Common risk variants in <i>AHI1</i> are associated with childhood steroid-sensitive nephrotic syndrome
Bibliographic record
Abstract
ABSTRACT Background Steroid-sensitive nephrotic syndrome (SSNS) is the most common form of kidney disease in children worldwide. Genome-wide association studies (GWAS) have demonstrated association of SSNS with genetic variation at HLA-DQ/DR and have identified several non- HLA loci that aid in further understanding of disease pathophysiology. We sought to identify additional genetic loci associated with SSNS in children of Sri Lankan and European ancestry. Methods We conducted a GWAS in a cohort of Sri Lankan individuals comprising 420 pediatric patients with SSNS and 2339 genetic ancestry matched controls obtained from the UK Biobank. We then performed a trans-ethnic meta-analysis with a previously reported European cohort of 422 pediatric patients and 5642 controls. Results Our GWAS confirmed the previously reported association of SSNS with HLA-DR/DQ (rs9271602, p=1.12×10 −27 , odds ratio[OR]=2.75). Trans-ethnic meta-analysis replicated these findings and identified a novel association at AHI1 (rs2746432, p=2.79×10 −8 , OR=1.37), which was also replicated in an independent South Asian cohort. AHI1 is implicated in ciliary protein transport and immune dysregulation, with rare variation in this gene contributing to Joubert syndrome type 3. Conclusions Common variation in AHI1 confers risk of the development of SSNS in both Sri Lankan and European populations. The association with common variation in AHI1 further supports the role of immune dysregulation in the pathogenesis of SSNS and demonstrates that variation across the allele frequency spectrum in a gene can contribute to disparate monogenic and polygenic diseases. AUTHOR SUMMARY Steroid-sensitive nephrotic syndrome (SSNS) is the most common kidney disease in children worldwide, but the cause of disease is not well understood. Genome-wide association studies (GWAS) in SSNS have shown that genes in the classical HLA region (the human immune centre) and several genes outside of this region are associated with the disease, which has allowed us to further understand the cause of disease. We performed a GWAS of Sri Lankan ancestry that included 420 paediatric patients and 2339 ancestry-matched controls and confirmed association at HLA-DQ/DR with SSNS. We then performed a Sri Lankan-European trans-ethnic meta-analysis and identified a new association with SSNS outside of HLA, in AHI1 . This finding further supports the role of immune system involvement in the etiology of SSNS and increases our knowledge of the genetic causes of disease. AHI1 is a gene that can also cause ciliary problems and demonstrates that different genetic variants within the same gene can contribute to both single-gene (Joubert syndrome, a rare disease that causes kidney and neurological problems) and multi-gene diseases (SSNS).
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".