Associations Between Single Nucleotide Polymorphisms in Iron-Related Genes and Iron Status in Multiethnic Populations
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».