Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density
Identifieur interne : 007178 ( Main/Exploration ); précédent : 007177; suivant : 007179Sibship analysis of associations between SNP haplotypes and a continuous trait with application to mammographic density
Auteurs : J. Stone [Australie] ; L. C. Gurrin [Australie] ; V. M. Hayes [Australie] ; M. C. Southey [Australie] ; J. L. Hopper [Australie] ; G. B. Byrnes [France]Source :
- Genetic Epidemiology [ 0741-0395 ] ; 2010-05.
Descripteurs français
- Wicri :
- topic : Simulation, Logiciel.
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
Affiliations:
- Australie, France
- Auvergne-Rhône-Alpes, Rhône-Alpes, Victoria (État)
- Lyon, Melbourne
- Université de Melbourne
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Hopper</name><affiliation wicri:level="4"><country xml:lang="fr">Australie</country><wicri:regionArea>Centre for Molecular, Environmental, Genetic, and Analytic (MEGA) Epidemiology, University of Melbourne, Melbourne</wicri:regionArea><placeName><settlement type="city">Melbourne</settlement><region type="état">Victoria (État)</region><settlement type="city">Melbourne</settlement></placeName><orgName type="university">Université de Melbourne</orgName></affiliation></author><author><name sortKey="Byrnes, G B" sort="Byrnes, G B" uniqKey="Byrnes G" first="G. B." last="Byrnes">G. B. Byrnes</name><affiliation wicri:level="3"><country xml:lang="fr">France</country><wicri:regionArea>Biostatistics Group, International Agency for Research on Cancer, Lyon</wicri:regionArea><placeName><region type="region">Auvergne-Rhône-Alpes</region><region type="old region">Rhône-Alpes</region><settlement type="city">Lyon</settlement></placeName></affiliation><affiliation wicri:level="1"><country wicri:rule="url">France</country></affiliation><affiliation wicri:level="1"><country xml:lang="fr" wicri:curation="lc">France</country><wicri:regionArea>Correspondence address: Epidemiology Methods and Support Group, International Agency for Research on Cancer, 150 cours Albert Thomas 69372 Lyon, cedex 08</wicri:regionArea><wicri:noRegion>cedex 08</wicri:noRegion><wicri:noRegion>cedex 08</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Genetic Epidemiology</title><title level="j" type="alt">GENETIC EPIDEMIOLOGY</title><idno type="ISSN">0741-0395</idno><idno type="eISSN">1098-2272</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">2010-05</date></imprint><idno type="ISSN">0741-0395</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0741-0395</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Active phenotype</term><term>Algorithm</term><term>Association studies</term><term>Carlin</term><term>Coefficient</term><term>Continuous trait</term><term>Covariates</term><term>Dense area</term><term>Different sizes</term><term>Effect size</term><term>Effect sizes</term><term>Epidemiol</term><term>Error rate</term><term>Estimate percent</term><term>First birth</term><term>General model</term><term>Genet</term><term>Genotype</term><term>Genotype data</term><term>Gurrin</term><term>Haplotype</term><term>Haplotype dosage</term><term>Intra</term><term>Linear regression model</term><term>Mammogram</term><term>Mammographic</term><term>Mammographic density</term><term>Mammographic measures</term><term>Menopausal status</term><term>Nondense</term><term>Nondense area</term><term>Nondense areab</term><term>Nonparous women</term><term>Null hypothesis</term><term>Orthogonal</term><term>Orthogonal transformation</term><term>Orthogonal transformation extension</term><term>Orthogonal transformation extensions</term><term>Orthogonal transformations</term><term>Other covariates</term><term>Pack smoking</term><term>Pack years</term><term>Permutation</term><term>Phenotype</term><term>Population distribution</term><term>Ppermutation</term><term>Random intercept</term><term>Regression</term><term>Regression analysis</term><term>Regression