Association of cardiometabolic genes with arsenic metabolism biomarkers in American Indian communities: The strong heart family study (SHFS)

• Authors: Poojitha Balakrishnan, Johns Hopkins University; Dhananjay Vaidya, Johns Hopkins University; Nora Franceschini, University of North Carolina; V. Saroja Voruganti, University of North Carolina; Matthew Gribble, Emory University; Karin Haack, Texas Biomedical Research Institute; Sandra Laston, University of Texas Rio Grande Valley; Jason G. Umans, MedStar Health Research Institute; Kevin A. Francesconi, University of Graz; Walter Goessler, University of Graz; Kari E. North, University of North Carolina; Elisa Lee, University of Oklahoma; Joseph Yracheta, Missouri Breaks Industries Research Inc.; Lyle G. Best, Missouri Breaks Industries Research Inc.; Jean W. Maccluer, Texas Biomedical Research Institute; Jack Kent, Jr., Texas Biomedical Research Institute; Shelley A. Cole, Texas Biomedical Research Institute; Ana Navas-Acien, Johns Hopkins University
• Journal Title: Environmental Health Perspectives
• Volume: Volume 125, Number 1
• Publisher: National Institute of Environmental Health Sciences (NIEHS) | 2017-01-01, Pages 15-22
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1289/EHP251
• Subjects: Biology, Genetics; Environmental Sciences; Health Sciences, Epidemiology

Abstract

Background: Metabolism of inorganic arsenic (iAs) is subject to inter-individual variability, which is explained partly by genetic determinants. Objectives: We investigated the association of genetic variants with arsenic species and principal components of arsenic species in the Strong Heart Family Study (SHFS). Methods: We examined variants previously associated with cardiometabolic traits (~ 200,000 from Illumina Cardio MetaboChip) or arsenic metabolism and toxicity (670) among 2,428 American Indian participants in the SHFS. Urine arsenic species were measured by high performance liquid chromatography–inductively coupled plasma mass spectrometry (HPLC-ICP-MS), and percent arsenic species [iAs, monomethylarsonate (MMA), and dimethylarsinate (DMA), divided by their sum × 100] were logit transformed. We created two orthogonal principal components that summarized iAs, MMA, and DMA and were also phenotypes for genetic analyses. Linear regression was performed for each phenotype, dependent on allele dosage of the variant. Models accounted for familial relatedness and were adjusted for age, sex, total arsenic levels, and population stratification. Single nucleotide polymorphism (SNP) associations were stratified by study site and were meta-analyzed. Bonferroni correction was used to account for multiple testing. Results: Variants at 10q24 were statistically significant for all percent arsenic species and principal components of arsenic species. The index SNP for iAs%, MMA%, and DMA% (rs12768205) and for the principal components (rs3740394, rs3740393) were located near AS3MT, whose gene product catalyzes methylation of iAs to MMA and DMA. Among the candidate arsenic variant associations, functional SNPs in AS3MT and 10q24 were most significant (p < 9.33 × 10–5). Conclusions: This hypothesis-driven association study supports the role of common variants in arsenic metabolism, particularly AS3MT and 10q24.