GWAS defines pathogenic signaling pathways and prioritizes drug targets for IgA nephropathy
Bibliographic record
Abstract
ABSTRACT IgA nephropathy (IgAN) is a progressive form of kidney disease defined by glomerular deposition of IgA. We performed a genome-wide association study involving 10,146 kidney biopsy-diagnosed IgAN cases and 28,751 matched controls across 17 international cohorts. We defined 30 independent genome-wide significant risk loci jointly explaining 11% of disease risk. A total of 16 loci were novel, including TNFSF4, REL, CD28, CXCL8/PF4V1, LY86, LYN, ANXA3, TNFSF8/15, REEP3, ZMIZ1, RELA, ETS1, IGH, IRF8, TNFRSF13B and FCAR . The SNP-based heritability of IgAN was estimated at 23%. We observed a positive genetic correlation between IgAN and total serum IgA levels, allergy, tonsillectomy, and several infections, and a negative correlation with inflammatory bowel disease. All significant non-HLA loci shared with serum IgA levels had a concordant effect on the risk of IgAN. Moreover, IgAN loci were globally enriched in gene orthologs causing abnormal IgA levels when genetically manipulated in mice. The explained heritability was enriched in the regulatory elements of cells from the immune and hematopoietic systems and intestinal mucosa, providing support for the pathogenic role of extra-renal tissues. The polygenic risk of IgAN was associated with early disease onset, increased lifetime risk of kidney failure, as well as hematuria and several other traits in a phenome-wide association study of 590,515 individuals. In the comprehensive functional annotation analysis of candidate causal genes across genome-wide significant loci, we observed the convergence of biological candidates on a common set of inflammatory signaling pathways and cytokine ligand-receptor pairs, prioritizing potential new drug targets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".