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 American Indians and European Americans (rs191177668) that was in strong linkage disequilibrium (LD) with a lead SNP in Bangladesh (rs145537350). Beyond this shared signal, differences in LDpatterns, 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. Author summary: Inorganic arsenic is highly toxic, and exposure to arsenic increases risk for multiple diseases, including cancer. Individuals differ in their ability to metabolize and excrete arsenic, in part due to inherited genetic variation in and around the AS3MT gene, and these differences impact arsenic toxicity risk. To identify candidate causal variants in the AS3MT region, we applied fine-mapping methods to targeted sequencing data from The Health Effects of Arsenic Longitudinal Study (HEALS), the Strong Heart Study (SHS), and the New Hampshire Skin Cancer Study (NHSCS) (Bangladesh, American Indian, and European American populations). We detected 3 independent association signals HEALS, 2 in SHS, and 1 in NHSCS; and we identified a set of candidate causal variants for each of these signals. One of the identified signals represents a potential causal variant that impacts arsenic metabolism across all three populations. Using omics-QTL co-localization analyses, we show that some of the variants identified act through regulation AS3MT in multiple tissue types. Overall, this work increases our understanding of variation in the AS3MT region and its role in arsenic metabolism across populations. [ABSTRACT FROM AUTHOR]