Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density
Identifieur interne : 001848 ( Istex/Corpus ); précédent : 001847; suivant : 001849Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density
Auteurs : J. Stone ; L. C. Gurrin ; V. M. Hayes ; M. C. Southey ; J. L. Hopper ; G. B. ByrnesSource :
- Genetic Epidemiology [ 0741-0395 ] ; 2010-05.
English descriptors
- KwdEn :
- Active phenotype, Algorithm, Association studies, Carlin, Coefficient, Continuous trait, Covariates, Dense area, Different sizes, Effect size, Effect sizes, Epidemiol, Error rate, Estimate percent, First birth, General model, Genet, Genotype, Genotype data, Gurrin, Haplotype, Haplotype dosage, Intra, Linear regression model, Mammogram, Mammographic, Mammographic density, Mammographic measures, Menopausal status, Nondense, Nondense area, Nondense areab, Nonparous women, Null hypothesis, Orthogonal, Orthogonal transformation, Orthogonal transformation extension, Orthogonal transformation extensions, Orthogonal transformations, Other covariates, Pack smoking, Pack years, Permutation, Phenotype, Population distribution, Ppermutation, Random intercept, Regression, Regression analysis, Regression coefficient, Regression coefficients, Regression estimates, Regression model, Regression models, Sibling, Sibling haplotypes, Sibship, Sibships, Simulation, Simulation study, Snp, Software, Standard software, Unbiased estimates, Uncorrected, Unrelated individuals, Variant alleles.
- Teeft :
- Active phenotype, Algorithm, Association studies, Carlin, Coefficient, Continuous trait, Covariates, Dense area, Different sizes, Effect size, Effect sizes, Epidemiol, Error rate, Estimate percent, First birth, General model, Genet, Genotype, Genotype data, Gurrin, Haplotype, Haplotype dosage, Intra, Linear regression model, Mammogram, Mammographic, Mammographic density, Mammographic measures, Menopausal status, Nondense, Nondense area, Nondense areab, Nonparous women, Null hypothesis, Orthogonal, Orthogonal transformation, Orthogonal transformation extension, Orthogonal transformation extensions, Orthogonal transformations, Other covariates, Pack smoking, Pack years, Permutation, Phenotype, Population distribution, Ppermutation, Random intercept, Regression, Regression analysis, Regression coefficient, Regression coefficients, Regression estimates, Regression model, Regression models, Sibling, Sibling haplotypes, Sibship, Sibships, Simulation, Simulation study, Snp, Software, Standard software, Unbiased estimates, Uncorrected, Unrelated individuals, Variant alleles.
Abstract
Haplotype‐based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within‐sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between‐ and within‐sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross‐sectional analysis or using subsets of sib‐pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within‐sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the VDR gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4‐SNP VDR haplotype. In conclusion, our proposed method retains the benefits of the between‐ and within‐pair analysis for pairs of siblings and can be implemented in standard software. Genet. Epidemiol. 34: 309–318, 2010. © 2009 Wiley‐Liss, Inc.
