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

Abstract

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 (h², 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 2β_OP/(1 + r_spouse) in 6227 offspring-parent pairs from the Framingham Heart Study, where r_spouse is the spouse correlation.

Results: 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.

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.

Figure 1.

A) Regression lines showing the increase in offspring’s BMI vs. the increase in their parent’s BMI (kg/m²) at the 10^th, 25^th, 50^th, 75^th, and 90^th percentiles of the offspring’s distribution (i.e. offspring-parent slopes, β_OP). B) Offspring-parent slopes (β_OP, left vertical axis) plotted as a function of the percentiles of the offspring’s BMI distribution (horizontal axis). The right axis displays the corresponding heritability estimates (h²=2β_OP/(1+r_spouse)). Shaded region designates the 95% confidence interval for the quantile-specific heritabilities and slopes. Parents and offspring BMI adjusted for sex, age, age², sex × age, and sex × age². Environmental factors that distinguish high vs. low offspring BMI written in italics.

Figure 2.

Age and sex-adjusted quantile-specific offspring-parent regression slope (solid curve) ± 95% confidence interval (gray area) by quantile of the offspring distribution for: A) height; B) DXA- total fat/height², C) CT-visceral fat/height², D) CT-subcutaneous fat/height². Sample sizes provided in Supplementary Table 1.

Figure 3.

Relationship between the effect of 131 lifestyle factors on BMI (β_E) vs. the gene × environment interaction between GRS_BMI and these lifestyle factors (β_GxE) in the UK Biobank resource reported by Rask-Andersen et al. [19]. Nineteen lifestyle factors showed significant interaction with GRS_BMI when Bonferroni corrected: 1 alcohol, 2 Townsend deprivation index, 3 television, 4 tiredness, 5 depression, 6 smoker, 7 medications, 8 nap frequency, 9 feeling fed-up, 10 number vehicles, 11 household size, 12 income, 13 stairs climbed, 14 vigorous activity, 15 red wine, 16 days walked, 17 moderate activity, 18 children born, and 19 walking pace.

Figure 4.

A) Terán-García et al.’s results [57] from a precision medicine perspective of different mean BMI increases by CETP rs289714 genotypes following overfeeding (histogram insert) vs. quantile-dependent expressivity interpretation (larger post-feeding genetic effect size when average BMI was high vs. lower, requiring nonparallel BMI increases by genotype (P_interaction=0.04); B) Kuzman et al.’s report of a significantly greater increase in waist circumference for TT homozygotes of the −759CT 5-HT2C polymorphism than carriers of the C allele (9.4 vs. 4.0 cm, P=0.03) following a 3-month olanzapine or risperidone regimen [58].