1
|
Chernoff MB, Delgado D, Tong L, Chen L, Oliva M, Tamayo LI, Best LG, Cole S, Jasmine F, Kibriya MG, Nelson H, Huang L, Haack K, Kent J, Umans JG, Graziano J, Navas-Acien A, Karagas MR, Ahsan H, Pierce BL. Sequencing-based fine-mapping and in silico functional characterization of the 10q24.32 arsenic metabolism efficiency locus across multiple arsenic-exposed populations. PLoS Genet 2023; 19:e1010588. [PMID: 36668670 PMCID: PMC9891528 DOI: 10.1371/journal.pgen.1010588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 02/01/2023] [Accepted: 12/20/2022] [Indexed: 01/22/2023] Open
Abstract
Inorganic arsenic is highly toxic and carcinogenic to humans. Exposed individuals vary in their ability to metabolize arsenic, and variability in arsenic metabolism efficiency (AME) is associated with risks of arsenic-related toxicities. Inherited genetic variation in the 10q24.32 region, near the arsenic methyltransferase (AS3MT) gene, is associated with urine-based measures of AME in multiple arsenic-exposed populations. To identify potential causal variants in this region, we applied fine mapping approaches to targeted sequencing data generated for exposed individuals from Bangladeshi, American Indian, and European American populations (n = 2,357, 557, and 648 respectively). We identified three independent association signals for Bangladeshis, two for American Indians, and one for European Americans. The size of the confidence sets for each signal varied from 4 to 85 variants. There was one signal shared across all three populations, represented by the same SNP in American Indians and European Americans (rs191177668) and in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LD patterns, minor allele frequency (MAF) (e.g., rs12573221 ~13% in Bangladesh ~0.2% among American Indians), and/or heterogeneity in effect sizes across populations likely contributed to the apparent population specificity of the additional identified signals. One of our potential causal variants influences AS3MT expression and nearby DNA methylation in numerous GTEx tissue types (with rs4919690 as a likely causal variant). Several SNPs in our confidence sets overlap transcription factor binding sites and cis-regulatory elements (from ENCODE). Taken together, our analyses reveal multiple potential causal variants in the 10q24.32 region influencing AME, including a variant shared across populations, and elucidate potential biological mechanisms underlying the impact of genetic variation on AME.
Collapse
Affiliation(s)
- Meytal Batya Chernoff
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
- Interdisciplinary Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America
- University of Chicago Pritzker School of Medicine, Chicago, Illinois, United States of America
| | - Dayana Delgado
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Lin Chen
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Meritxell Oliva
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Lizeth I. Tamayo
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Lyle G. Best
- Missouri Breaks Industries Research Inc, Eagle Butte, South Dakota, United States of America
| | - Shelley Cole
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Farzana Jasmine
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Muhammad G. Kibriya
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
| | - Heather Nelson
- School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Lei Huang
- Center for Research Informatics, University of Chicago, Chicago, Illinois, United States of America
| | - Karin Haack
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Jack Kent
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Jason G. Umans
- MedStar Health Research Institute, Hyattsville, Maryland, United States of America
- Georgetown-Howard Universities Center for Clinical and Translational Science, Georgetown University, Washington, District of Columbia, United States of America
| | - Joseph Graziano
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
- Department of Pharmacology, Columbia University, New York City, New York, United States of America
| | - Ana Navas-Acien
- Mailman School of Public Health, Columbia University, New York City, New York, United States of America
| | - Margaret R. Karagas
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America
| | - Habib Ahsan
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
- Comprehensive Cancer Center, University of Chicago, Chicago, Illinois, United States of America
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Brandon L. Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois, United States of America
- Comprehensive Cancer Center, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| |
Collapse
|