Genome-Wide Sequencing Identified Rare Genetic Variants for Childhood-Onset Monogenic Lupus
Bibliographic record
Abstract
OBJECTIVE: Genetics play an important role in systemic lupus erythematosus (SLE) pathogenesis. We calculated the prevalence of rare variants in known monogenic lupus genes among children suspected of monogenic lupus. METHODS: We completed paired-end genome-wide sequencing (whole genome sequencing [WGS] or whole exome sequencing) in patients suspected of monogenic lupus, and focused on 36 monogenic lupus genes. We prioritized rare (minor allele frequency < 1%) exonic, nonsynonymous, and splice variants with predicted pathogenicity classified as deleterious variants (Combined Annotation Dependent Depletion [CADD], PolyPhen2, and Sorting Intolerant From Tolerant [SIFT] scores). Additional filtering restricted to predicted damaging variants by considering reported zygosity. In those with WGS (n = 69), we examined copy number variants (CNVs) > 1 kb in size. We created additive non-HLA and HLA SLE genetic risk scores (GRSs) using common SLE-risk single-nucleotide polymorphisms. We tested the relationship between SLE GRSs and the number of rare variants with multivariate logistic models, adjusted for sex, ancestry, and age of diagnosis. RESULTS: = 0.01). We did not identify damaging CNVs. There was no significant association between non-HLA or HLA SLE GRSs and the odds of carrying ≥ 1 rare variant in multivariate analyses. CONCLUSION: In a cohort of patients with suspected monogenic lupus who underwent genome-wide sequencing, 13% carried rare predicted damaging variants for monogenic lupus. Additional studies are needed to validate our findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".