Loading…

A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples

Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-...

Full description

Saved in:

Bibliographic Details
Published in:	Biometrics 2013-06, Vol.69 (2), p.417-426
Main Authors:	Wu, Colin O., Zheng, Gang, Kwak, Minjung
Format:	Article
Language:	English
Subjects:	Arthritis, Rheumatoid - genetics Arthritis, Rheumatoid - immunology Autoantibodies - blood Autoantibodies - genetics BIOMETRIC METHODOLOGY Biometry - methods Biostatistics Genetic Association Studies - statistics & numerical data Genetic association study Genetic research Humans Joint regression model Likelihood Functions Models, Genetic Models, Statistical Peptides, Cyclic - immunology Pleiotropic analysis Polymorphism, Single Nucleotide Qualitative trait Quantitative trait Quantitative Trait Loci Regression Analysis Stratified sample
Citations:	Items that this one cites Items that cite this one
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by	cdi_FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53
cites	cdi_FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53
container_end_page	426
container_issue	2
container_start_page	417
container_title	Biometrics
container_volume	69
creator	Wu, Colin O. Zheng, Gang Kwak, Minjung
description	Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose in this article an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16).
doi_str_mv	10.1111/biom.12012
format	article
fullrecord	<record><control><sourceid>jstor_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_1372081238</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>44695258</jstor_id><sourcerecordid>44695258</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53</originalsourceid><addsrcrecordid>eNp9kM9v0zAYhi0EYmXjwh0UaReElOHfSY9lGmVoo4OCQHCwnOQzuCRxsR2N_vc4y9bDDvhi2e_zPdL3IvSM4BOSzuvKuu6EUEzoAzQjgpMcc4ofohnGWOaMk28H6EkIm_ScC0wfowPKeDknWMzQj0X23tk-Zp_gp4cQrOuzRa_bXbAhM85nS-gh2jpbhOBqq-MIrOPQWAjZtY2_stUQa9dB-vQpNRaabK27bQvhCD0yug3w9PY-RF_enn0-fZdfrJbnp4uLvOakoDkVRhteNYZQghswTUWxLKtSckFwpRvNmYS60IRBzUFCxU1hhGQNY8BlJdghejl5t979GSBE1dlQQ9vqHtwQFGEFxSWhrEzo8T104waf9r2hEpSaG4WvJqr2LgQPRm297bTfKYLVWLkaK1c3lSf4xa1yqDpo9uhdxwkgE3BtW9j9R6XenK8u76TPp5lNiM7vZziXc0HFuEc-5TZE-LvPtf-tZMEKob5-WKo1vvp--ZEJdcX-Acz5pFw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1370810425</pqid></control><display><type>article</type><title>A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples</title><source>EBSCOhost SPORTDiscus with Full Text</source><source>JSTOR Archival Journals and Primary Sources Collection</source><source>Oxford Journals Online</source><creator>Wu, Colin O. ; Zheng, Gang ; Kwak, Minjung</creator><creatorcontrib>Wu, Colin O. ; Zheng, Gang ; Kwak, Minjung</creatorcontrib><description>Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose in this article an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16).</description><identifier>ISSN: 0006-341X</identifier><identifier>EISSN: 1541-0420</identifier><identifier>DOI: 10.1111/biom.12012</identifier><identifier>PMID: 23489105</identifier><identifier>CODEN: BIOMA5</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Arthritis, Rheumatoid - genetics ; Arthritis, Rheumatoid - immunology ; Autoantibodies - blood ; Autoantibodies - genetics ; BIOMETRIC METHODOLOGY ; Biometry - methods ; Biostatistics ; Genetic Association Studies - statistics & numerical data ; Genetic association study ; Genetic research ; Humans ; Joint regression model ; Likelihood Functions ; Models, Genetic ; Models, Statistical ; Peptides, Cyclic - immunology ; Pleiotropic analysis ; Polymorphism, Single Nucleotide ; Qualitative trait ; Quantitative trait ; Quantitative Trait Loci ; Regression Analysis ; Stratified sample</subject><ispartof>Biometrics, 2013-06, Vol.69 (2), p.417-426</ispartof><rights>2013 International Biometric Society</rights><rights>2013, The International Biometric Society</rights><rights>2013, The International Biometric Society.