Identified genetic locus for longitudinal disease activity in adults with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Genetics significantly impacts systemic lupus erytematosus (SLE) risk, disease manifestations, and damage. Our aim was to identify genetic risk loci for disease activity burden over time. METHODS: We included participants from a tertiary care lupus clinic. Participants met ACR and/or SLICC classification criteria for SLE, were genotyped on one of three arrays and had ≥3 measures of disease activity [SLEDAI 2000 (SLEDAI-2K)] to derive adjusted mean SLEDAI-2K and glucocorticoid (AMSG) scores. We completed a genome-wide association study (GWAS) of AMSG, adjusted for sex and five PCs, and stratified by array, then meta-analysed GWAS (P < 5 × 10-8). Meta-GWAS results were used in colocalization analyses with expression quantitative trait loci in multiple tissues. In a subset of patients, we examined the association between the top single nucleotide polymorphism (SNP) for AMSG and interferon-stimulated gene expression. RESULTS: The cohort included 538 individuals with SLE (88% female), with a median age at diagnosis of 30.7 years (interquartile range = 23.3, 41.7 years). Most patients (75%) had a first clinic visit within 1 year of SLE diagnosis and were followed for a mean of 4.5 years (SD = 0.95). The median AMSG was 5.5 (Q25, Q75 = 3.2, 8.8). Meta-GWAS identified a genome-wide significant SNP for AMSG (rs4561613) on chromosome 2, intronic to AGAP1 (Beta = 0.34, SE = 0.06, P = 4.16 × 10-9). Colocalization analysis did not identify a significant difference in gene expression for the top SNP. Interferon gene scores were significantly associated with AMSG (Beta = 0.02, SE = 8.70 × 10-3, P = 0.006). CONCLUSION: We identified a genome-wide significant locus intronic to AGAP1 for SLE disease activity burden as measured by AMSG.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".