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Record W2979907661 · doi:10.1182/blood.v122.21.179.179

Exome Sequencing Identifies Genes and Variant Alleles Associated With Severity Of Iron Overload In Hemochromatosis HFE C282Y Homozygotes

2013· article· en· W2979907661 on OpenAlexaff
Christine E. McLaren, Mary J. Emond, Pradyumna D. Phatak, Paul C. Adams, V. Nathan Subramaniam, James C. Barton, Lawrie W. Powell, Lyle C. Gurrin, Katrina J. Allen, Deborah A. Nickerson, Tin Louie, Grant A. Ramm, Gregory J. Anderson, Gordon D. McLaren

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsHemochromatosisExome sequencingPhlebotomyHereditary hemochromatosisGeneticsBiologySerum ironExomeHepcidinAlleleConfoundingInternal medicineMedicineGeneMutationAnemia

Abstract

fetched live from OpenAlex

Abstract Variability in the severity of iron overload among homozygotes for the HFE C282Y polymorphism is one of the major problems extant in our understanding of hereditary hemochromatosis (HH). We conducted exome sequencing of DNA from C282Y homozygotes with markedly increased iron stores (cases) and C282Y homozygotes with normal or mildly increased iron stores (controls) to identify rare and common causal variants associated with variability of disease expression in HH. Criteria for cases included serum ferritin >1000 µg/L at diagnosis, and (a) mobilized body iron >10 g by quantitative phlebotomy, and/or (b) hepatic iron concentration >236 µmol/g dry weight. Criteria for controls included (a) serum ferritin <300 µg/L, or (b) age ≥50 y with ≤3.0 g iron removed by phlebotomy or age ≥40 y with ≤2.5 g iron removed by phlebotomy to achieve serum ferritin <50 µg/L. Deep sequencing of the full exome was performed in 33 cases and 14 controls. After quality control filtering, the dataset included 82,068 SNPs and 1,403 insertions/deletions (indels). Our initial analysis tested for differences in the distribution of variants between groups for each gene separately using the Sequence Kernel Association Test (SKAT) that includes rare and common variants but downweights the contribution of common variants to the test statistic. Only non-synonymous variants were included in the by-gene tests. Principal components were constructed from the exome variants to adjust for possible confounding by ancestry and to confirm no ancestral outliers. All study participants were male, and all clustered closely together within a larger group of Europeans in a principal components analysis of ancestry. Mean (SD) ages at presentation were 54 (11.0) y and 56 (9.4) y for cases and controls, respectively. Median serum ferritin was 2788 µg/L in those with increased iron stores and 309 μg/L in those with normal or mildly increased iron stores. The median transferrin saturation (94%) was greater in cases than in the comparison group (70%). In a preliminary analysis, we found 9 genes associated with case-control status. To separate effects of alcohol use and/or alcohol addiction variants, an analysis was conducted to compare the 13 controls and 22 cases who reported never using alcohol or only very light use. The two most significant genes identified in this comparison were GNPAT (p=7.4x10-6) and CDHR2 (p=2.8x10-4). A quantile-quantile (QQ) plot is shown in the Figure, comparing the observed distribution of –(log10p-values) from 10,337 genes to the expected uniform distribution if there were no variants modifying severity of expression, and gives evidence of the effect of the GNPAT gene.Figure 1Figure 1. Inspection of the two variants contributing to the GNPAT by-gene p-value revealed one missense variant (rs11558492) for which 0/13 controls had a polymorphism, while 16/22 cases had at least one missense variant, and one case was homozygous for this missense variant. The latter case presented at the early age of 26 with a serum ferritin of 1762 µg/L, 4+ hepatocellular iron and hepatic iron concentration of 284.4 µmol/g dry weight. GNPAT (aka DHAPAT) mutations/deletions have been found in peroxisomal disease, a class of diseases in which increased hepatic iron is observed (Biochim Biophys Acta 1801:272-280, 2010). GNPAT rs11558492 is common among people of European descent but might interact with aberrant HFE to increase risk of hepatic iron overload. Three rare variants in CDHR2 accounted for its low p-value, having a cumulative frequency of 4/13 among controls and 0/22 among cases: rs115050587, rs752138, rs143224505 with minor allele frequencies, MAF = 1.4%, 4.7% and 0.06%, respectively. The first two polymorphisms are predicted to be highly damaging by PolyPhen2 and the third probably damaging. Expression levels of CDHR2 recently have been associated with increased hepatocyte iron and elevated serum ferritin in liver allograft patients (J Clin Invest 122:368-382, 2012). These data indicate associations between iron status in HFE C282Y homozygotes and genes with previous links to iron overload that may modify severity of disease expression. Of note, the data suggest that more than one modifier gene may be involved in determining severity of disease in HFE C282Y homozygotes. Our results identify candidate genes for expanded studies that would examine their functional significance for iron absorption and metabolism. 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.009
GPT teacher head0.210
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2013
Admission routes1
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