Dataset Information

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction.

ABSTRACT: Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interactions in the epidemiological setting using existing statistical approaches for genome-wide searches for interaction. In this paper, we describe a novel two-step approach using omics data to conduct genome-wide searches for gene-environment interactions. Using existing genome-wide SNP data from a large Bangladeshi cohort study specifically designed to assess the effect of arsenic exposure on health, we evaluated gene-arsenic interactions by first conducting genome-wide searches for SNPs that modify the effect of arsenic on molecular phenotypes (gene expression and DNA methylation features). Using this set of SNPs showing evidence of interaction with arsenic in relation to molecular phenotypes, we then tested SNP-arsenic interactions in relation to skin lesions, a hallmark characteristic of arsenic toxicity. With the emergence of additional omics data in the epidemiologic setting, our approach may have the potential to boost power for genome-wide interaction research, enabling the identification of interactions that will enhance our understanding of disease etiology and our ability to develop interventions targeted at susceptible sub-populations.

SUBMITTER: Argos M

PROVIDER: S-EPMC5908479 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction.

Argos Maria M Tong Lin L Roy Shantanu S Sabarinathan Mekala M Ahmed Alauddin A Islam Md Tariqul MT Islam Tariqul T Rakibuz-Zaman Muhammad M Sarwar Golam G Shahriar Hasan H Rahman Mahfuzar M Yunus Md M Graziano Joseph H JH Jasmine Farzana F Kibriya Muhammad G MG Zhou Xiang X Ahsan Habibul H Pierce Brandon L BL

Mammalian genome : official journal of the International Mammalian Genome Society 20180216 1-2

Identifying gene-environment interactions is a central challenge in the quest to understand susceptibility to complex, multi-factorial diseases. Developing an understanding of how inter-individual variability in inherited genetic variation alters the effects of environmental exposures will enhance our knowledge of disease mechanisms and improve our ability to predict disease and target interventions to high-risk sub-populations. Limited progress has been made identifying gene-environment interac ...[more]

PMID: 29453499

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Project description:BackgroundSingle nucleotide polymorphisms (SNPs) may influence arsenic methylation efficiency, affecting arsenic metabolism. Whether gene-environment interactions affect arsenic metabolism during pregnancy remains unclear, which may have implications for pregnancy outcomes.ObjectiveWe aimed to investigate main effects as well as potential SNP-arsenic interactions on arsenic methylation efficiency in pregnant women.MethodWe recruited 1613 pregnant women in Bangladesh, and collected two urine samples from each participant, one at 4-16 weeks, and the second at 21-37 weeks of pregnancy. We determined the proportions of each arsenic metabolite [inorganic As (iAs)%, monomethylarsonic acid (MMA)%, and dimethylarsinic acid (DMA)%] from the total urinary arsenic level of each sample. A panel of 63 candidate SNPs was selected for genotyping based on their reported associations with arsenic metabolism (including in As3MT, N6AMT1, and GSTO2 genes). We used linear regression models to assess the association between each SNP and DMA% with an additive allelic assumption, as well as SNP-arsenic interaction on DMA%. These analyses were performed separately for two urine collection time-points to capture differences in susceptibility to arsenic toxicity.ResultIntron variants for As3MT were associated with DMA%. rs9527 (β = -2.98%, PFDR = 0.008) and rs1046778 (β = 1.64%, PFDR = 0.008) were associated with this measure in the early gestational period; rs3740393 (β = 2.54%, PFDR = 0.002) and rs1046778 (β = 1.97%, PFDR = 0.003) in the mid-to-late gestational period. Further, As3MT, GSTO2, and N6AMT1 polymorphisms showed different effect sizes on DMA% conditional on arsenic exposure levels. However, SNP-arsenic interactions were not statistically significant after adjusting for false discovery rate (FDR). rs1048546 in N6AMT1 had the highest significance level in the SNP-arsenic interaction test during mid-to-late gestation (β = -1.8% vs. 1.4%, PGxE_FDR = 0.075). Finally, As3MT and As3MT/CNNM2 haplotypes were associated with DMA% at both time points.ConclusionWe found that not all genetic associations reported in arsenic methylation efficiency replicate in pregnant women. Arsenic exposure level has a limited effect in modifying the association between genetic variation and arsenic methylation efficiency.

Dataset Information

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction.

Publications

Screening for gene-environment (G×E) interaction using omics data from exposed individuals: an application to gene-arsenic interaction.

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