Identification of novel susceptibility genes in childhood‐onset systemic lupus erythematosus using a uniquely designed candidate gene pathway platform
Bibliographic record
Abstract
OBJECTIVE: Childhood-onset systemic lupus erythematosus (SLE) presents a unique subgroup of patients for genetic study. The present study was undertaken to identify susceptibility genes contributing to SLE, using a novel candidate gene pathway microarray platform to investigate gene expression in patients with childhood-onset SLE and both of their parents. METHODS: Utilizing bioinformatic tools, a platform of 9,412 single-nucleotide polymorphisms (SNPs) from 1,204 genes was designed and validated. Molecular inversion probes and high-throughput SNP technologies were used for assay development. Seven hundred fifty three subjects, corresponding to 251 full trios of childhood-onset SLE families, were genotyped and analyzed using transmission disequilibrium testing (TDT) and multitest corrections. RESULTS: Family-based TDT showed a significant association of SLE with a N673S polymorphism in the P-selectin gene (SELP) (P = 5.74 x 10(-6)) and a C203S polymorphism in the interleukin-1 receptor-associated kinase 1 gene (IRAK1) (P = 9.58 x 10(-6)). These 2 SNPs had a false discovery rate for multitest correction of <0.05, and therefore a >95% probability of being considered as proven. Furthermore, 7 additional SNPs showed q values of <0.5, suggesting association with SLE and providing a direction for followup studies. These additional genes notably included TNFRSF6 (Fas) and IRF5, supporting previous findings of their association with SLE pathogenesis. CONCLUSION: SELP and IRAK1 were identified as novel SLE-associated genes with a high degree of significance, suggesting new directions in understanding the pathogenesis of SLE. The overall design and results of this study demonstrate that the candidate gene pathway microarray platform used provides a novel and powerful approach that is generally applicable in identifying genetic foundations of complex diseases.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".