Trans-ethnic analysis of the human leukocyte antigen region for ulcerative colitis reveals shared but also ethnicity-specific disease associations
Bibliographic record
Abstract
ABSTRACT Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the gut. Genetic association studies have identified the highly variable human leukocyte antigen (HLA) region as the strongest susceptibility locus for IBD, and specifically DRB1*01:03 as a determining factor for ulcerative colitis (UC). However, for most of the association signal such a delineation could not be made due to tight structures of linkage disequilibrium within the HLA. The aim of this study was therefore to further characterize the HLA signal using a trans-ethnic approach. We performed a comprehensive fine mapping of single HLA alleles in UC in a cohort of 9,272 individuals with African American, East Asian, Puerto Rican, Indian and Iranian descent and 40,691 previously analyzed Caucasians, additionally analyzing whole HLA haplotypes. We computationally characterized the binding of associated HLA alleles to human self-peptides and analysed the physico-chemical properties of the HLA proteins and predicted self-peptidomes. Highlighting alleles of the HLA-DRB1*15 group and their correlated HLA- DQ-DR haplotypes, we identified consistent associations across different ethnicities but also identified population-specific signals. We observed that DRB1*01:03 is mostly present in individuals of Western European descent and hardly present in non-Caucasian individuals. We found peptides predicted to bind to risk HLA alleles to be rich in positively charged amino acids such. We conclude that the HLA plays an important role for UC susceptibility across different ethnicities. This research further implicates specific features of peptides that are predicted to bind risk and protective HLA proteins.
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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 | 0.000 | 0.000 |
| 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".