The current obesity 'epidemic': segregation of familial genetic risk in NHANES cohort supports a major role for large genetic effects
Background/Objectives: The continuing increase in many countries in adult body mass index (BMI kg/m2) and its dispersion is contributed to by interactions between genetic susceptibilities and an increasingly obesogenic environment. Whether population susceptibility to obesogenic environments is mainly determined by a subgroup with high genetic susceptibility or susceptibility is more evenly distributed throughout the population is unresolved, due to uncertainties around relevant genetic and
... ant genetic and environmental architecture. We aimed to test the predictions of a Mendelian genetic architecture based on collectively common but individually rare large-effect variants and its ability to account for current trends in a large population-based sample. Subjects/Methods: We studied publicly available adult BMI data (n = 9102) from 3 cycles of NHANES (1999, 2005, 2013) adjusted for age, gender and race/ethnicity. A first degree family history of diabetes (FH) served as a binary marker (FH0/FH1) of genetic obesity susceptibility. We tested for multi-modality in BMI non-parametrically using a runs tests in conditional quantile regression (CQR) models of FH effects, obtained parametric fits to a Mendelian model in FH1, and estimated FH-environment interactions in CQR models and in ANCOVA models incorporating secular time. Results: A unimodal FH effect on BMI was excluded (p≤0.0001) in CQR models and parametric fits to a Mendelian model in FH1 identified 3 modes at 25.8±1.0 (SEM), 32.1±1.8 and 40.6±2.5 kg/m2. Mode separation accounted for ~40% of BMI variance in FH1 providing a lower bound for the contribution of large effects. CQR analysis identified strong interactions between FH and environmental factors (p≤0.01) and FH1 accounted for ~60% of the secular trends in BMI and its SD in ANCOVA models. Conclusions: Multimodality in the FH effect is inconsistent with a predominantly polygenic small effect architecture. We conclude that large genetic effects interacting with obesogenic environment provide a better, quantitative explanation for current trends in BMI.