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Genome-wide association study identifies chromosome 10q24.32 variants associated with arsenic metabolism and toxicity phenotypes in Bangladesh.

Pierce BL, Kibriya MG, Tong L, Jasmine F, Argos M, Roy S, Paul-Brutus R, Rahaman R, Rakibuz-Zaman M, Parvez F, Ahmed A, Quasem I, Hore SK, Alam S, Islam T, Slavkovich V, Gamble MV, Yunus M, Rahman M, Baron JA, Graziano JH, Ahsan H - PLoS Genet. (2012)

Bottom Line: Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10(-8)) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations.Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10(-12)) and neighboring gene C10orf32 (P = 10(-44)), which are involved in C10orf32-AS3MT read-through transcription.These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Studies, The University of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Arsenic contamination of drinking water is a major public health issue in many countries, increasing risk for a wide array of diseases, including cancer. There is inter-individual variation in arsenic metabolism efficiency and susceptibility to arsenic toxicity; however, the basis of this variation is not well understood. Here, we have performed the first genome-wide association study (GWAS) of arsenic-related metabolism and toxicity phenotypes to improve our understanding of the mechanisms by which arsenic affects health. Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10(-8)) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations. In a follow-up analysis of 1,085 individuals with arsenic-induced premalignant skin lesions (the classical sign of arsenic toxicity) and 1,794 controls, we show that one of these five variants (rs9527) is also associated with skin lesion risk (P = 0.0005). Using a subset of individuals with prospectively measured arsenic (n = 769), we show that rs9527 interacts with arsenic to influence incident skin lesion risk (P = 0.01). Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10(-12)) and neighboring gene C10orf32 (P = 10(-44)), which are involved in C10orf32-AS3MT read-through transcription. This is the largest and most comprehensive genomic investigation of arsenic metabolism and toxicity to date, the only GWAS of any arsenic-related trait, and the first study to implicate 10q24.32 variants in both arsenic metabolism and arsenical skin lesion risk. The observed patterns of associations suggest that MMA% and DMA% have distinct genetic determinants and support the hypothesis that DMA is the less toxic of these two methylated arsenic species. These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide.

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Multiple variants in the 10q24.32 region show independent associations with MMA% (n = 1,313).P-values were generated using mixed models adjusted for age, sex, and water arsenic concentration. The strongest associated SNP is labeled in each panel. Panel A shows the overall association results. Panel B shows P-values from models that are adjusted for rs4919694. Panel C shows P-values from models adjusted for both rs4919694 and rs4290163.
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pgen-1002522-g002: Multiple variants in the 10q24.32 region show independent associations with MMA% (n = 1,313).P-values were generated using mixed models adjusted for age, sex, and water arsenic concentration. The strongest associated SNP is labeled in each panel. Panel A shows the overall association results. Panel B shows P-values from models that are adjusted for rs4919694. Panel C shows P-values from models adjusted for both rs4919694 and rs4290163.

Mentions: The strongest association observed for MMA% was rs4919694 (P = 2.9×10−8) (Figure 2). After conditioning on rs4919694, residual association was still observed (rs4290163; P = 7.0×10−5). This association is much weaker without adjustment for rs4919694 (P = 0.03) due to LD between rs4919694 and rs4290163 (D′ = 0.80, r2 = 0.09 in our data; D′ = 0.80, r2 = 0.04 in HapMap GIH). Aftern conditioning on both SNPs, residual association was observed for rs11191659 (P = 0.0009), a SNP in moderate LD with rs9527, the top SNP from the %DMA analysis (D′ = 0.66, r2 = 0.23 in our data; D′ = 0.82, r2 = 0.30 in HapMap GIH). Conditioning on all three SNPs eliminated the 10q24.32 association signal. Imputation of unobserved genotypes in the region did not reveal associations stronger than those observed for the measured genotypes (Figure S6). Multivariate models for %DMA and %MMA including all five of the above-mentioned SNPs are described in Table 1. Outside of the 10q24.32 region, there was no genome-wide significant (P<5×10−8) association signal for DMA% or MMA%.


Genome-wide association study identifies chromosome 10q24.32 variants associated with arsenic metabolism and toxicity phenotypes in Bangladesh.

