Genetic sensitivity analysis: adjusting for genetic confounding in epidemiological associations
Dr. Jean-Baptiste Pingault. University College London
Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes – child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely.
Dr. Pingault aims to delineate causal pathways from early risk factors to mental health difficulties in childhood and adolescence. To this end, he studies the influences of genetic and environmental risk factors on the development of a variety of mental health difficulties. His team adopts an interdisciplinary approach building on several disciplines including developmental psychopathology, epidemiological psychiatry and biology. In particular, they implement innovative methods for causal inference in big datasets, building on statistical innovation and genetically informed designs. In collaboration with national and international colleagues, Dr. Pingault and his team then seek to further characterize these causal pathways by investigating possible underlying biological mechanisms (e.g. cognitive profiles, epigenetics).