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 (GRSBMI) increased significantly with increasing quantiles of BMI. However, BMI is an inexact adiposity measure and GRSBMI 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 (h2, heritability in the narrow sense), extend the result to other adiposity measures, and demonstrate its consistency with purported gene–environment interactions.
Quantile-specific offspring–parent regression slopes (βOP) 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 2βOP/(1 + rspouse) in 6227 offspring–parent pairs from the Framingham Heart Study, where rspouse is the spouse correlation.
Compared to h2 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/ht2 (3.62-fold, P = 0.0002), DXA leg fat/ht2 (3.29-fold, P = 2.0 × 10−11), DXA arm fat/ht2 (4.02-fold, P = 0.001), CT-visceral fat/ht2 (3.03-fold, P = 0.002), and CT-subcutaneous fat/ht2 (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.
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.
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We are grateful to the efforts of the investigators and staff of the Framingham Heart Study who collected the data used in these analyses. This paper was prepared using Framingham Heart Study Research Materials obtained from the National Heart, Lung, and Blood Institute (NHLBI) Biologic Specimens and Data Repository Information Coordinating Center. The Framingham Heart Study is conducted and supported by the NHLBI in collaboration with Boston University (Contract Nos. N01-HC-25195 and HHSN268201500001I). Funding support for the Framingham Whole Body and Regional Dual X-ray Absorptiometry (DXA) dataset was provided by NIH grants R01 AR/AG 41398. This paper was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.
This research was supported by NIH grant R21ES020700 from the National Institute of Environmental Health Sciences, and an unrestricted gift from HOKA ONE ONE.
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Williams, P.T. Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity. Int J Obes 44, 2101–2112 (2020). https://doi.org/10.1038/s41366-020-0636-1