Url:
DOI: 10.1002/gepi.20462
Links to Exploration step
ISTEX:818BF96E999B268CC244202129409325489377EELe document en format XML
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individuals</term><term>Variant alleles</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Haplotype‐based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within‐sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between‐ and within‐sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross‐sectional analysis or using subsets of sib‐pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within‐sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the VDR gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4‐SNP VDR haplotype. In conclusion, our proposed method retains the benefits of the between‐ and within‐pair analysis for pairs of siblings and can be implemented in standard software. Genet. 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Stone</name><affiliations><json:string>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia</json:string></affiliations></json:item><json:item><name>L. C. Gurrin</name><affiliations><json:string>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia</json:string></affiliations></json:item><json:item><name>V. M. Hayes</name><affiliations><json:string>Cancer Genetics Group, Children's Cancer Institute Australia for Medical Research, Sydney Children's Hospital, Randwick, NSW, Australia</json:string></affiliations></json:item><json:item><name>M. C. Southey</name><affiliations><json:string>Department of Pathology, University of Melbourne, Melbourne, Australia</json:string></affiliations></json:item><json:item><name>J. L. Hopper</name><affiliations><json:string>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia</json:string></affiliations></json:item><json:item><name>G. B. Byrnes</name><affiliations><json:string>Biostatistics Group, International Agency for Research on Cancer, Lyon, France</json:string><json:string>E-mail: byrnesg@iarc.fr</json:string><json:string>Correspondence address: Epidemiology Methods and Support Group, International Agency for Research on Cancer, 150 cours Albert Thomas 69372 Lyon, cedex 08, France===</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>family studies</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>regression modelling</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>simulation study</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>VDR gene</value></json:item></subject><articleId><json:string>GEPI20462</json:string></articleId><arkIstex>ark:/67375/WNG-FJQVXVJ6-P</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Haplotype‐based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within‐sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between‐ and within‐sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross‐sectional analysis or using subsets of sib‐pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within‐sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the VDR gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4‐SNP VDR haplotype. In conclusion, our proposed method retains the benefits of the between‐ and within‐pair analysis for pairs of siblings and can be implemented in standard software. Genet. 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C.</forename><surname>Gurrin</surname></persName><affiliation>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia<address><country key="AU"></country></address></affiliation></author><author xml:id="author-0002"><persName><forename type="first">V. M.</forename><surname>Hayes</surname></persName><affiliation>Cancer Genetics Group, Children's Cancer Institute Australia for Medical Research, Sydney Children's Hospital, Randwick, NSW, Australia<address><country key="AU"></country></address></affiliation></author><author xml:id="author-0003"><persName><forename type="first">M. C.</forename><surname>Southey</surname></persName><affiliation>Department of Pathology, University of Melbourne, Melbourne, Australia<address><country key="AU"></country></address></affiliation></author><author xml:id="author-0004"><persName><forename type="first">J. L.</forename><surname>Hopper</surname></persName><affiliation>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia<address><country key="AU"></country></address></affiliation></author><author xml:id="author-0005" role="corresp"><persName><forename type="first">G. B.</forename><surname>Byrnes</surname></persName><email>byrnesg@iarc.fr</email><affiliation>Biostatistics Group, International Agency for Research on Cancer, Lyon, France<address><country key="FR"></country></address></affiliation><affiliation>Epidemiology Methods and Support Group, International Agency for Research on Cancer, 150 cours Albert Thomas 69372 Lyon, cedex 08, France===</affiliation></author><idno type="istex">818BF96E999B268CC244202129409325489377EE</idno><idno type="ark">ark:/67375/WNG-FJQVXVJ6-P</idno><idno type="DOI">10.1002/gepi.20462</idno><idno type="unit">GEPI20462</idno><idno type="toTypesetVersion">file:GEPI.GEPI20462.pdf</idno></analytic><monogr><title level="j" type="main">Genetic Epidemiology</title><title level="j" type="alt">GENETIC EPIDEMIOLOGY</title><idno type="pISSN">0741-0395</idno><idno type="eISSN">1098-2272</idno><idno type="book-DOI">10.1002/(ISSN)1098-2272</idno><idno type="book-part-DOI">10.1002/gepi.v34:4</idno><idno type="product">GEPI</idno><imprint><biblScope unit="vol">34</biblScope><biblScope unit="issue">4</biblScope><biblScope unit="page" from="309">309</biblScope><biblScope unit="page" to="318">318</biblScope><biblScope unit="page-count">10</biblScope><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2010-05"></date></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en" style="main"><head>Abstract</head><p>Haplotype‐based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within‐sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between‐ and within‐sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross‐sectional analysis or using subsets of sib‐pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within‐sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the <hi rend="italic">VDR</hi> gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4‐SNP <hi rend="italic">VDR</hi> haplotype. In conclusion, our proposed method retains the benefits of the between‐ and within‐pair analysis for pairs of siblings and can be implemented in standard software. <hi rend="italic">Genet. Epidemiol</hi>. 