Statistical Methods for Understanding Trajectories in Genetic Epidemiology

Genetic influences on how human traits change over time remain underexplored and may play an important role in disease processes. In this review, we explore emerging statistical approaches for incorporating longitudinal data on trait trajectories into genetic epidemiology studies, including longitudinal genome-wide association studies, polygenic scores, and Mendelian randomization. We discuss the caution required when analyzing longitudinal data focused on disease progression, where analyses are conducted within a group of patients rather than the general population. Finally, we outline the large longitudinal data resources that are available and discuss future directions in trajectory-based genetic epidemiological studies. Embracing time as a critical dimension of human traits offers deeper insight into disease pathways and intervention opportunities.