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Gene-environment interaction and maternal arsenic methylation efficiency during pregnancy

BACKGROUND: Single 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. OBJECTIVE: We aimed to...

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Detalles Bibliográficos
Autores principales: Gao, Shangzhi, Mostofa, Md. Golam, Quamruzzaman, Quazi, Rahman, Mahmudur, Rahman, Mohammad, Su, Li, Hsueh, Yu-mei, Weisskopf, Marc, Coull, Brent, Christiani, David C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7592115/
https://www.ncbi.nlm.nih.gov/pubmed/30703610
http://dx.doi.org/10.1016/j.envint.2019.01.042
Descripción
Sumario:BACKGROUND: Single 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. OBJECTIVE: We aimed to investigate main effects as well as potential SNP-arsenic interactions on arsenic methylation efficiency in pregnant women. METHOD: We 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 GST02 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. RESULT: Intron variants for As3MT were associated with DMA%. rs9527 (β = − 2.98%, P(FDR) = 0.008) and rs1046778 (β = 1.64%, P(FDR) = 0.008) were associated with this measure in the early gestational period; rs3740393 (β = 2.54%, P(FDR) = 0.002) and rs1046778 (β = 1.97%, P(FDR) = 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%, P(GXE_FDR) = 0.075). Finally, As3MT and As3MT/CNNM2 haplotypes were associated with DMA% at both time points. CONCLUSION: We 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.