Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits

Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.

Article highlights: We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Figure 1

Exploration of the rs79762542 interaction and replication. A: Genotype-stratified dietary main effect estimates. B: Stratified plots (one for each cohort with HbA_1c available) display residualized HbA_1c within strata defined by both genotype at rs79762542 (none vs. any minor alleles) and tertile of carbohydrate/fat ratio. This ratio was defined in the pooled data set on a caloric basis and is used to provide a visual representation of the modeled macronutrient exchange. C: Similar stratified plots for the UKB replication cohort. For B and C, the y-axis displays residuals after regressing the relevant trait (HbA_1c or FG) on the set of covariates used in the replication analysis. Error bars indicate 95% CIs for the effect estimates (A) or mean residual values after stratification (B and C).

Figure 2

Power calculations for gene–environment interaction incorporating exposure measurement error. For all plots above, HbA_1c is used as a basis for parameter choices. A: Genetic and dietary effect sizes on HbA_1c for reference for potential interaction effects. Bars are annotated with the source study, either Churuangsuk et al. (28) or Wheeler et al. (27). B: Simulation-based empirical power estimates are shown as a function of the interaction effect (x-axis), MAF (panels left to right), and diet measurement reliability (colors). C: Bar plots show the estimated sample size needed to achieve 80% statistical power. Panels and colors are as in B. D: As in A, but modeling empirical power for simulated aggregate tests of 20 rare variants with a causal fraction of 0.1 or 0.5 (indicated in panel labels). Additional assumptions for these simulations (full details in Research Design and Methods): N = 35,000; phenotype mean of 5.5; phenotype SD of 0.5; exposure mean of 0; exposure SD of 1; genetic main effect of 0.01; and environmental main effect of 0.2.