Heritability of serum iron measures in the hemochromatosis and iron overload screening (HEIRS) family study
Bibliographic record
Abstract
Heritability is the proportion of observed variation in a trait among individuals in a population that is attributable to hereditary factors. The Hemochromatosis and Iron Overload Screening family study estimated heritability of serum iron measures. Probands were HFE C282Y homozygotes or non-C282Y homozygotes with elevated transferrin saturation (TS > 50%, men; TS > 45%, women) and serum ferritin concentration (SF > 300 microg/L, men; SF > 200 microg/L, women). Heritability (h(2)) was estimated by variance component analysis of TS, natural logarithm (ln) of SF, and unsaturated iron-binding capacity (UIBC). Participants (N = 942) were 77% Caucasians, 10% Asians, 8% Hispanics, and 5% other race/ethnicities. Average age (SD) was 49 (16) years; 57% were female. For HFE C282Y homozygote probands and their family members, excluding variation due to HFE C282Y and H63D genotype and measured demographic and environmental factors, the residual h(2) (SE) was 0.21 (0.07) for TS, 0.37 (0.08) for ln SF, and 0.34 (0.08) for UIBC (all P < 0.0004 for comparisons with zero). For the non-C282Y homozygote proband group, residual h(2) was significant with a value of 0.64 (0.26) for ln SF (P = 0.0096). In conclusion, serum iron measures have significant heritability components, after excluding known genetic and nongenetic sources of variation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".