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Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank.

ABSTRACT: Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

SUBMITTER: Hillary RF

PROVIDER: S-EPMC11347662 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank.

Hillary Robert F RF Gadd Danni A DA Kuncheva Zhana Z Mangelis Tasos T Lin Tinchi T Ferber Kyle K McLaughlin Helen H Runz Heiko H Marioni Riccardo E RE Foley Christopher N CN Sun Benjamin B BB

Nature communications 20240826 1

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include ...[more]

PMID: 39187491

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Project description:The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation rg<1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average N=325K) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with rg significantly < 1 (FDR<5%) (average rg=0.95); for example, white blood cell count had rg=0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.

Project description:BackgroundUnraveling gene-environment interaction can provide a novel insight into early disease prevention. Nevertheless, current understanding of the interplay between genetic predisposition and air pollution in relation to myocardial infarction (MI) risk remains limited. Furthermore, the potential long-term influence of air pollutants on MI incidence risk warrants more conclusive evidence in a community population.ObjectiveWe investigated interactions between genetic predisposition and exposure to air pollutants on MI incidence.MethodsThis study incorporated a sample of 456,354 UK Biobank participants and annual mean air pollution (PM2.5, PM10, NO2, and NOx) from the UK Department for Environment, Food and Rural Affairs (2006-2021). The Cox proportional hazards model was employed to explore MI incidence after chronic air pollutants exposure. By quantifying genetic risk through the calculation of polygenic risk score (PRS), this study further examined the interactions between genetic risk and exposure to air pollutants in the development of MI on both additive and multiplicative scales.ResultsAmong 456,354 participants, 9,114 incident MI events were observed during a median follow-up of 12.08 y. Chronic exposure to air pollutants was linked with an increased risk of MI occurrence. Specifically, the hazard ratios (per interquartile range) were 1.12 (95% CI: 1.10, 1.13) for PM2.5, 1.20 (95% CI: 1.19, 1.22) for PM10, 1.13 (95% CI: 1.12, 1.15) for NO2, and 1.12 (95% CI: 1.11, 1.13) for NOx. In terms of the joint effects, participants with high PRS and high level of air pollution exposure exhibited the greatest risk of MI among all study participants (∼255% to 324%). Remarkably, both multiplicative and additive interactions were detected in the ambient air pollutants exposure and genetic risk on the incidence of MI.DiscussionThere were interactions between exposure to ambient air pollutants and genetic susceptibility on the risk of MI onset. Moreover, the joint effects of these two exposures were greater than the effect of each factor alone. https://doi.org/10.1289/EHP14291.

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Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank.

Publications

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank.

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