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53</citedby><cites>FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/44695258$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/44695258$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,58238,58471</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23489105$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Colin O.</creatorcontrib><creatorcontrib>Zheng, Gang</creatorcontrib><creatorcontrib>Kwak, Minjung</creatorcontrib><title>A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples</title><title>Biometrics</title><addtitle>Biom</addtitle><description>Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose in this article an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16).</description><subject>Arthritis, Rheumatoid - genetics</subject><subject>Arthritis, Rheumatoid - immunology</subject><subject>Autoantibodies - blood</subject><subject>Autoantibodies - genetics</subject><subject>BIOMETRIC METHODOLOGY</subject><subject>Biometry - methods</subject><subject>Biostatistics</subject><subject>Genetic Association Studies - statistics & numerical data</subject><subject>Genetic association study</subject><subject>Genetic research</subject><subject>Humans</subject><subject>Joint regression model</subject><subject>Likelihood Functions</subject><subject>Models, Genetic</subject><subject>Models, Statistical</subject><subject>Peptides, Cyclic - immunology</subject><subject>Pleiotropic analysis</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Qualitative trait</subject><subject>Quantitative trait</subject><subject>Quantitative Trait Loci</subject><subject>Regression Analysis</subject><subject>Stratified sample</subject><issn>0006-341X</issn><issn>1541-0420</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kM9v0zAYhi0EYmXjwh0UaReElOHfSY9lGmVoo4OCQHCwnOQzuCRxsR2N_vc4y9bDDvhi2e_zPdL3IvSM4BOSzuvKuu6EUEzoAzQjgpMcc4ofohnGWOaMk28H6EkIm_ScC0wfowPKeDknWMzQj0X23tk-Zp_gp4cQrOuzRa_bXbAhM85nS-gh2jpbhOBqq-MIrOPQWAjZtY2_stUQa9dB-vQpNRaabK27bQvhCD0yug3w9PY-RF_enn0-fZdfrJbnp4uLvOakoDkVRhteNYZQghswTUWxLKtSckFwpRvNmYS60IRBzUFCxU1hhGQNY8BlJdghejl5t979GSBE1dlQQ9vqHtwQFGEFxSWhrEzo8T104waf9r2hEpSaG4WvJqr2LgQPRm297bTfKYLVWLkaK1c3lSf4xa1yqDpo9uhdxwkgE3BtW9j9R6XenK8u76TPp5lNiM7vZziXc0HFuEc-5TZE-LvPtf-tZMEKob5-WKo1vvp--ZEJdcX-Acz5pFw</recordid><startdate>201306</startdate><enddate>201306</enddate><creator>Wu, Colin O.</creator><creator>Zheng, Gang</creator><creator>Kwak, Minjung</creator><general>Blackwell Publishing Ltd</general><general>Wiley-Blackwell</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>JQ2</scope><scope>7X8</scope></search><sort><creationdate>201306</creationdate><title>A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples</title><author>Wu, Colin O. ; Zheng, Gang ; Kwak, Minjung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Arthritis, Rheumatoid - genetics</topic><topic>Arthritis, Rheumatoid - immunology</topic><topic>Autoantibodies - blood</topic><topic>Autoantibodies - genetics</topic><topic>BIOMETRIC METHODOLOGY</topic><topic>Biometry - methods</topic><topic>Biostatistics</topic><topic>Genetic Association Studies - statistics & numerical data</topic><topic>Genetic association study</topic><topic>Genetic research</topic><topic>Humans</topic><topic>Joint regression model</topic><topic>Likelihood Functions</topic><topic>Models, Genetic</topic><topic>Models, Statistical</topic><topic>Peptides, Cyclic - immunology</topic><topic>Pleiotropic analysis</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Qualitative trait</topic><topic>Quantitative trait</topic><topic>Quantitative Trait Loci</topic><topic>Regression Analysis</topic><topic>Stratified sample</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Colin O.