Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity

• Published in: International Journal of Obesity Vol. 44; no. 10; pp. 2101 - 2112
• Main Author: Williams, Paul T.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.10.2020; Nature Publishing Group
• Subjects: 692/308/174; 692/499; Absorptiometry, Photon; Adipose tissue; Adiposity - genetics; Analysis; Anthropometry; Antipsychotic drugs; Antipsychotics; Arm; Bioelectricity; Body fat; Body Height; Body mass index; Body measurements; Computed tomography; Consistency; Correlation analysis; CT imaging; Diameters; DNA methylation; Dual energy X-ray absorptiometry; Electric Impedance; Environmental effects; Epidemiology; Epigenetics; Estimates; Gene expression; Gene-Environment Interaction; Health Promotion and Disease Prevention; Heritability; Hip; Humans; Internal Medicine; Intra-Abdominal Fat; Laboratories; Medicine; Medicine & Public Health; Metabolic Diseases; Obesity; Obesity - genetics; Offspring; Physical activity; Polycystic ovary syndrome; Population; Precision medicine; Public Health; Quantiles; Regression analysis; Sleep; Socioeconomics; Subcutaneous Fat; Tomography, X-Ray Computed; Transcription; United States; X-rays; United States
• ISSN: 0307-0565; 1476-5497; 1476-5497
• DOI: 10.1038/s41366-020-0636-1

Background/objectives Quantile-dependent expressivity occurs when a gene’s phenotypic expression depends upon whether the trait (e.g., BMI) is high or low relative to its distribution. We have previously shown that the obesity effects of a genetic risk score (GRS BMI ) increased significantly with increasing quantiles of BMI. However, BMI is an inexact adiposity measure and GRS BMI explains <3% of the BMI variance. The purpose of this paper is to test BMI for quantile-dependent expressivity using a more inclusive genetic measure ( h 2 , heritability in the narrow sense), extend the result to other adiposity measures, and demonstrate its consistency with purported gene–environment interactions. Subjects/methods Quantile-specific offspring–parent regression slopes ( β OP ) were obtained from quantile regression for height (ht) and computed tomography (CT), dual-energy x-ray absorptiometry (DXA), anthropometric, and bioelectrical impedance (BIA) adiposity measures. Heritability was estimated by 2 β OP /(1 +  r spouse ) in 6227 offspring–parent pairs from the Framingham Heart Study, where r spouse is the spouse correlation. Results Compared to h 2 at the 10th percentile, genetic heritability was significantly greater at the 90th population percentile for BMI (3.14-fold greater, P  < 10 −15 ), waist girth/ht (3.27-fold, P  < 10 −15 ), hip girth/ht (3.12-fold, P  = 6.3 × 10 −14 ), waist-to-hip ratio (1.75-fold, P  = 0.01), sagittal diameter/ht (3.89-fold, P  = 3.7 × 10 −7 ), DXA total fat/ht 2 (3.62-fold, P  = 0.0002), DXA leg fat/ht 2 (3.29-fold, P  = 2.0 × 10 −11 ), DXA arm fat/ht 2 (4.02-fold, P  = 0.001), CT-visceral fat/ht 2 (3.03-fold, P  = 0.002), and CT-subcutaneous fat/ht 2 (3.54-fold, P  = 0.0004). External validity was suggested by the phenomenon’s consistency with numerous published reports. Quantile-dependent expressivity potentially explains precision medicine markers for weight gain from overfeeding or antipsychotic medications, and the modifying effects of physical activity, sleep, diet, polycystic ovary syndrome, socioeconomic status, and depression on gene–BMI relationships. Conclusions Genetic heritabilities of anthropometric, CT, and DXA adiposity measures increase with increasing adiposity. Some gene–environment interactions may arise from analyzing subjects by characteristics that distinguish high vs. low adiposity rather than the effects of environmental stimuli on transcriptional and epigenetic processes.