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

In genetic epidemiology studies, many diseases are multifactorial that can be bothenvironmental and genetic inherited pattern. The relationship between genetic variability andindividual phenotypes is usually investigated by genetic association studies. In geneticassociation 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 associationsconsidering different types of phenotype distributions. In this study, it is aimed to accommodateGeneralized Estimating Equations (GEE) method for genetic association studies in the presenceof both familial and serial correlation. For this purpose, a real genotyped data set with thepedigree information and a continuous trait measured over time is used to model the associationbetween the disease and the genotype by analyzing several variants, which have been associatedwith the disease. A joint working correlation structure is adapted, accounting for two differentsources of correlations for estimating equations.

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