The Genetics of Psoriasis and Psoriatic Arthritis
Bibliographic record
Abstract
Psoriatic arthritis (PsA) is an inflammatory arthritis that manifests in 20–30% of patients diagnosed with psoriasis. Epidemiologic studies suggest a substantial genetic contribution to PsA. There is a strong need for genome-wide association studies on patients with PsA, including PsA-weighted or specific variants, and a need for a better understanding of the relevance of HLA alleles in disease expression. Interferon signaling and the nuclear factor-κB cascade are involved in PsA, and there are genetic differences between purely cutaneous psoriasis (PsC) and PsA. Psoriasis susceptibility genes for which putative functional coding variants in TYK2 and TRAF3IP2 are strongly associated with PsC and PsA, and neutrophil extracellular traps promote Th17 induction in an Act1 D10N-dependent fashion. Genomics and serological factors may also predict treatment response in tumor necrosis factor inhibitors (TNFi) in PsA, and genetics may play a role in treatment response to TNFi. Collaborations through the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) are essential to increase study population size, which will enhance the ability to detect the genetic variants that create a predisposition to psoriatic disease and to predict response to biological therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".