coefficient</term><term>Regression coefficients</term><term>Regression estimates</term><term>Regression model</term><term>Regression models</term><term>Sibling</term><term>Sibling haplotypes</term><term>Sibship</term><term>Sibships</term><term>Simulation</term><term>Simulation study</term><term>Snp</term><term>Software</term><term>Standard software</term><term>Unbiased estimates</term><term>Uncorrected</term><term>Unrelated individuals</term><term>Variant alleles</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Active phenotype</term><term>Algorithm</term><term>Association studies</term><term>Carlin</term><term>Coefficient</term><term>Continuous trait</term><term>Covariates</term><term>Dense area</term><term>Different sizes</term><term>Effect size</term><term>Effect sizes</term><term>Epidemiol</term><term>Error rate</term><term>Estimate percent</term><term>First birth</term><term>General model</term><term>Genet</term><term>Genotype</term><term>Genotype data</term><term>Gurrin</term><term>Haplotype</term><term>Haplotype dosage</term><term>Intra</term><term>Linear regression model</term><term>Mammogram</term><term>Mammographic</term><term>Mammographic density</term><term>Mammographic measures</term><term>Menopausal status</term><term>Nondense</term><term>Nondense area</term><term>Nondense areab</term><term>Nonparous women</term><term>Null hypothesis</term><term>Orthogonal</term><term>Orthogonal transformation</term><term>Orthogonal transformation extension</term><term>Orthogonal transformation extensions</term><term>Orthogonal transformations</term><term>Other covariates</term><term>Pack smoking</term><term>Pack years</term><term>Permutation</term><term>Phenotype</term><term>Population distribution</term><term>Ppermutation</term><term>Random intercept</term><term>Regression</term><term>Regression analysis</term><term>Regression coefficient</term><term>Regression coefficients</term><term>Regression estimates</term><term>Regression model</term><term>Regression models</term><term>Sibling</term><term>Sibling haplotypes</term><term>Sibship</term><term>Sibships</term><term>Simulation</term><term>Simulation study</term><term>Snp</term><term>Software</term><term>Standard software</term><term>Unbiased estimates</term><term>Uncorrected</term><term>Unrelated individuals</term><term>Variant alleles</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Simulation</term><term>Logiciel</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. Epidemiol. 34: 309–318, 2010. © 2009 Wiley‐Liss, Inc.</div></front></TEI><affiliations><list><country><li>Australie</li><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Rhône-Alpes</li><li>Victoria (État)</li></region><settlement><li>Lyon</li><li>Melbourne</li></settlement><orgName><li>Université de Melbourne</li></orgName></list><tree><country name="Australie"><region name="Victoria (État)"><name sortKey="Stone, J" sort="Stone, J" uniqKey="Stone J" first="J." last="Stone">J. Stone</name></region><name sortKey="Gurrin, L C" sort="Gurrin, L C" uniqKey="Gurrin L" first="L. C." last="Gurrin">L. C. Gurrin</name><name sortKey="Hayes, V M" sort="Hayes, V M" uniqKey="Hayes V" first="V. M." last="Hayes">V. M. Hayes</name><name sortKey="Hopper, J L" sort="Hopper, J L" uniqKey="Hopper J" first="J. L." last="Hopper">J. L. Hopper</name><name sortKey="Southey, M C" sort="Southey, M C" uniqKey="Southey M" first="M. C." last="Southey">M. C. Southey</name></country><country name="France"><region name="Auvergne-Rhône-Alpes"><name sortKey="Byrnes, G B" sort="Byrnes, G B" uniqKey="Byrnes G" first="G. B." last="Byrnes">G. B. Byrnes</name></region><name sortKey="Byrnes, G B" sort="Byrnes, G B" uniqKey="Byrnes G" first="G. B." last="Byrnes">G. B. Byrnes</name><name sortKey="Byrnes, G B" sort="Byrnes, G B" uniqKey="Byrnes G" first="G. B." last="Byrnes">G. B. Byrnes</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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