IL5RA and TNFRSF6B Gene Variants Are Associated With Sporadic IgA Nephropathy
Bibliographic record
Abstract
Familial clustering and genome-wide linkage scans strongly support a genetic susceptibility to familial IgA nephropathy (IgAN), but genetic factors that predispose to sporadic IgAN are unknown. A high-throughput single nucleotide polymorphism (SNP) association study was conducted using a customized Illumina BeadChip in 732 white patients with biopsy-proven IgAN and 503 control subjects from Canada, France, and Finland. Approximately 93% of 1536 SNPs on the array were tag SNPs from Phase I+II of the HapMap with a minor allele frequency > or =5%, designed to capture the common variants of genes within the critical interval of IGAN1 on chromosome 6q22 and 69 biologic candidate genes for IgAN. SNPs of suggestive or significant association were identified by using logistic regression to adjust for age, gender, study site, and population stratification. Despite using a dense marker set that covered an average interval of 6.5 kb between SNPs, there was no strong and consistent association signal within the IGAN1 critical interval. Among the biologic candidate genes examined, two significant association signals were found at IL5RA and TNFRSF6B, the latter being particularly interesting because this gene encodes a decoy receptor for a TNF family ligand that causes IgAN in mice when overexpressed. Pending replication, these data suggest that variants of IL5RA and TNFRSF6B may predispose to sporadic IgAN.
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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 | 0.001 |
| 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 | 0.001 | 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".