The <i><scp>COL</scp>5A3</i> and <i><scp>MMP</scp>9</i> genes interact in eczema susceptibility
Bibliographic record
Abstract
Summary Background Genetic studies of eczema have identified many genes, which explain only 14% of the heritability. Missing heritability may be partly due to ignored gene–gene (G‐G) interactions. Objective Our aim was to detect new interacting genes involved in eczema. Methods The search for G‐G interaction in eczema was conducted using a two‐step approach, which included as a first step, a biological selection of genes, which are involved either in the skin or epidermis development or in the collagen metabolism, and as a second step, an interaction analysis of the selected genes. Analyses were carried out at both SNP and gene levels in three asthma‐ascertained family samples: the discovery dataset of 388 EGEA (Epidemiological study on the Genetics and Environment of Asthma) families and the two replication datasets of 253 SLSJ (Saguenay–Lac‐Saint‐Jean) families and 207 MRCA (Medical Research Council) families. Results One pair of SNPs, rs2287807 in COL5A3 and rs17576 in MMP9, that were detected in EGEA at P ≤ 10−5 showed significant interaction by meta‐analysis of EGEA, SLSJ and MRCA samples (P = 1.1 × 10−8 under the significant threshold of 10−7). Gene‐based analysis confirmed strong interaction between COL5A3 and MMP9 (P = 4 × 10−8 under the significant threshold of 4 × 10−6) by meta‐analysis of the three datasets. When stratifying the data on asthma, this interaction remained in both groups of asthmatic and non‐asthmatic subjects. Conclusion This study identified significant interaction between two new genes, COL5A3 and MMP9, which may be accounted for by a degradation of COL5A3 by MMP9 influencing eczema susceptibility. Further confirmation of this interaction as well as functional studies is needed to better understand the role of these genes in eczema.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".