A Genome-Wide Association Study to Identify Single-Nucleotide Polymorphisms for Acute Kidney Injury
Bibliographic record
Abstract
Abstract Rationale Acute kidney injury is a common and severe complication of critical illness and cardiac surgery. Despite significant attempts at developing treatments, therapeutic advances to attenuate acute kidney injury and expedite recovery have largely failed. Objectives Identifying genetic loci associated with increased risk of acute kidney injury may reveal novel pathways for therapeutic development. Methods We conducted an exploratory genome-wide association study to identify single-nucleotide polymorphisms associated with genetic susceptibility to in-hospital acute kidney injury. Measurements and Main Results We genotyped 609,508 single-nucleotide polymorphisms and performed genotype imputation in 760 acute kidney injury cases and 669 controls. We then evaluated polymorphisms that showed the strongest association with acute kidney injury in a replication patient population containing 206 cases with 1,406 controls. We observed an association between acute kidney injury and four single-nucleotide polymorphisms at two independent loci on metaanalysis of discovery and replication populations. These include rs62341639 (metaanalysis P = 2.48 × 10−7; odds ratio [OR], 0.64; 95% confidence interval [CI], 0.55–0.76) and rs62341657 (P = 3.26 × 10−7; OR, 0.65; 95% CI, 0.55–0.76) on chromosome 4 near APOL1-regulator IRF2, and rs9617814 (metaanalysis P = 3.81 × 10−6; OR, 0.70; 95% CI, 0.60–0.81) and rs10854554 (P = 6.53 × 10−7; OR, 0.67; 95% CI, 0.57–0.79) on chromosome 22 near acute kidney injury–related gene TBX1. Conclusions Our findings reveal two genetic loci that are associated with acute kidney injury. Additional studies should be conducted to functionally evaluate these loci and to identify other common genetic variants contributing to acute kidney injury.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".