evolutionary biology</jtitle><addtitle>J Evol Biol</addtitle><date>2007-01</date><risdate>2007</risdate><volume>20</volume><issue>1</issue><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1010-061X</issn><eissn>1420-9101</eissn><abstract>Two symmetric matrices underlie our understanding of microevolutionary change. The first is the matrix of nonlinear selection gradients (γ) which describes the individual fitness surface. The second is the genetic variance–covariance matrix (G) that influences the multivariate response to selection. A common approach to the empirical analysis of these matrices is the element‐by‐element testing of significance, and subsequent biological interpretation of pattern based on these univariate and bivariate parameters. Here, I show why this approach is likely to misrepresent the genetic basis of quantitative traits, and the selection acting on them in many cases. Diagonalization of square matrices is a fundamental aspect of many of the multivariate statistical techniques used by biologists. Applying this, and other related approaches, to the analysis of the structure of γ and G matrices, gives greater insight into the form and strength of nonlinear selection, and the availability of genetic variance for multiple traits.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>17209986</pmid><doi>10.1111/j.1420-9101.2006.01164.x</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1010-061X
ispartof Journal of evolutionary biology, 2007-01, Vol.20 (1), p.1-8
issn 1010-061X
1420-9101
language eng
recordid cdi_proquest_miscellaneous_68412959
source MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Access via Wiley Online Library; Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection
subjects Biological Evolution
Data Interpretation, Statistical
genetic constraints
genetic variance‐covariance matrix
Genetic Variation
Multivariate Analysis
nonlinear selection
Quantitative Trait, Heritable
Selection, Genetic
stabilizing selection
title A tale of two matrices: multivariate approaches in evolutionary biology
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-05T18%3A48%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20tale%20of%20two%20matrices:%20multivariate%20approaches%20in%20evolutionary%20biology&rft.jtitle=Journal%20of%20evolutionary%20biology&rft.au=BLOWS,%20M.%20W.&rft.date=2007-01&rft.volume=20&rft.issue=1&rft.spage=1&rft.epage=8&rft.pages=1-8&rft.issn=1010-061X&rft.eissn=1420-9101&rft_id=info:doi/10.1111/j.1420-9101.2006.01164.x&rft_dat=%3Cproquest_cross%3E68412959%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=68412959&rft_id=info:pmid/17209986&rfr_iscdi=true