Two-level mixed modeling of longitudinal pedigree data for genetic association analysis: 5th International Conference on Bioinformatics and Computational Biology 2013, BICoB 2013

Genetic association analysis on complex phenotypes under a longitudinal design involving pedigrees encounters the problem of correlation within pedigrees which could affect statistical assessment of the genetic effects on both the mean level of the phenotype and its rate of change over the time of follow-up. Approaches have been proposed to integrate kinship correlation into the mixed effect models to explicitly model the genetic relationship which have been proven as an efficient way for dealing with sample clustering in pedigree data. Although useful for adjusting relatedness in the mixed modeling of genetic effects on the mean level of a phenotype, current algorithms implemented in popular statistical packages are not so straightforward to handle the kinship correlation on the time-dependent trajectories of a phenotype. We introduce a two-level hierarchical linear model to separately assess the genetic associations with the mean level and the rate of change in a phenotype both with kinship correlation integrated in the mixed effect models. We apply our method to longitudinal pedigree data to estimate the genetic effects on systolic blood pressure measured over time in large pedigrees. Our results show that the method efficiently handles relatedness in detecting genetic variations that affect the mean level or the rate of change for a phenotype of interest.