</creatorcontrib><creatorcontrib>Zheng, Gang</creatorcontrib><creatorcontrib>Kwak, Minjung</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>ProQuest Computer Science Collection</collection><collection>MEDLINE - Academic</collection><jtitle>Biometrics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Colin O.</au><au>Zheng, Gang</au><au>Kwak, Minjung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples</atitle><jtitle>Biometrics</jtitle><addtitle>Biom</addtitle><date>2013-06</date><risdate>2013</risdate><volume>69</volume><issue>2</issue><spage>417</spage><epage>426</epage><pages>417-426</pages><issn>0006-341X</issn><eissn>1541-0420</eissn><coden>BIOMA5</coden><abstract>Genetic association studies in practice often involve multiple traits resulting from a common disease mechanism, and samples for such studies are often stratified based on some trait outcomes. In such situations, statistical methods using only one of these traits may be inadequate and lead to under-powered tests for detecting genetic associations. We propose in this article an estimation and testing procedure for evaluating the shared-association of a genetic marker on the joint distribution of multiple traits of a common disease. Specifically, we assume that the disease mechanism involves both quantitative and qualitative traits, and our samples could be stratified based on the qualitative trait. Through a joint likelihood function, we derive a class of estimators and test statistics for evaluating the shared genetic association on both the quantitative and qualitative traits. Our simulation study shows that the joint likelihood test procedure is potentially more powerful than association tests based on separate traits. Application of our proposed procedure is demonstrated through the rheumatoid arthritis data provided by the Genetic Analysis Workshop 16 (GAW16).</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>23489105</pmid><doi>10.1111/biom.12012</doi><tpages>10</tpages></addata></record>
fulltext	fulltext
identifier	ISSN: 0006-341X
ispartof	Biometrics, 2013-06, Vol.69 (2), p.417-426
issn	0006-341X 1541-0420
language	eng
recordid	cdi_proquest_miscellaneous_1372081238
source	EBSCOhost SPORTDiscus with Full Text; JSTOR Archival Journals and Primary Sources Collection; Oxford Journals Online
subjects	Arthritis, Rheumatoid - genetics Arthritis, Rheumatoid - immunology Autoantibodies - blood Autoantibodies - genetics BIOMETRIC METHODOLOGY Biometry - methods Biostatistics Genetic Association Studies - statistics & numerical data Genetic association study Genetic research Humans Joint regression model Likelihood Functions Models, Genetic Models, Statistical Peptides, Cyclic - immunology Pleiotropic analysis Polymorphism, Single Nucleotide Qualitative trait Quantitative trait Quantitative Trait Loci Regression Analysis Stratified sample
title	A Joint Regression Analysis for Genetic Association Studies with Outcome Stratified Samples
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-30T22%3A30%3A21IST&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=A%20Joint%20Regression%20Analysis%20for%20Genetic%20Association%20Studies%20with%20Outcome%20Stratified%20Samples&rft.jtitle=Biometrics&rft.au=Wu,%20Colin%20O.&rft.date=2013-06&rft.volume=69&rft.issue=2&rft.spage=417&rft.epage=426&rft.pages=417-426&rft.issn=0006-341X&rft.eissn=1541-0420&rft.coden=BIOMA5&rft_id=info:doi/10.1111/biom.12012&rft_dat=%3Cjstor_proqu%3E44695258%3C/jstor_proqu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c4172-25faf4bdf1210defdb2068b864510bada436ec7a13ec4e6eb4f7f563d33e46b53%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1370810425&rft_id=info:pmid/23489105&rft_jstor_id=44695258&rfr_iscdi=true