47 Cross-ancestry GWAS meta-analysis of keloids discovers novel susceptibility loci in diverse populations
Bibliographic record
Abstract
OBJECTIVES/GOALS: We aimed to conduct an updated genome-wide meta-analysis of keloids in expanded populations, including those most afflicted by keloids. Our overall objective was to improve understanding of keloid development though the identification and further characterization of keloid-associated genes with genetically predicted gene expression (GPGE). METHODS/STUDY POPULATION: We used publicly available summary statistics from several large-scale DNA biobanks, including the UK Biobank, FinnGen, and Biobank Japan. We also leveraged data from the Million Veterans Program and performed genome-wide association studies of keloids in BioVU and eMERGE. For each of these datasets, cases were determined from ICD-9/ICD-10 codes and phecodes. With these data we conducted fixed effects meta-analysis, both across ancestries and stratified by broad ancestry groups. This approach allowed us to consider cumulative evidence for genetic risk factors for keloids and explore potential ancestry-specific components of risk. We used FUMA for functional annotation of results and LDSC to estimate ancestry-specific heritability. We performed GPGE analysis using S-PrediXcan with GTEx v8 tissues. RESULTS/ANTICIPATED RESULTS: We detected 30 (23 novel) genomic risk loci in the cross-ancestry analysis. Major risk loci were broadly consistent between ancestries, with variable effects. Keloid heritability estimates from LDSC were 6%, 21%, and 34% for European, East Asian, and African ancestry, respectively. The top hit (P = 1.7e-77) in the cross-ancestry analysis was at a replicated variant (rs10863683) located downstream of LINC01705. GPGE analysis identified an association between decreased risk of keloids and increased expression of LINC01705 in fibroblasts (P = 3.6e10-20), which are important in wound healing. The top hit in the African-ancestry analysis (P = 5.5e-31) was a novel variant (rs34647667) in a conserved region downstream of ITGA11. ITGA11 encodes a collagen receptor and was previously associated with uterine fibroids. DISCUSSION/SIGNIFICANCE: This work significantly increases the yield of discoveries from keloid genetic association studies, describing both common and ancestry-specific effects. Stark differences in heritability support a potential adaptive origin for keloid disparities. Further work will continue to examine keloids in the broader context of other fibrotic diseases.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.020 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".