A genome‐wide linkage scan for iron phenotype quantitative trait loci: the HEIRS Family Study
Bibliographic record
Abstract
Iron overload phenotypes in persons with and without hemochromatosis are variable. To investigate this further, probands with hemochromatosis or evidence of elevated iron stores and their family members were recruited for a genome-wide linkage scan to identify potential quantitative trait loci (QTL) that contribute to variation in transferrin saturation (TS), unsaturated iron-binding capacity (UIBC), and serum ferritin (SF). Genotyping utilized 402 microsatellite markers with average spacing of 9 cM. A total of 943 individuals, 64% Caucasian, were evaluated from 174 families. After adjusting for age, gender, and race/ethnicity, there was evidence for linkage of UIBC to chromosome 4q logarithm of the odds (LOD) = 2.08, p = 0.001) and of UIBC (LOD = 9.52), TS (LOD = 4.78), and SF (LOD = 2.75) to the chromosome 6p region containing HFE (each p < 0.0001). After adjustments for HFE genotype and other covariates, there was evidence of linkage of SF to chromosome 16p (LOD = 2.63, p = 0.0007) and of UIBC to chromosome 5q (LOD = 2.12, p = 0.002) and to chromosome 17q (LOD = 2.19, p = 0.002). We conclude that these regions should be considered for fine mapping studies to identify QTL that contribute to variation in SF and UIBC.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".