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Oligogenic model selection using the bayesian information criterion: Linkage analysis of the P300 Cz event-related brain potential
The traditional likelihood‐based approach to hypothesis testing may not be an optimal strategy for evaluating oligogenic models of inheritance. Under oligogenic inheritance the number of possible multilocus models can become very large; there may be several competing linkage models having similar li...
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Published in: | Genetic epidemiology 1999, Vol.17 (S1), p.S67-S72 |
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container_end_page | S72 |
container_issue | S1 |
container_start_page | S67 |
container_title | Genetic epidemiology |
container_volume | 17 |
creator | Blangero, John Williams, Jeff T. Iturria, Stephen J. Almasy, Laura |
description | The traditional likelihood‐based approach to hypothesis testing may not be an optimal strategy for evaluating oligogenic models of inheritance. Under oligogenic inheritance the number of possible multilocus models can become very large; there may be several competing linkage models having similar likelihoods; and comparisons among non‐nested models can be required to determine if a given multilocus model provides a significantly better fit to observed phenotypic variation than an alternative model. We propose an efficient Bayesian approach to oligogenic model selection that makes use of existing model likelihoods, and show how model uncertainty can be incorporated into parameter estimation. |
doi_str_mv | 10.1002/gepi.1370170712 |
format | article |
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We propose an efficient Bayesian approach to oligogenic model selection that makes use of existing model likelihoods, and show how model uncertainty can be incorporated into parameter estimation.</description><subject>Alcoholism - genetics</subject><subject>Bayes factor</subject><subject>Bayes Theorem</subject><subject>Event-Related Potentials, P300 - genetics</subject><subject>Genetic Linkage</subject><subject>Genetic Testing</subject><subject>Humans</subject><subject>hypothesis testing</subject><subject>likelihood</subject><subject>Models, Genetic</subject><subject>Models, Statistical</subject><subject>Quantitative Trait, Heritable</subject><subject>statistical genetics</subject><issn>0741-0395</issn><issn>1098-2272</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><recordid>eNqFkEFv1DAQRi0EokvhzA35yCWtJ7bXCZzQqmwrrWglQCAuluNMgqljb-0ssBz55aRN1cKJ04xm3vcdHiHPgR0BY-Vxj1t3BFwxUExB-YAsgNVVUZaqfEgWTAkoGK_lAXmS8zfGAEQtH5MDYLKefmJBfp9718ceg7N0iC16mtGjHV0MdJdd6On4FWlj9pidCdSFLqbB3LxtciOmaXtFNy5cmh6pCcbvs8s0dje5C84YXf2i-B3DWCT0ZsSWNsm4QLdxnI7O-KfkUWd8xme385B8fHvyYXVabM7XZ6s3m8IKqcrCKKkU8mapWl5JBhaE4oo3tgHbtWrZCSlqXlVlyZolr01r0NYNN8YK4IgdPyQv595tilc7zKMeXLbovQkYd1mD5KLiHGoxocczalPMOWGnt8kNJu01MH0tXl-L1_fip8SL2_JdM2D7Fz-bnoDXM_DDedz_r0-vTy7O_qkv5rTLI_68S5t0qZeTBak_vVvr9Rd4v6lOQX_mfwCtDKDr</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Blangero, John</creator><creator>Williams, Jeff T.</creator><creator>Iturria, Stephen J.</creator><creator>Almasy, Laura</creator><general>Wiley Subscription Services, Inc., A Wiley Company</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>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope></search><sort><creationdate>1999</creationdate><title>Oligogenic model selection using the bayesian information criterion: Linkage analysis of the P300 Cz event-related brain potential</title><author>Blangero, John ; Williams, Jeff T. ; Iturria, Stephen J. ; Almasy, Laura</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4572-a7577e3b67d38501c147373bcb1cfd76f4549388220b639adaec9b3aac413eef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Alcoholism - genetics</topic><topic>Bayes factor</topic><topic>Bayes Theorem</topic><topic>Event-Related Potentials, P300 - genetics</topic><topic>Genetic Linkage</topic><topic>Genetic Testing</topic><topic>Humans</topic><topic>hypothesis testing</topic><topic>likelihood</topic><topic>Models, Genetic</topic><topic>Models, Statistical</topic><topic>Quantitative Trait, Heritable</topic><topic>statistical genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Blangero, John</creatorcontrib><creatorcontrib>Williams, Jeff T.</creatorcontrib><creatorcontrib>Iturria, Stephen J.</creatorcontrib><creatorcontrib>Almasy, Laura</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>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><jtitle>Genetic epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Blangero, John</au><au>Williams, Jeff T.</au><au>Iturria, Stephen J.</au><au>Almasy, Laura</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Oligogenic model selection using the bayesian information criterion: Linkage analysis of the P300 Cz event-related brain potential</atitle><jtitle>Genetic epidemiology</jtitle><addtitle>Genet. Epidemiol</addtitle><date>1999</date><risdate>1999</risdate><volume>17</volume><issue>S1</issue><spage>S67</spage><epage>S72</epage><pages>S67-S72</pages><issn>0741-0395</issn><eissn>1098-2272</eissn><abstract>The traditional likelihood‐based approach to hypothesis testing may not be an optimal strategy for evaluating oligogenic models of inheritance. Under oligogenic inheritance the number of possible multilocus models can become very large; there may be several competing linkage models having similar likelihoods; and comparisons among non‐nested models can be required to determine if a given multilocus model provides a significantly better fit to observed phenotypic variation than an alternative model. 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subjects | Alcoholism - genetics Bayes factor Bayes Theorem Event-Related Potentials, P300 - genetics Genetic Linkage Genetic Testing Humans hypothesis testing likelihood Models, Genetic Models, Statistical Quantitative Trait, Heritable statistical genetics |
title | Oligogenic model selection using the bayesian information criterion: Linkage analysis of the P300 Cz event-related brain potential |
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