Meta-analysis of Genome-Wide Linkage Studies of Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is considered as a hereditary skin disease. Recently, a latest form of genetic research, genome-wide linkage study, has been carried out to detect its specific susceptibility genes. Since the results of these studies have been inconsistent, with linkage confirmed in different regions, we performed this meta-analysis. OBJECTIVE: To assess heritability and identify chromosomal regions showing evidence of AD. METHODS: A comprehensive and systematic search was conducted with PubMed databases. We combined results from three non-overlapping genome-wide linkage studies of AD, using the heterogeneity-based genome scan meta-analysis (HEGESMA) method, a rank-based analysis that assesses the strongest evidence for linkage within bins of a 30-centimorgan width on autosomes and the X chromosome. RESULTS: The following ten bins had a p value of less than .05 in both weighted and unweighted analyses, suggesting that these bins contain AD-linked loci: 3.2, 3.4, 3.5, 4.5, 5.1, 5.2, 6.3, 9.2, 9.4, and 22.1. HEGESMA produced significant genome-wide evidence for linkage on 3p14.1-q12.3, with high average rank and low between-study heterogeneity; 5pter-p15.1 represents new significant loci not reported previously. CONCLUSION: HEGESMA confirms the region encoding CD80, CD86, and interleukins on chromosomes 3 and 5. The filaggrin gene (FLG) may be the susceptibility factor.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".