Conditional analysis identifies three novel major histocompatibility complex loci associated with psoriasis
Bibliographic record
Abstract
Psoriasis is a common, chronic, inflammatory skin disorder. A number of genetic loci have been shown to confer risk for psoriasis. Collectively, these offer an integrated model for the inherited basis for susceptibility to psoriasis that combines altered skin barrier function together with the dysregulation of innate immune pathogen sensing and adap-tive immunity. The major histocompatibility complex (MHC) harbours the psoriasis susceptibility region which exhibits the largest effect size, driven in part by variation contained on the HLA-Cw*0602 allele. However, the resolution of the number and genomic location of potential independent risk loci are hampered by extensive linkage disequilibrium across the region. We leveraged the power of large psoriasis case and control data sets and the statistical approach of conditional analysis to identify potential further association signals distributed across the MHC. In addition to the major loci at HLA-C (P = 2.20 × 10(-236)), we observed and replicated four additional independent signals for disease association, three of which are novel. We detected evidence for association at SNPs rs2507971 (P = 6.73 × 10(-14)), rs9260313 (P = 7.93 × 10(-09)), rs66609536 (P = 3.54 × 10(-07)) and rs380924 (P = 6.24 × 10(-06)), located within the class I region of the MHC, with each observation replicated in an independent sample (P ≤ 0.01). The previously identified locus is close to MICA, the other three lie near MICB, HLA-A and HCG9 (a non-coding RNA gene). The identification of disease associations with both MICA and MICB is particularly intriguing, since each encodes an MHC class I-related protein with potent immunological function.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".