|
Abstract:
|
Many phenotypes in health studies are measured at multiple time points. The rich information on repeated measurements on each subject not only provides a more accurate assessment of disease condition, but also allows us to explore the genetic influence on disease onset and progression. We propose LSKAT (Longitudinal Sequence Kernel Association Test), a region-based variants association test for longitudinal data. LSKAT uses several variance components to account for the within-subject correlation structure of the longitudinal data, and the contributions from all genetic variants (common and rare) in a region. Additionally, we propose another test LMSKAT (Longitudinal Multi-Kernel Association Test) which allows for the time-varing genetic effects by using multiple kernels to detect genes affecting the temporal trends of the trait. In simulation studies, we evaluate the performance of LSKAT and LMSKAT, and demonstrate that they have improved power, by making full use of multiple measurements, as comparing to previously proposed tests on a single measurement or average measurements for each subject. We apply LSKAT and LMSKAT to testing with body mass index in a longitudinal cohort.
|
