How to Measure Indirect Genetic Effects: The Congruence of Trait-Based and Variance-Partitioning Approaches

Indirect genetic effects (IGEs), which occur when phenotypic expression in one individual is influenced by genes in another conspecific individual, may have a drastic effect on evolutionary response to selection. General evolutionary models of IGEs have been developed using two distinct theoretical...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Evolution 2009-07, Vol.63 (7), p.1785-1795
Hauptverfasser: McGlothlin, Joel W, Brodie, Edmund D
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1795
container_issue 7
container_start_page 1785
container_title Evolution
container_volume 63
creator McGlothlin, Joel W
Brodie, Edmund D
description Indirect genetic effects (IGEs), which occur when phenotypic expression in one individual is influenced by genes in another conspecific individual, may have a drastic effect on evolutionary response to selection. General evolutionary models of IGEs have been developed using two distinct theoretical frameworks derived from maternal effects theory. The first framework is trait-based and focuses on how phenotypes are influenced by specific traits in a social partner, with the strength of interactions defined by the matrix Ö. The second framework partitions total genetic variance into components representing direct effects, indirect effects, and the covariance between them, without identifying specific social traits responsible for IGEs. The latter framework has been employed more commonly by empiricists because the methods for estimating variance components are relatively straightforward. Here, we show how these two theoretical frameworks are related to each other and derive equations that can be used to translate between them. This translation leads to a generalized method that can be used to estimate Ψ via standard quantitative genetic breeding designs or pedigrees from natural populations. This method can be used in a very general set of circumstances and is widely applicable to all IGEs, including maternal effects and other interactions among relatives.
doi_str_mv 10.1111/j.1558-5646.2009.00676.x
format Article
fullrecord <record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_67484440</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>40306254</jstor_id><sourcerecordid>40306254</sourcerecordid><originalsourceid>FETCH-LOGICAL-b6476-3691830998e141dfd183012134459a64b2cf1d7d52ad69ffcfb1bdf637061c3e3</originalsourceid><addsrcrecordid>eNqNkUFv1DAQhSMEokvhJ4AsDtwSbMd2EsSlrLbbSoUisRSJi-XE49bpbrzYibr993Wa1SJxob7Yo_e9sWZekiCCMxLPxzYjnJcpF0xkFOMqw1gUIts9S2YH4Xkyw5iwNC8pPkpehdDiSHJSvUyOSEUZF0U-S27P3B3qHfoKKgwe0HmnrYemR0vooLcNWhgTy_AJrW4AzV137QfoGkDOoJVXtk-_qAAaqU6jK-Wtilr6Xfne9tZ1trtGJ9utd6q5gfA6eWHUOsCb_X2c_DxdrOZn6cXl8nx-cpHWghUizUVFyhxXVQmEEW30WBFKcsZ4pQSraWOILjSnSovKmMbUpNZG5AUWpMkhP04-TH3jx38GCL3c2NDAeq06cEOQomAlYwz_F6QElwUtSATf_wO2bvBdHEJSWmBa8UeonKDGuxA8GLn1dqP8vSRYjrHJVo7pyDEdOcYmH2OTu2h9t-8_1BvQf437nCLweQLu7Brun9xYLq4u4yPa3072NvTOH-xxBVhQzqKeTroNPewOuvK3cVl5weWvb0tZ_p4T_oPmclycmPjaOtfB0wd9ADabzVU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>227029571</pqid></control><display><type>article</type><title>How to Measure Indirect Genetic Effects: The Congruence of Trait-Based and Variance-Partitioning Approaches</title><source>MEDLINE</source><source>Wiley Journals</source><source>BioOne Complete</source><source>JSTOR Archive Collection A-Z Listing</source><source>Oxford University Press Journals All Titles (1996-Current)</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>McGlothlin, Joel W ; Brodie, Edmund D</creator><creatorcontrib>McGlothlin, Joel W ; Brodie, Edmund D</creatorcontrib><description>Indirect genetic effects (IGEs), which occur when phenotypic expression in one individual is influenced by genes in another conspecific individual, may have a drastic effect on evolutionary response to selection. General evolutionary models of IGEs have been developed using two distinct theoretical frameworks derived from maternal effects theory. The first framework is trait-based and focuses on how phenotypes are influenced by specific traits in a social partner, with the strength of interactions defined by the matrix Ö. The second framework partitions total genetic variance into components representing direct effects, indirect effects, and the covariance between them, without identifying specific social traits responsible for IGEs. The latter framework has been employed more commonly by empiricists because the methods for estimating variance components are relatively straightforward. Here, we show how these two theoretical frameworks are related to each other and derive equations that can be used to translate between them. This translation leads to a generalized method that can be used to estimate Ψ via standard quantitative genetic breeding designs or pedigrees from natural populations. This method can be used in a very general set of circumstances and is widely applicable to all IGEs, including maternal effects and other interactions among relatives.