Associations between Single Nucleotide Polymorphisms in Iron-Related Genes and Iron Status in Multiethnic Populations
Bibliographic record
Abstract
The existence of multiple inherited disorders of iron metabolism suggests genetic contributions to iron deficiency. We previously performed a genome-wide association study of iron-related single nucleotide polymorphisms (SNPs) using DNA from white men aged ≥ 25 y and women ≥ 50 y in the Hemochromatosis and Iron Overload Screening (HEIRS) Study with serum ferritin (SF) ≤ 12 µg/L (cases) and controls (SF >100 µg/L in men, SF >50 µg/L in women). We report a follow-up study of white, African-American, Hispanic, and Asian HEIRS participants, analyzed for association between SNPs and eight iron-related outcomes. Three chromosomal regions showed association across multiple populations, including SNPs in the TF and TMPRSS6 genes, and on chromosome 18q21. A novel SNP rs1421312 in TMPRSS6 was associated with serum iron in whites (p = 3.7 × 10(-6)) and replicated in African Americans (p = 0.0012).Twenty SNPs in the TF gene region were associated with total iron-binding capacity in whites (p<4.4 × 10(-5)); six SNPs replicated in other ethnicities (p<0.01). SNP rs10904850 in the CUBN gene on 10p13 was associated with serum iron in African Americans (P = 1.0 × 10(-5)). These results confirm known associations with iron measures and give unique evidence of their role in different ethnicities, suggesting origins in a common founder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".