Abstract 16274: Identification of Novel CAD Genetic Loci by 1000 Genomes-Based Imputation and a Non-Additive Discovery Screen
Bibliographic record
Abstract
Introduction: Known common coronary artery disease (CAD) risk variants explain only 10% of the predicted genetic heritability of the disease, suggesting that important genetic signals remain to be discovered. Hypothesis: The 1000 Genomes imputation training set, and non-additive discovery screens, may allow detection of additional CAD-associated genetic variants that contribute to missing heritability. Methods: As part of the CARDIoGRAMplusC4D Consortium, we assembled 48 GWAS of CAD that included 1000 Genomes imputed data. These consisted of 60,801 CAD cases and 123,504 controls, 23% being of non-European ancestry. In each study, GWAS analysis was carried out by assuming an additive, dominant or recessive model of inheritance, and followed by meta-analysis to combine the GWAS results for each model. Results: After QC filtering, 9.4 million variants (91% SNPs, 9% INDELs) were available for meta-analysis. 29% of these were lower frequency variants (0.005 < MAF < 0.05). Novel associations (P < 5 x 10 -8 , and outside of known CAD genomic regions) under the additive model were detected for 38 variants (8% INDELs) with imputation info score of 0.94 [0.88-0.96] (median [IQR]). Of note, 34% of novel variants (N=13) were of low allele frequency (MAF < 0.05; median [IQR] = 0.03 [0.02-0.03]); these exhibited much larger effect sizes (P < 0.0001; Cohen’s d = 2.3) as compared to the common variants (MAF ≥ 0.05). The newly identified variants were mapped to 10 novel genomic loci for CAD. Together, these variants explained 2.5% of the heritability of CAD and majority (60%) were intronic. Three of these loci fit a dominant mode of inheritance. An additional two novel loci were identified by a recessive mode of inheritance. Among the newly identified loci, three had been previously reported at GWAS levels of significance for metabolic traits. Conclusions: These findings demonstrate the value of using a global imputation training set to enhance coverage of low allele frequency and incompletely tagged variants. Consideration of non-additive models of inheritance enabled identification of additional genetic variants associated with CAD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".