P32 Statistical colocalization identifies twenty-four novel risk loci associated with disease risk of primary biliary cholangitis
Bibliographic record
Abstract
<h3></h3> Genome-wide association (GWA) studies of primary biliary cholangitis (PBC) have identified more than 60 risk loci for the disease. Causal variants and candidate genes at those loci remain obscure, however, limiting biological insight. We sought to address this limitation using statistical colocalisation. Accordingly, we applied coloc and HyPrColoc to summary statistics from the genome wide meta-analysis (GWMA) of PBC by Cordell et al. (2021); GWA studies of 15 other immune mediated inflammatory diseases; and GWA studies of DNA methylation, gene expression, and plasma proteins, respectively, focusing on loci with at least suggestive evidence of association with PBC (P <1×10–5). For each locus and each trait, we determined the likelihood that PBC and the other trait share a common association signal; defined a credible set of variants which could account for that shared association; and, for each variant in the credible set, determined its probability of being the single causal variant at that locus. We used colocalisation with methylation, expression, or protein quantitative trait loci to prioritize candidate genes for in silico drug efficacy screening. We found robust evidence of colocalization (PP3+PP4 ≥0.7) at 74 loci, including 24 loci with only suggestive evidence of association (5×10–8 <P <1×10–5) in the GWMA of 2021, which are validated herein as genuine risk loci for PBC. We implicated a single candidate gene at 72 loci, including IL2RA, CTSH, and ICOSL, amongst others. We used network proximity analysis of these candidate genes to prioritize drugs predicted to be effective in PBC (z-score <-0.15); these included Golimumab, Briakinumab, and Guselkumab, each used for the treatment of IMIDs that are phenotypically associated with PBC.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".