</description><identifier>ISSN: 0014-3820</identifier><identifier>EISSN: 1558-5646</identifier><identifier>DOI: 10.1111/j.1558-5646.2009.00676.x</identifier><identifier>PMID: 19245673</identifier><language>eng</language><publisher>Malden, USA: Wiley/Blackwell</publisher><subject>Animal model ; Animal models ; Biological Evolution ; Covariance ; Evolution ; Evolution &amp; development ; Evolutionary biology ; Evolutionary genetics ; Gene expression ; Genetic variance ; Genetic Variation ; Genetics ; Genotype &amp; phenotype ; interacting phenotypes ; Maternal effect ; maternal effects ; Models, Genetic ; ORIGINAL ARTICLES ; Phenotype ; Phenotypes ; Phenotypic traits ; Quantitative genetics ; Selection, Genetic ; social evolution ; Social interaction</subject><ispartof>Evolution, 2009-07, Vol.63 (7), p.1785-1795</ispartof><rights>2009 The Society for the Study of Evolution.</rights><rights>Copyright 2009 The Society for the Study of Evolution</rights><rights>2009 The Author(s). Journal compilation © 2009 The Society for the Study of Evolution</rights><rights>Copyright Society for the Study of Evolution Jul 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b6476-3691830998e141dfd183012134459a64b2cf1d7d52ad69ffcfb1bdf637061c3e3</citedby><cites>FETCH-LOGICAL-b6476-3691830998e141dfd183012134459a64b2cf1d7d52ad69ffcfb1bdf637061c3e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://bioone.org/doi/pdf/10.1111/j.1558-5646.2009.00676.x$$EPDF$$P50$$Gbioone$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/40306254$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,1417,26978,27924,27925,45574,45575,52363,58017,58250</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19245673$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>McGlothlin, Joel W</creatorcontrib><creatorcontrib>Brodie, Edmund D</creatorcontrib><title>How to Measure Indirect Genetic Effects: The Congruence of Trait-Based and Variance-Partitioning Approaches</title><title>Evolution</title><addtitle>Evolution</addtitle><description>Indirect genetic effects (IGEs), which occur when phenotypic expression in one individual is influenced by genes in another conspecific individual, may have a drastic effect on evolutionary response to selection. General evolutionary models of IGEs have been developed using two distinct theoretical frameworks derived from maternal effects theory. The first framework is trait-based and focuses on how phenotypes are influenced by specific traits in a social partner, with the strength of interactions defined by the matrix Ö. The second framework partitions total genetic variance into components representing direct effects, indirect effects, and the covariance between them, without identifying specific social traits responsible for IGEs. The latter framework has been employed more commonly by empiricists because the methods for estimating variance components are relatively straightforward. Here, we show how these two theoretical frameworks are related to each other and derive equations that can be used to translate between them. This translation leads to a generalized method that can be used to estimate Ψ via standard quantitative genetic breeding designs or pedigrees from natural populations. This method can be used in a very general set of circumstances and is widely applicable to all IGEs, including maternal effects and other interactions among relatives.</description><subject>Animal model</subject><subject>Animal models</subject><subject>Biological Evolution</subject><subject>Covariance</subject><subject>Evolution</subject><subject>Evolution &amp; development</subject><subject>Evolutionary biology</subject><subject>Evolutionary genetics</subject><subject>Gene expression</subject><subject>Genetic variance</subject><subject>Genetic Variation</subject><subject>Genetics</subject><subject>Genotype &amp; phenotype</subject><subject>interacting phenotypes</subject><subject>Maternal effect</subject><subject>maternal effects</subject><subject>Models, Genetic</subject><subject>ORIGINAL ARTICLES</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Phenotypic traits</subject><subject>Quantitative genetics</subject><subject>Selection, Genetic</subject><subject>social evolution</subject><subject>Social interaction</subject><issn>0014-3820</issn><issn>1558-5646</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkUFv1DAQhSMEokvhJ4AsDtwSbMd2EsSlrLbbSoUisRSJi-XE49bpbrzYibr993Wa1SJxob7Yo_e9sWZekiCCMxLPxzYjnJcpF0xkFOMqw1gUIts9S2YH4Xkyw5iwNC8pPkpehdDiSHJSvUyOSEUZF0U-S27P3B3qHfoKKgwe0HmnrYemR0vooLcNWhgTy_AJrW4AzV137QfoGkDOoJVXtk-_qAAaqU6jK-Wtilr6Xfne9tZ1trtGJ9utd6q5gfA6eWHUOsCb_X2c_DxdrOZn6cXl8nx-cpHWghUizUVFyhxXVQmEEW30WBFKcsZ4pQSraWOILjSnSovKmMbUpNZG5AUWpMkhP04-TH3jx38GCL3c2NDAeq06cEOQomAlYwz_F6QElwUtSATf_wO2bvBdHEJSWmBa8UeonKDGuxA8GLn1dqP8vSRYjrHJVo7pyDEdOcYmH2OTu2h9t-8_1BvQf437nCLweQLu7Brun9xYLq4u4yPa3072NvTOH-xxBVhQzqKeTroNPewOuvK3cVl5weWvb0tZ_p4T_oPmclycmPjaOtfB0wd9ADabzVU</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>McGlothlin, Joel W</creator><creator>Brodie, Edmund D</creator><general>Wiley/Blackwell</general><general>Blackwell Publishing Inc</general><general>Wiley-Blackwell</general><general>Oxford