Bivariate traits association analysis using generalized estimating equations in family data
Mariza de Andrade (),
Mazo Lopera Mauricio A. and
Duarte Nubia E.
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Mariza de Andrade: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA
Mazo Lopera Mauricio A.: Escuela de Estadística, Universidad Nacional de Colombia, Medellín, Antioquia, 050022, Colombia
Duarte Nubia E.: Departamento de Matemáticas, Universidad Nacional de Colombia, Manizales, Caldas, 170001, Colombia
Statistical Applications in Genetics and Molecular Biology, 2020, vol. 19, issue 2, 13
Genome wide association study (GWAS) is becoming fundamental in the arduous task of deciphering the etiology of complex diseases. The majority of the statistical models used to address the genes-disease association consider a single response variable. However, it is common for certain diseases to have correlated phenotypes such as in cardiovascular diseases. Usually, GWAS typically sample unrelated individuals from a population and the shared familial risk factors are not investigated. In this paper, we propose to apply a bivariate model using family data that associates two phenotypes with a genetic region. Using generalized estimation equations (GEE), we model two phenotypes, either discrete, continuous or a mixture of them, as a function of genetic variables and other important covariates. We incorporate the kinship relationships into the working matrix extended to a bivariate analysis. The estimation method and the joint gene-set effect in both phenotypes are developed in this work. We also evaluate the proposed methodology with a simulation study and an application to real data.
Keywords: bivariate analysis; gene-set test; generalized estimating equations; family data (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:19:y:2020:i:2:p:13:n:3
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