Complex traits arise from the interplay between genetic and environmental factors. The actions of these factors usually appear to be additive, and few compelling examples of gene–environment synergy have been documented. Here we show that adiposity significantly amplifies the effect of three sequence variants (encoding PNPLA3 p.I148M, TM6SF2 p.E167K, and GCKR p.P446L) associated with nonalcoholic fatty liver disease (NAFLD). Synergy between adiposity and genotype promoted the full spectrum of NAFLD, from steatosis to hepatic inflammation to cirrhosis. We found no evidence of strong interaction between adiposity and sequence variants influencing other adiposity-associated traits. These results indicate that adiposity augments genetic risk of NAFLD at multiple loci that confer susceptibility to hepatic steatosis through diverse metabolic mechanisms.
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This work was supported by grants from the US National Institutes of Health (NIH) (PO1 HL20948 and RO1 DK090066 to H.H.H. and J.C.C. and UL1TR001105 to H.H.H.) and The Danish Council for Independent Research, Medical Sciences (Sapere Aude 4004-00398 to S.S.). The Copenhagen cohort is supported by the Danish Council for Independent Research, the Research Fund at Rigshospitalet, Copenhagen University Hospital, Chief Physician Johan Boserup and Lise Boserup's Fund, Ingeborg and Leo Dannin's Grant, Henry Hansen and Wife's Grant, and a grant from the Odd Fellow Order (to A.T.-H.).
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Integrated supplementary information
Supplementary Figure 1 The effect of scale transformation on interaction between BMI and variants in PNPLA3, GCKR, and TM6SF2 for hepatic triglyceride content (HTGC) in the Dallas Heart Study.
A series of scale transformations were explored, depicted in rows (top to bottom): (i) original scale (no transformation); (ii) power transformation, HTGC0.3; (iii) inverse normal transformation; and (iv) natural logarithm transformation. The leftmost panels illustrate the distributions of HTGC under various scale transformations. Normal Q-Q plots of residual HTGC (second column from left) visualize the degree of deviation from normality. The residuals were obtained by linear regression of transformed and standardized (mean = 0, variance = 1) HTGC, adjusted for age, gender, ethnicity, and BMI. The variance plots show the distribution of residual HTGC across BMI group (<25, 25-30, 30-35, and >35 kg/m2). Boxes: median and interquartile range. Error bars extend to the most extreme data point that is no more than 1.5 times the interquartile range from the box. The interactions between BMI and the three genetic variants were tested using either linear regression or a heteroscedasticity-robust model (see Online Methods).
Supplementary Figure 2 The effect of scale transformation on interaction between BMI and variants in PNPLA3, GCKR, and TM6SF2 for ALT in the Dallas Heart Study.
A series of scale transformations were explored, depicted in rows (top to bottom): (i) original scale (no transformation); (ii) power transformation, ALT-0.25; (iii) inverse normal transformation; and (iv) natural logarithm transformation. The leftmost panels illustrate the distributions of ALT under various scale transformations. Normal Q-Q plots of residual ALT (second column from left) visualize the degree of deviation from normality. The residuals were obtained by linear regression of transformed and standardized (mean = 0, variance = 1) ALT, adjusted for age, gender, ethnicity, and BMI. The variance plots show the distribution of residual ALT across BMI group (<25, 25-30, 30-35, and >35 kg/m2). Boxes: median and interquartile range. Error bars extend to the most extreme data point that is no more than 1.5 times the interquartile range from the box. The interactions between BMI and the three genetic variants were tested using either linear regression or a heteroscedasticity-robust model (see Online Methods).
Supplementary Figure 3 Interaction between TM6SF2 p.E167K and body mass index on serum levels of ALT.
Circles and error bars depict medians and interquartile ranges of ALT.
Circles and error bars depict medians and interquartile ranges of ALT.
Supplementary Figure 5 Genetically elevated body mass index and hepatic triglyceride content: Mendelian randomization analysis.
(a) Quintiles of the BMI-SNP genotype score, BMI, and HTGC in European American subjects from the Dallas Heart Study. P-values by linear regression, with the genotype score entered as a continuous variable. Circles and error bars depict medians and interquartile ranges. (b) Increase in HTGC for a 1 kg/m2 increase in, respectively, observational and genetically determined BMI in the Dallas Heart Study European Americans. The increase for a 1 kg/m2 increase in observational BMI was calculated by linear regression, adjusted for age, sex, and ethnicity. This estimate is prone to confounding and reverse causation, inherent limitations of observational epidemiology. The genetic estimate, which is less susceptible to confounding and reverse causation, was calculated as follows: for each European American participant in the Dallas Heart Study, a genetic score of 30 SNPs known to associate with BMI in European-ancestry individuals was calculated. The SNP-score explained 0.4% of the total variation in BMI, and the F-score was 12, indicating sufficient instrument strength. Two-stage least square regression was used to estimate the effect of genetically determined BMI on HTGC. A 1 kg/m2 increase in genetically determined BMI was significantly associated with increased HTGC (P = 0.02). This supports that obesity per se causally increases HTGC.
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Stender, S., Kozlitina, J., Nordestgaard, B. et al. Adiposity amplifies the genetic risk of fatty liver disease conferred by multiple loci. Nat Genet 49, 842–847 (2017). https://doi.org/10.1038/ng.3855
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