Pooled DNA Resequencing of 68 Myocardial Infarction Candidate Genes in French Canadians
Bibliographic record
Abstract
BACKGROUND: Familial history is a strong risk factor for coronary artery disease (CAD), especially for early-onset myocardial infarction (MI). Several genes and chromosomal regions have been implicated in the genetic cause of coronary artery disease/MI, mostly through the discovery of familial mutations implicated in hyper-/hypocholesterolemia by linkage studies and single nucleotide polymorphisms by genome-wide association studies. Except for a few examples (eg, PCSK9), the role of low-frequency genetic variation (minor allele frequency [MAF]) ≈0.1%-5% on MI/coronary artery disease predisposition has not been extensively investigated. METHODS AND RESULTS: We selected 68 candidate genes and sequenced their exons (394 kb) in 500 early-onset MI cases and 500 matched controls, all of French-Canadian ancestry, using solution-based capture in pools of nonindexed DNA samples. In these regions, we identified 1852 single nucleotide variants (695 novel) and captured 85% of the variants with MAF≥1% found by the 1000 Genomes Project in Europe-ancestry individuals. Using gene-based association testing, we prioritized for follow-up 29 low-frequency variants in 8 genes and attempted to genotype them for replication in 1594 MI cases and 2988 controls from 2 French-Canadian panels. Our pilot association analysis of low-frequency variants in 68 candidate genes did not identify genes with large effect on MI risk in French Canadians. CONCLUSIONS: We have optimized a strategy, applicable to all complex diseases and traits, to discover efficiently and cost-effectively DNA sequence variants in large populations. Resequencing endeavors to find low-frequency variants implicated in common human diseases are likely to require very large sample size.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".