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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

Abstract: 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)
Date: 2020
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DOI: 10.1515/sagmb-2019-0030

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