Segregation of Familial Risk of Obesity in NHANES Cohort Supports a Major Role for Large Genetic Effects in the Current Obesity Epidemic

Abstract

AbstractThe continuing increase in many countries in adult body mass index (BMI kg/m2) and its dispersion is contributed to by interaction between genetic susceptibilities and an increasingly obesogenic environment (OE). The determinants of OE-susceptibility are unresolved, due to uncertainty around relevant genetic and environmental architecture. We aimed to test the multi-modal distributional predictions of a Mendelian genetic architecture based on collectively common, but individually rare, large-effect variants and their ability to account for current trends in a large population-based sample. We studied publicly available adult BMI data (n = 9102) from 3 cycles of NHANES (1999, 2005, 2013). A first degree family history of diabetes served as a binary marker (FH0/FH1) of genetic obesity susceptibility. We tested for multi-modal BMI distributions non-parametrically using kernel-smoothing and conditional quantile regression (CQR), obtained parametric fits to a Mendelian model in FH1, and estimated FH x OE interactions in CQR models and ANCOVA models incorporating secular time. Non-parametric distributional analyses were consistent with multi-modality and fits to a Mendelian model in FH1 reliably identified 3 modes. Mode separation accounted for ~40% of BMI variance in FH1 providing a lower bound for the contribution of large effects. CQR identified strong FH x OE interactions and FH1 accounted for ~60% of the secular trends in BMI and its SD in ANCOVA models. Multimodality in the FH effect is inconsistent with a predominantly polygenic, small effect architecture and we conclude that large genetic effects interacting with OE provide a better quantitative explanation for current trends in BMI.