Computing a Synthetic Chronic Psychosocial Stress Measurement in Multiple Datasets and its Application in the...

Chronic psychosocial stress adversely affects health and is associated with the development of disease [Williams, 2008]. Systematic epidemiological and genetic studies are needed to uncover genetic variants that interact with stress to modify metabolic responses across the life cycle that are the proximal contributors to the development of cardiovascular disease and precipitation of acute clinical events. Among the central challenges in the field are to perform and replicate gene-by-environment (G × E) studies. The challenge of measurement of individual experience of psychosocial stress is magnified in this context. Although many research datasets exist that contain genotyping and disease-related data, measures of psychosocial stress are often either absent or vary substantially across studies. In this paper, we provide an algorithm to create a synthetic measure of chronic psychosocial stress across multiple datasets, applying a consistent criterion that uses proxy indicators of stress components. We validated the computed scores of chronic psychosocial stress by observing moderately strong and significant correlations with the self-rated chronic psychosocial stress in the Multi-Ethnic Study of Atherosclerosis Cohort (Rho = 0.23, P < 0.0001) and with the measures of depressive symptoms in five datasets (Rho = 0.15-0.42, Ps = 0.005 to <0.0001) and by comparing the distributions of the self-rated and computed measures. Finally, we demonstrate the utility of this computed chronic psychosocial stress variable by providing three additional replications of our previous finding of gene-by-stress interaction with central obesity traits [Singh et al., 2015].