University Press</general><scope>BSCLL</scope><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>7QG</scope><scope>7QL</scope><scope>7QP</scope><scope>7QR</scope><scope>7SN</scope><scope>7SS</scope><scope>7TK</scope><scope>7TM</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>200907</creationdate><title>How to Measure Indirect Genetic Effects: The Congruence of Trait-Based and Variance-Partitioning Approaches</title><author>McGlothlin, Joel W ; Brodie, Edmund D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b6476-3691830998e141dfd183012134459a64b2cf1d7d52ad69ffcfb1bdf637061c3e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Animal model</topic><topic>Animal models</topic><topic>Biological Evolution</topic><topic>Covariance</topic><topic>Evolution</topic><topic>Evolution &amp; development</topic><topic>Evolutionary biology</topic><topic>Evolutionary genetics</topic><topic>Gene expression</topic><topic>Genetic variance</topic><topic>Genetic Variation</topic><topic>Genetics</topic><topic>Genotype &amp; phenotype</topic><topic>interacting phenotypes</topic><topic>Maternal effect</topic><topic>maternal effects</topic><topic>Models, Genetic</topic><topic>ORIGINAL ARTICLES</topic><topic>Phenotype</topic><topic>Phenotypes</topic><topic>Phenotypic traits</topic><topic>Quantitative genetics</topic><topic>Selection, Genetic</topic><topic>social evolution</topic><topic>Social interaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McGlothlin, Joel W</creatorcontrib><creatorcontrib>Brodie, Edmund D</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Evolution</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McGlothlin, Joel W</au><au>Brodie, Edmund D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>How to Measure Indirect Genetic Effects: The Congruence of Trait-Based and Variance-Partitioning Approaches</atitle><jtitle>Evolution</jtitle><addtitle>Evolution</addtitle><date>2009-07</date><risdate>2009</risdate><volume>63</volume><issue>7</issue><spage>1785</spage><epage>1795</epage><pages>1785-1795</pages><issn>0014-3820</issn><eissn>1558-5646</eissn><abstract>Indirect genetic effects (IGEs), which occur when phenotypic expression in one individual is influenced by genes in another conspecific individual, may have a drastic effect on evolutionary response to selection. General evolutionary models of IGEs have been developed using two distinct theoretical frameworks derived from maternal effects theory. The first framework is trait-based and focuses on how phenotypes are influenced by specific traits in a social partner, with the strength of interactions defined by the matrix Ö. The second framework partitions total genetic variance into components representing direct effects, indirect effects, and the covariance between them, without identifying specific social traits responsible for IGEs. The latter framework has been employed more commonly by empiricists because the methods for estimating variance components are relatively straightforward. Here, we show how these two theoretical frameworks are related to each other and derive equations that can be used to translate between them. This translation leads to a generalized method that can be used to estimate Ψ via standard quantitative genetic breeding designs or pedigrees from natural populations. This method can be used in a very general set of circumstances and is widely applicable to all IGEs, including maternal effects and other interactions among relatives.</abstract><cop>Malden, USA</cop><pub>Wiley/Blackwell</pub><pmid>19245673</pmid><doi>10.1111/j.1558-5646.2009.00676.x</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0014-3820
ispartof Evolution, 2009-07, Vol.63 (7), p.1785-1795
issn 0014-3820
1558-5646
language eng
recordid cdi_proquest_miscellaneous_67484440
source MEDLINE; Wiley Journals; BioOne Complete; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals
subjects Animal model
Animal models
Biological Evolution
Covariance
Evolution
Evolution & development
Evolutionary biology
Evolutionary genetics
Gene expression
Genetic variance
Genetic Variation
Genetics
Genotype & phenotype
interacting phenotypes
Maternal effect
maternal effects
Models, Genetic
ORIGINAL ARTICLES
Phenotype
Phenotypes
Phenotypic traits
Quantitative genetics
Selection, Genetic
social evolution
Social interaction
title How to Measure Indirect Genetic Effects: The Congruence of Trait-Based and Variance-Partitioning Approaches
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T09%3A42%3A48IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=How%20to%20Measure%20Indirect%20Genetic%20Effects:%20The%20Congruence%20of%20Trait-Based%20and%20Variance-Partitioning%20Approaches&rft.jtitle=Evolution&rft.au=McGlothlin,%20Joel%20W&rft.date=2009-07&rft.volume=63&rft.issue=7&rft.spage=1785&rft.epage=1795&rft.pages=1785-1795&rft.issn=0014-3820&rft.eissn=1558-5646&rft_id=info:doi/10.1111/j.1558-5646.2009.00676.x&rft_dat=%3Cjstor_proqu%3E40306254%3C/jstor_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=227029571&rft_id=info:pmid/19245673&rft_jstor_id=40306254&rfr_iscdi=true