38 Novel approach for estimating high-dimensional genetic influence on a longitudinal phenotype

Oregon Convention Center 

Although researchers have developed approaches for estimating high-dimensional genetic influence on cross-sectional data, there has been little work in generalizing these approaches in a mixed model for longitudinal settings. We develop a linear mixed model incorporating two separate genetic effects on the baseline and rate of change for a longitudinal response. Methodological challenges arise from the need to deal with the high-dimensional computation and to account for the crossed nature of the genetic and subject-specific random effects, which induce dependence between longitudinal measurements across all subjects. We propose a modified average information restricted maximum likelihood (AI-ReML) method to obtain the estimation for the variances of these two separate genetic effects. We illustrate our methodology through examining two separate genetic effects integrating approximately 7 million genetic variants on the trajectory of prostate-specific antigen (PSA) level in healthy males from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. Our analysis uncovers a substantial genetic influence on the rate of change in PSA level over time.

longitudinal data

high dimension

genetic effects

ReML estimation

AI-ReML algorithm

PSA level 

Section on Statistics in Epidemiology