Genetic variation in the FMO and GSTO gene clusters impacts arsenic metabolism in humans
Bibliographic record
Abstract
BACKGROUND: In Bangladesh, > 50 million individuals are chronically exposed to inorganic arsenic (iAs) through drinking water, increasing risk for cancer and other iAs-related diseases. Previous studies show that individuals' ability to metabolize and eliminate iAs, and their risk of toxicity, is influenced by genetic variation in the AS3MT and FTCD gene regions. METHODS: To identify additional loci influencing arsenic metabolism, we used data from Bangladeshi individuals to conduct genome-wide association analyses of the relative abundances of arsenic species measured in both urine (n = 6,540) and blood (n = 976). These species include iAs, monomethylated arsenic (MMA) and dimethylated arsenic (DMA) species. RESULTS: In analyses of urine arsenic species, we identified a novel association signal in the FMO gene cluster (1q24.3), with the lead SNP residing in FMO3 (MMA% P = 4.2x10-16). In analyses of blood arsenic species, we identified an additional signal in the FMO cluster, with the lead SNP residing in FMO4 (DMA% P = 2.3x10-22) and a novel signal at 10q25.1, with the lead SNP in GSTO1 (DMA% P = 5.3x10-13). Lead SNPs at FMO3 and GSTO1 are associated with the splicing of FMO3 and GSTO1, respectively, in multiple tissue types, but also contain missense variants. The lead SNPs at FMO4 are associated with FMO4 expression level in multiple tissue types. These newly identified SNPs did not show a clear association with risk for arsenic-induced skin lesions (P > 0.05), based on 3,448 cases and 5,207 controls. CONCLUSION: We identified novel loci influencing arsenic metabolites measured in both urine and blood. FMOs are involved in the oxidation of xenobiotics but have no known direct role in arsenic metabolism, while GSTO1 has a well-established role in catalyzing the reduction of arsenic species. The novel associations we report appear specific to blood or urine, with no detectable impact on skin toxicity risk, pointing to complexities in arsenic metabolism and its genetic contributors that require further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".