Summary of single nucleotide polymorphisms in filaggrin associated with atopic dermatitis
Bibliographic record
Abstract
Abstract Background Loss of function mutations in the filaggrin gene ( FLG) play an important role in the pathogenesis of atopic dermatitis (AD). However, FLG is structurally challenging to sequence using conventional high‐throughput techniques. Genome‐wide association studies (GWAS) chips and imputation panels are also not designed to detect most of these mutations. Furthermore, bioinformatics tools have variable sensitivity for identification of loss of function variants. Targeted sequencing is often performed for AD but requires a comprehensive list of potential variants. Objectives This study sought to compile all published FLG single nucleotide polymorphisms (SNPs) in AD and characterize the methods for assessing the associated phenotype. Methods We searched nine electronic databases for studies that reported measures of association between FLG and AD. Data regarding FLG SNPs and participant demographics were extracted. The identified SNPs were compared to those available in the National Human Genome Research Institute‐European Bioinformatics Institute (NHGRI‐EBI) GWAS Catalogue and the 1000Genomes reference panel. Results We identified 168 SNPs in FLG that have been associated with AD, with the most studied being R501X, 2282del4, R2447X, 3321delA, S3247X and p.S2554 in European and Asian ancestries. A total of 153 of these SNPs are not available from GWAS studies, and 78 are not included in the 1000Genomes reference panel. Conclusions Because FLG is a complex gene, current GWAS chips do not capture most of the polymorphisms that have been associated with AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".