Abstract 18244: A Multi-Ancestry GWAS of Calcific Aortic Stenosis Among 2.7 Million Individuals
Bibliographic record
Abstract
Introduction: Calcific aortic stenosis (CAS) is a common, progressive fibrocalcific pathology of the aortic valve without medical therapy. The genetics of CAS remain only partially understood. Methods: We performed a genome wide association study (GWAS) of CAS among 2,799,598 individuals from the International Aortic Valve Genetics Consortium (IAVGC), comprising 28 cohorts. CAS was identified using a common ICD/CPT based phenotype. GWAS were meta-analyzed using inverse variance weighting with adjustment by linkage-disequilibrium score regression (LDSR) intercept. Unique genome-wide significant (GWS) loci and causal genes were annotated by nearest gene and eQTL colocalization. Genetic correlations were performed against atherosclerotic, adiposity, and lipid traits using LDSR with publicly available GWAS (CARDIoGRAMplusC4D for coronary artery disease [CAD], Million Veteran Program for peripheral artery disease [PAD], MEGASTROKE for ischemic stroke [IS], GIANT for body mass index [BMI], and GLGC for lipids). Results: There were 85,329 individuals with CAS (79,397 White, 3,126 Black, 1,403 Hispanic, and 1,403 East Asian) among 2,799,598 individuals. Meta-analysis of GWAS resulted in 224 unique GWS genomic regions, of which 205 were novel. The majority of the GWS genomic regions (134) did not overlap with prior risk loci for CAD, PAD, IS, BMI, or lipids. Genetic correlation demonstrated modest but significant correlations between CAS and CAD ( r =0.26, p=3.3x10 -18 ), PAD ( r =0.41, p=1.4x10 -30 ), IS ( r =0.18,p=2.8x10 -7 ), BMI ( r =0.22,p=8.6x10 -30 ), and lipids (LDL-C r =0.17,p=3.6x10 -10 ;triglycerides r =0.10,p=1.9x10 -5 ; HDL-C r =-0.07,p=8.0x10 -4 ). Conclusions: This largest to-date multi-ancestry GWAS of CAS identified 205 novel genomic regions. We demonstrate that CAS is genetically distinct from cardiometabolic traits, with only modest genetic correlations and with a majority of CAS risk loci having no overlap with cardiometabolic GWAS risk loci.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.005 | 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".