Pierce BL, Kibriya MG, Tong L, Jasmine F, Argos M, Roy S, Paul-Brutus R, Rahaman R, Rakibuz-Zaman M, Parvez F, Ahmed A, Quasem I, Hore SK, Alam S, Islam T, Slavkovich V, Gamble MV, Yunus M, Rahman M, Baron JA, Graziano JH, Ahsan H - PLoS Genet. (2012)

Multiple variants in the 10q24.32 region show independent associations with MMA% (n = 1,313).P-values were generated using mixed models adjusted for age, sex, and water arsenic concentration. The strongest associated SNP is labeled in each panel. Panel A shows the overall association results. Panel B shows P-values from models that are adjusted for rs4919694. Panel C shows P-values from models adjusted for both rs4919694 and rs4290163.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3285587&req=5

pgen-1002522-g002: Multiple variants in the 10q24.32 region show independent associations with MMA% (n = 1,313).P-values were generated using mixed models adjusted for age, sex, and water arsenic concentration. The strongest associated SNP is labeled in each panel. Panel A shows the overall association results. Panel B shows P-values from models that are adjusted for rs4919694. Panel C shows P-values from models adjusted for both rs4919694 and rs4290163.
Mentions: The strongest association observed for MMA% was rs4919694 (P = 2.9×10−8) (Figure 2). After conditioning on rs4919694, residual association was still observed (rs4290163; P = 7.0×10−5). This association is much weaker without adjustment for rs4919694 (P = 0.03) due to LD between rs4919694 and rs4290163 (D′ = 0.80, r2 = 0.09 in our data; D′ = 0.80, r2 = 0.04 in HapMap GIH). Aftern conditioning on both SNPs, residual association was observed for rs11191659 (P = 0.0009), a SNP in moderate LD with rs9527, the top SNP from the %DMA analysis (D′ = 0.66, r2 = 0.23 in our data; D′ = 0.82, r2 = 0.30 in HapMap GIH). Conditioning on all three SNPs eliminated the 10q24.32 association signal. Imputation of unobserved genotypes in the region did not reveal associations stronger than those observed for the measured genotypes (Figure S6). Multivariate models for %DMA and %MMA including all five of the above-mentioned SNPs are described in Table 1. Outside of the 10q24.32 region, there was no genome-wide significant (P<5×10−8) association signal for DMA% or MMA%.

Bottom Line: Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10(-8)) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations.Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10(-12)) and neighboring gene C10orf32 (P = 10(-44)), which are involved in C10orf32-AS3MT read-through transcription.These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide.

View Article: PubMed Central - PubMed

Affiliation: Department of Health Studies, The University of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Arsenic contamination of drinking water is a major public health issue in many countries, increasing risk for a wide array of diseases, including cancer. There is inter-individual variation in arsenic metabolism efficiency and susceptibility to arsenic toxicity; however, the basis of this variation is not well understood. Here, we have performed the first genome-wide association study (GWAS) of arsenic-related metabolism and toxicity phenotypes to improve our understanding of the mechanisms by which arsenic affects health. Using data on urinary arsenic metabolite concentrations and approximately 300,000 genome-wide single nucleotide polymorphisms (SNPs) for 1,313 arsenic-exposed Bangladeshi individuals, we identified genome-wide significant association signals (P<5×10(-8)) for percentages of both monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA) near the AS3MT gene (arsenite methyltransferase; 10q24.32), with five genetic variants showing independent associations. In a follow-up analysis of 1,085 individuals with arsenic-induced premalignant skin lesions (the classical sign of arsenic toxicity) and 1,794 controls, we show that one of these five variants (rs9527) is also associated with skin lesion risk (P = 0.0005). Using a subset of individuals with prospectively measured arsenic (n = 769), we show that rs9527 interacts with arsenic to influence incident skin lesion risk (P = 0.01). Expression quantitative trait locus (eQTL) analyses of genome-wide expression data from 950 individual's lymphocyte RNA suggest that several of our lead SNPs represent cis-eQTLs for AS3MT (P = 10(-12)) and neighboring gene C10orf32 (P = 10(-44)), which are involved in C10orf32-AS3MT read-through transcription. This is the largest and most comprehensive genomic investigation of arsenic metabolism and toxicity to date, the only GWAS of any arsenic-related trait, and the first study to implicate 10q24.32 variants in both arsenic metabolism and arsenical skin lesion risk. The observed patterns of associations suggest that MMA% and DMA% have distinct genetic determinants and support the hypothesis that DMA is the less toxic of these two methylated arsenic species. These results have potential translational implications for the prevention and treatment of arsenic-associated toxicities worldwide.

Show MeSH
Related in: MedlinePlus