34: 309–318, 2010. © 2009 Wiley‐Liss, Inc.</p></abstract><textClass><keywords xml:lang="en"><term xml:id="kwd1">family studies</term><term xml:id="kwd2">regression modelling</term><term xml:id="kwd3">simulation study</term><term xml:id="kwd4"><hi rend="italic">VDR</hi> gene</term></keywords><keywords rend="articleCategory"><term>Original Article</term></keywords><keywords rend="tocHeading1"><term>Original Articles</term></keywords></textClass><langUsage><language ident="en"></language></langUsage></profileDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/818BF96E999B268CC244202129409325489377EE/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Wiley Subscription Services, Inc., A Wiley Company</publisherName><publisherLoc>Hoboken</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1098-2272</doi><issn type="print">0741-0395</issn><issn type="electronic">1098-2272</issn><idGroup><id type="product" value="GEPI"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="GENETIC EPIDEMIOLOGY">Genetic Epidemiology</title><title type="short">Genet. 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Second, we propose a simple permutation approach to avoid errors of inference due to the within‐sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the <i>VDR</i> gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4‐SNP <i>VDR</i> haplotype. In conclusion, our proposed method retains the benefits of the between‐ and within‐pair analysis for pairs of siblings and can be implemented in standard software. <i>Genet. Epidemiol</i>. 34: 309–318, 2010. © 2009 Wiley‐Liss, Inc.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Sibling Haplotypes and a Continuous Trait</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density</title></titleInfo><name type="personal"><namePart type="given">J.</namePart><namePart type="family">Stone</namePart><affiliation>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">L. C.</namePart><namePart type="family">Gurrin</namePart><affiliation>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">V. M.</namePart><namePart type="family">Hayes</namePart><affiliation>Cancer Genetics Group, Children's Cancer Institute Australia for Medical Research, Sydney Children's Hospital, Randwick, NSW, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">M. C.</namePart><namePart type="family">Southey</namePart><affiliation>Department of Pathology, University of Melbourne, Melbourne, Australia</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">J. 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B.</namePart><namePart type="family">Byrnes</namePart><affiliation>Biostatistics Group, International Agency for Research on Cancer, Lyon, France</affiliation><affiliation>E-mail: byrnesg@iarc.fr</affiliation><affiliation>Correspondence address: Epidemiology Methods and Support Group, International Agency for Research on Cancer, 150 cours Albert Thomas 69372 Lyon, cedex 08, France===</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2010-05</dateIssued><dateCaptured encoding="w3cdtf">2009-04-20</dateCaptured><dateValid encoding="w3cdtf">2009-09-12</dateValid><copyrightDate encoding="w3cdtf">2010</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><extent unit="figures">1</extent><extent unit="tables">5</extent><extent unit="references">22</extent></physicalDescription><abstract lang="en">Haplotype‐based association studies have been proposed as a powerful comprehensive approach to identify causal genetic variation underlying complex diseases. Data comparisons within families offer the additional advantage of dealing naturally with complex sources of noise, confounding and population stratification. Two problems encountered when investigating associations between haplotypes and a continuous trait using data from sibships are (i) the need to define within‐sibship comparisons for sibships of size greater than two and (ii) the difficulty of resolving the joint distribution of haplotype pairs within sibships in the absence of parental genotypes. We therefore propose first a method of orthogonal transformation of both outcomes and exposures that allow the decomposition of between‐ and within‐sibship regression effects when sibship size is greater than two. We conducted a simulation study, which confirmed analysis using all members of a sibship is statistically more powerful than methods based on cross‐sectional analysis or using subsets of sib‐pairs. Second, we propose a simple permutation approach to avoid errors of inference due to the within‐sibship correlation of any errors in haplotype assignment. These methods were applied to investigate the association between mammographic density (MD), a continuously distributed and heritable risk factor for breast cancer, and single nucleotide polymorphisms (SNPs) and haplotypes from the VDR gene using data from a study of 430 twins and sisters. We found evidence of association between MD and a 4‐SNP VDR haplotype. In conclusion, our proposed method retains the benefits of the between‐ and within‐pair analysis for pairs of siblings and can be implemented in standard software. Genet. 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