A graphical approach to relatedness inference

The estimation of relatedness structure in natural populations using molecular marker data has become an important tool in population biology, resulting in a variety of estimation procedures for specific sampling scenarios. In this article a general approach is proposed, in which the detailed relati...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Theoretical population biology 2007-03, Vol.71 (2), p.213-229
1. Verfasser: Almudevar, Anthony
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 229
container_issue 2
container_start_page 213
container_title Theoretical population biology
container_volume 71
creator Almudevar, Anthony
description The estimation of relatedness structure in natural populations using molecular marker data has become an important tool in population biology, resulting in a variety of estimation procedures for specific sampling scenarios. In this article a general approach is proposed, in which the detailed relationship structure, typically a pedigree graph or partition, is considered to be the object of inference. This makes available tools used in complex model selection theory which have demonstrated effectiveness. An important advantage of this approach is that it permits a fully Bayesian approach to the problem, providing a principled and accessible way to measure statistical error. The approach is demonstrated by applying the minimum description length principle. This technique is used in model selection to provide a rational way of comparing models of varying complexity. We show how the resulting score may be interpreted and applied as a Bayesian posterior density.
doi_str_mv 10.1016/j.tpb.2006.10.005
format Article
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1995094</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0040580906001328</els_id><sourcerecordid>19576219</sourcerecordid><originalsourceid>FETCH-LOGICAL-c546t-52f7a9235634414f2e204aea0016b348996e1991852a190340d624db9b6197943</originalsourceid><addsrcrecordid>eNqFkV1r2zAUhsXoWNN0P2A3xVe9c3aOvmwxKISyrYNCb9ZrIcvHiYJje5IT6L-v04R1u2mvhKRHD0fvy9gXhAUC6q-bxThUCw6gp_0CQH1gMwSjcxBcnbEZgIRclWDO2UVKGwAoUYhP7BwL1EYYnLF8ma2iG9bBuzZzwxB759fZ2GeRWjdS3VFKWegaitR5umQfG9cm-nxa5-zxx_fft3f5_cPPX7fL-9wrqcdc8aZwhgulhZQoG04cpCMH09CVkKUxmtAYLBV3aEBIqDWXdWUqjaYwUszZzdE77Kot1Z66MbrWDjFsXXyyvQv2_5surO2q39vJquBFcH0SxP7PjtJotyF5alvXUb9LVpemLApQ74JoVKE5mgnEI-hjn1Kk5u80CPbQht3YqQ17aONwBC_yq3-_8friFP8EfDsCNIW5DxRt8uEQdB0i-dHWfXhD_wxfWZjH</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>19576219</pqid></control><display><type>article</type><title>A graphical approach to relatedness inference</title><source>MEDLINE</source><source>Access via ScienceDirect (Elsevier)</source><creator>Almudevar, Anthony</creator><creatorcontrib>Almudevar, Anthony</creatorcontrib><description>The estimation of relatedness structure in natural populations using molecular marker data has become an important tool in population biology, resulting in a variety of estimation procedures for specific sampling scenarios. In this article a general approach is proposed, in which the detailed relationship structure, typically a pedigree graph or partition, is considered to be the object of inference. This makes available tools used in complex model selection theory which have demonstrated effectiveness. An important advantage of this approach is that it permits a fully Bayesian approach to the problem, providing a principled and accessible way to measure statistical error. The approach is demonstrated by applying the minimum description length principle. This technique is used in model selection to provide a rational way of comparing models of varying complexity. We show how the resulting score may be interpreted and applied as a Bayesian posterior density.</description><identifier>ISSN: 0040-5809</identifier><identifier>EISSN: 1096-0325</identifier><identifier>DOI: 10.1016/j.tpb.2006.10.005</identifier><identifier>PMID: 17169391</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Bayes Theorem ; Bayesian inference ; Computer Simulation ; Graphical models ; Humans ; Mathematics ; Minimum description length ; Models, Biological ; Pedigree ; Pedigree reconstruction ; Population Density ; Population Dynamics</subject><ispartof>Theoretical population biology, 2007-03, Vol.71 (2), p.213-229</ispartof><rights>2006 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c546t-52f7a9235634414f2e204aea0016b348996e1991852a190340d624db9b6197943</citedby><cites>FETCH-LOGICAL-c546t-52f7a9235634414f2e204aea0016b348996e1991852a190340d624db9b6197943</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.tpb.2006.10.005$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/17169391$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Almudevar, Anthony</creatorcontrib><title>A graphical approach to relatedness inference</title><title>Theoretical population biology</title><addtitle>Theor Popul Biol</addtitle><description>The estimation of relatedness structure in natural populations using molecular marker data has become an important tool in population biology, resulting in a variety of estimation procedures for specific sampling scenarios. In this article a general approach is proposed, in which the detailed relationship structure, typically a pedigree graph or partition, is considered to be the object of inference. This makes available tools used in complex model selection theory which have demonstrated effectiveness. An important advantage of this approach is that it permits a fully Bayesian approach to the problem, providing a principled and accessible way to measure statistical error. The approach is demonstrated by applying the minimum description length principle. This technique is used in model selection to provide a rational way of comparing models of varying complexity. We show how the resulting score may be interpreted and applied as a Bayesian posterior density.