“Genetic Sensitivity Analysis: Adjusting for Genetic Confounding in Epidemiological Associations”, 2021-05-07 (; backlinks; similar):
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 (BMI), and Attention Deficit Hyperactivity Disorder (ADHD). Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP-based 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.
Author Summary: An objective shared across the life, behavioral, and social sciences is to identify factors that increase risk for a particular disease or trait. However, identifying true risk factors is challenging. Often, a risk factor is statistically associated with a disease even if it is not really relevant, meaning that even successfully improving the risk factor will not impact the disease. One reason for the existence of such misleading associations stems from genetic confounding. This is when genetic factors influence directly both the risk factor and the disease, which generates a statistical association even in the absence of a true effect of the risk factor. Here, we propose a method to estimate genetic confounding and quantify its effect on observed associations. We show that a large part of the associations between maternal education and 3 child outcomes—educational achievement, body mass index and Attention-Deficit Hyperactivity Disorder—is explained by genetic confounding. Our findings can be applied to better understand the role of genetics in explaining associations of key risk factors with diseases and traits.
…However, because polygenic scores capture only a small part of heritability, controlling for polygenic scores cannot entirely capture genetic confounding. We therefore propose a sensitivity analysis using polygenic scores to gauge how likely it is that genetic confounding accounts, in part or entirely, for a given exposure-outcome association. Here, we develop this proposition in two stages. First, we test to what extent associations of interest are accounted for by observed polygenic scores. Second, in the sensitivity analysis per se, we use structural equation models to examine how an increase in the predictive accuracy of polygenic scores based on heritability estimates would affect association estimates. This can be thought of as adjusting for latent polygenic scores that capture as much of the variance in the exposure and outcome as suggested by available heritability estimates. We note that recent studies have adjusted for polygenic scores to account, to some extent, for genetic confounding (eg.)5 0. However, this approach is fraught with two issues: as mentioned above, polygenic scores only capture a small fraction of trait heritability and, thus, of the confounding arising from genetic factors; adjustment can sometimes amplify rather than reduce bias.6 Gsens addresses these issues.