Generalized Estimating Equations for Genetic Association Studies of Multi-Correlated Longitudinal Family Data

Year 2018, Volume: 31 Issue: 1, 273 - 280, 01.03.2018

Özge Karadağ Serpil Aktaş Altunay

Abstract

In genetic epidemiology studies, many
diseases are multifactorial that can be both environmental and genetic
inherited pattern. The relationship between genetic variability and individual
phenotypes is usually investigated by genetic association studies. In genetic
association studies, longitudinal measures are very important scale in
detecting disease variants. They enable to observe both factors in the progress
of disease. Generalized Linear Modelling (GLM) techniques offer a flexible
approach for testing and quantifying genetic associations considering different
types of phenotype distributions. In this study, it is aimed to accommodate
Generalized Estimating Equations (GEE) method for genetic association studies
in the presence of both familial and serial correlation. For this purpose, a
real genotyped data set with the pedigree information and a continuous trait
measured over time is used to model the association between the disease and the
genotype by analyzing several variants, which have been associated with the
disease. A joint working correlation structure is adapted, accounting for two
different sources of correlations for estimating equations. 

Keywords

genetic association, pedigree data, multi-correlated data

References

• Liang KY, Zeger SL (1986) Longitudinal data analysis using generalized linear models. Biometrika 73: 13–22