</description><subject>Bayes Theorem</subject><subject>Bayesian inference</subject><subject>Computer Simulation</subject><subject>Graphical models</subject><subject>Humans</subject><subject>Mathematics</subject><subject>Minimum description length</subject><subject>Models, Biological</subject><subject>Pedigree</subject><subject>Pedigree reconstruction</subject><subject>Population Density</subject><subject>Population Dynamics</subject><issn>0040-5809</issn><issn>1096-0325</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkV1r2zAUhsXoWNN0P2A3xVe9c3aOvmwxKISyrYNCb9ZrIcvHiYJje5IT6L-v04R1u2mvhKRHD0fvy9gXhAUC6q-bxThUCw6gp_0CQH1gMwSjcxBcnbEZgIRclWDO2UVKGwAoUYhP7BwL1EYYnLF8ma2iG9bBuzZzwxB759fZ2GeRWjdS3VFKWegaitR5umQfG9cm-nxa5-zxx_fft3f5_cPPX7fL-9wrqcdc8aZwhgulhZQoG04cpCMH09CVkKUxmtAYLBV3aEBIqDWXdWUqjaYwUszZzdE77Kot1Z66MbrWDjFsXXyyvQv2_5surO2q39vJquBFcH0SxP7PjtJotyF5alvXUb9LVpemLApQ74JoVKE5mgnEI-hjn1Kk5u80CPbQht3YqQ17aONwBC_yq3-_8friFP8EfDsCNIW5DxRt8uEQdB0i-dHWfXhD_wxfWZjH</recordid><startdate>20070301</startdate><enddate>20070301</enddate><creator>Almudevar, Anthony</creator><general>Elsevier Inc</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>7SN</scope><scope>C1K</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20070301</creationdate><title>A graphical approach to relatedness inference</title><author>Almudevar, Anthony</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c546t-52f7a9235634414f2e204aea0016b348996e1991852a190340d624db9b6197943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Bayes Theorem</topic><topic>Bayesian inference</topic><topic>Computer Simulation</topic><topic>Graphical models</topic><topic>Humans</topic><topic>Mathematics</topic><topic>Minimum description length</topic><topic>Models, Biological</topic><topic>Pedigree</topic><topic>Pedigree reconstruction</topic><topic>Population Density</topic><topic>Population Dynamics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Almudevar, Anthony</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Theoretical population biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Almudevar, Anthony</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A graphical approach to relatedness inference</atitle><jtitle>Theoretical population biology</jtitle><addtitle>Theor Popul Biol</addtitle><date>2007-03-01</date><risdate>2007</risdate><volume>71</volume><issue>2</issue><spage>213</spage><epage>229</epage><pages>213-229</pages><issn>0040-5809</issn><eissn>1096-0325</eissn><abstract>The estimation of relatedness structure in natural populations using molecular marker data has become an important tool in population biology, resulting in a variety of estimation procedures for specific sampling scenarios. In this article a general approach is proposed, in which the detailed relationship structure, typically a pedigree graph or partition, is considered to be the object of inference. This makes available tools used in complex model selection theory which have demonstrated effectiveness. An important advantage of this approach is that it permits a fully Bayesian approach to the problem, providing a principled and accessible way to measure statistical error. The approach is demonstrated by applying the minimum description length principle. This technique is used in model selection to provide a rational way of comparing models of varying complexity. We show how the resulting score may be interpreted and applied as a Bayesian posterior density.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>17169391</pmid><doi>10.1016/j.tpb.2006.10.005</doi><tpages>17</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0040-5809
ispartof Theoretical population biology, 2007-03, Vol.71 (2), p.213-229
issn 0040-5809
1096-0325
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1995094
source MEDLINE; Access via ScienceDirect (Elsevier)
subjects Bayes Theorem
Bayesian inference
Computer Simulation
Graphical models
Humans
Mathematics
Minimum description length
Models, Biological
Pedigree
Pedigree reconstruction
Population Density
Population Dynamics
title A graphical approach to relatedness inference
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T16%3A09%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20graphical%20approach%20to%20relatedness%20inference&rft.jtitle=Theoretical%20population%20biology&rft.au=Almudevar,%20Anthony&rft.date=2007-03-01&rft.volume=71&rft.issue=2&rft.spage=213&rft.epage=229&rft.pages=213-229&rft.issn=0040-5809&rft.eissn=1096-0325&rft_id=info:doi/10.1016/j.tpb.2006.10.005&rft_dat=%3Cproquest_pubme%3E19576219%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=19576219&rft_id=info:pmid/17169391&rft_els_id=S0040580906001328&rfr_iscdi=true