Associations Between Single Nucleotide Polymorphisms in Iron-Related Genes and Iron Status in Multiethnic Populations
Bibliographic record
Abstract
Abstract Abstract 2105 The existence of multiple inherited disorders of iron metabolism in man, rodents and other vertebrates suggests genetic contributions to iron deficiency. We hypothesized that common variants in genes involved in iron metabolism may modulate susceptibility or resistance to the development of iron deficiency in humans. To examine the association between single nucleotide polymorphisms (SNPs) in key genes involved in iron metabolism pathways, we previously performed a genome-wide association study using DNA collected 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 now report on a multiethnic follow-up association study of HEIRS participants. Candidate SNPs were identified from our GWAS and the scientific literature. Population samples of whites, African Americans, Hispanics, and Asians from the U.S. and Canada were analyzed separately for association between SNPs and case-control status and each of seven quantitative outcomes including serum iron, total iron-binding capacity (TIBC), unsaturated iron-binding capacity (UIBC), transferrin saturation, SF, serum transferrin receptor, and body iron. There were 1084 white (357 cases, 727 controls), 153 Asian (51 cases, 102 controls), 221 African American (77 cases, 144 controls) and 233 of 239 Hispanic individuals (79 cases, 160 controls) that passed quality control. For the African-American and Hispanic samples, ancestry proportions were estimated based on genotypes of ancestry informative markers. Regression analysis was used to examine the association between case-control status and quantitative serum iron measures and 1134, 1115, 1113 and 1134 SNP genotypes in the white, African-American, Hispanic, and Asian population samples, respectively. Model predictors included age, sex, the estimated ancestry proportion (for African American and Hispanic only), genotype, and measured covariates that showed nominally significant associations with the outcome. Three chromosomal regions showed evidence of association across multiple populations, including SNPs in the TF gene on chromosome 3q22, the TMPRSS6 gene on chromosome 22q12, and loci on chromosome 18q21. SNP rs1421312 in TMPRSS6 was associated with serum iron in whites (p=4.7×10−7) and was replicated in African Americans (p=0.0012).Twenty SNPs in the TF gene region were significantly associated with TIBC in the white sample (p<4.4×10−5); six SNPs were replicated in other ethnicities (p< 0.01). SNP rs10904850 in the CUBN gene on 10p13 was significantly associated with serum iron in the African-American sample (P=1.0×10−5). Mutations in the TMPRSS6 gene have been implicated in iron-refractory iron deficiency anemia through linkage studies. We found a novel SNP in TMPRSS6 that was associated with serum iron in whites and replicated in African Americans, suggesting a role for this SNP in increasing the risk of iron deficiency in affected persons. Our results confirm known associations with iron measures and give evidence of their role in different ethnic groups, a unique aspect of this study, suggesting origins in a common founder. Disclosures: No relevant conflicts of interest to declare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".