Genome-Wide Association Study Identifies Genetic Loci Associated with Iron Deficiency.
Notice bibliographique
Résumé
Abstract Abstract 4048 Poster Board III-983 Introduction Iron deficiency is the most common nutritional disorder in the world with an estimated two billion affected persons. Although commonly considered environmental in origin, the existence of multiple genetic disorders of iron metabolism in man, rodents and other vertebrates suggest a genetic contribution to iron deficiency. Methods: The Hemochromatosis and Iron Overload Screening (HEIRS) Study is a multi-center, multi-ethnic study in which transferrin saturation (TS), serum ferritin (SF), and HFE mutations were determined in 101,168 adults. To identify genomic locations associated with iron deficiency, we performed a genome-wide association study (GWAS) using DNA collected from white HEIRS Study participants who had SF ≤ 12 μg/L (cases) and an equal number of white controls (SF > 100 μg/L in men, SF > 50 μg/L in women) frequency-matched to cases by sex and geographic location. Men aged ≥ 25 y and women ≥ 50 y were included in both groups. Tissue body iron, an index of iron deficiency, was estimated from serum transferrin receptor (sTfR) and SF. Genotyping was performed with the Illumina HumanCNV370K Beadchip platform. Quality control filters excluded single nucleotide polymorphisms (SNPs) or samples with > 5% missing genotypes, SNPs showing heterozygosity or Hardy-Weinberg deviations (P<10−7), and SNPs with minor allele frequency < 0.02. Population admixture/structure was assessed using principal component analysis. Regression analysis was used to examine the association between outcomes (case-control status, tissue body iron, serum ferritin, transferrin receptor, serum iron, total iron-binding capacity [TIBC], and unsaturated iron-binding capacity [UIBC]) and each SNP genotype variable; covariates included age, sex, and geographic location. Replication for 56 SNPs was conducted in a population attending primary care clinics at a Veterans Affairs (VA) medical center using the iPlex platform. Eligibility within the VA replication population was restricted to age and self-reported white ethnicity as for the HEIRS subset from a total of 2559 people (138 women). VA participants with SF ≤ 20 μg/L were classified as iron-deficient cases and frequency matched 1:2 with controls (men with SF > 100 μg/L and women with SF > 50 μg/L). Results The GWAS genomic control parameter was not significantly different from 1.0. There were 392 cases (96 men) and 390 controls (96 men) in the HEIRS subset GWAS with average age (SD) of 59 (10) y and 61 (11) y, respectively. Geometric mean SF (minimum, maximum), and mean (SD) for sTfR and tissue body iron in the HEIRS subset were 7.5 (1.2, 12) μg/L, 6.4 (3.77) mg/kg and -2.0 (2.50) for cases and 141 (51, 881) μg/L, 3.0 (0.98) mg/kg and 10.8 (2.5) for controls. After quality control tests, GWAS analysis included genotype data for 331,060 SNPs in 734 individuals (364 cases, 370 controls). For the VA replication population there were 67 male and 11 female cases, and 136 male and 27 female controls for whom DNA was successfully prepared; the average age (SD) was 68 (12) y for cases and 65 (11) y for controls. Regression analysis identified seven SNPs within four independent regions that replicated associations found in the GWAS (GWAS P<1×10-4 and VA P<0.05).The SNP rs6735681 on chromosome 2p24 was associated with serum iron (GWAS P<3.9×10-5, VA P=0.038). Three SNPs on chromosome 2p14 (rs6750096, rs2698541 and rs2698530) significantly influenced both TIBC and UIBC (GWAS P<2.9×10-5, VA P< 0.04 for all). Two SNPs in the TF gene region on chromosome 3q22 also showed significant effects on TIBC and UIBC (GWAS P<4.7×10-6, VA P<0.03 for all). The SNP rs10512064 on chromosome 9q21 was associated with serum ferritin concentration and tissue body iron (GWAS P<2.5×10-5, VA P<0.05 for both). Conclusion: From these GWAS and replication studies, we have identified three new genetic loci and one known iron gene, TF, associated with iron phenotype variability. These results point to specific loci as targets for gene identification and TF polymorphisms as determinants of iron metabolism, which in turn may play a role in regulation of body iron status. 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) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».