No large-effect low-frequency coding variation found for myocardial infarction
Bibliographic record
Abstract
Genome-wide association studies have identified variants, primarily common, that are associated with coronary artery disease or myocardial infarction (MI), but have not tested the majority of the low frequency and rare variation in the genome. We explored the hypothesis that previously untested low frequency (1-5% minor allele frequency) and rare (<1% minor allele frequency) coding variants are associated with MI. We genotyped 2906 MI cases and 6738 non-MI controls from Norway using the Illumina HumanExome Beadchip, allowing for direct genotyping of 85 972 polymorphic coding variants as well as 48 known GWAS SNPs. We followed-up 34 coding variants in an additional 2350 MI cases and 2318 controls from Norway. We evaluated exome array coverage in a subset of these samples using whole exome sequencing (N = 151). The exome array provided successful genotyping for an estimated 72.5% of Norwegian loss-of-function or missense variants with frequency >1% and 66.2% of variants <1% frequency observed more than once. Despite 80% power in the two-stage study (N = 14 312) to detect association with low-frequency variants with high effect sizes [odds ratio (OR) >1.86 and >1.36 for 1 and 5% frequency, respectively], we did not identify any novel genes or single variants that reached significance. This suggests that low-frequency coding variants with large effect sizes (OR >2) may not exist for MI. Larger sample sizes may identify coding variants with more moderate effects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".