Genome-Wide Association Mapping With Longitudinal Data

Many genome-wide association studies have been performed on population cohorts that contain phenotype measurements at multiple time points. However, standard association methodologies only consider one time point. In this paper, we propose a mixed-model-based approach for performing association mapping which utilizes multiple phenotype measurements for each individual. We introduce an analytical approach to calculate statistical power and show that this model leads to increased power when compared to traditional approaches. Moreover, we show that by using this model we are able to differentiate the genetic, environmental, and residual error contributions to the phenotype. Using predictions of these components, we show how the proportion of the phenotype due to environment and genetics can be predicted and show that the ranking of individuals based on these predictions is very accurate. The software implementing this method may be found at http://genetics.cs.ucla.edu/longGWAS/.