Economics at your fingertips  

Bivariate traits association analysis using generalized estimating equations in family data

Mariza de Andrade (), Mazo Lopera Mauricio A. and Duarte Nubia E.
Additional contact information
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
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1515/sagmb-2019-0030

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

Page updated 2021-05-24
Handle: RePEc:bpj:sagmbi:v:19:y:2020:i:2:p:13:n:2