Bivariate traits association analysis using generalized estimating equations in family data

Mariza De Andrade, Mauricio A. Mazo Lopera, Nubia E. Duarte

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalStatistical Applications in Genetics and Molecular Biology
DOIs
StatePublished - May 5 2020

Keywords

  • bivariate analysis
  • family data
  • gene-set test
  • generalized estimating equations

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Bivariate traits association analysis using generalized estimating equations in family data'. Together they form a unique fingerprint.

  • Cite this