Global Analysis of genetic variants associated with cardiovascular disease and related metabolic traits
Bibliographic record
Abstract
Coronary heart disease (CHD) is the leading cause of morbidity and mortality in the western world. CHD is common throughout the world and is multifactorial, caused by the accumulation or interaction of quantitative changes in various intermediate traits (risk factors or metabolic phenotypes). Intermediate traits that are commonly studied include plasma levels of cholesterol (ie. low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and total cholesterol), body mass index (BMI) and blood pressure, which are all believed to be influenced by a combination of genetic and environmental factors (such as diet, alcohol, and exercise). In this thesis, the identification of novel genetic variants in candidate genes (a non-synonymous variant in farnesyl-diphosphate farnesyltransferase 1 (FDFT1) and a non-coding variant in insulin-induced gene 2 protein (INSIG2)) that are associated with total cholesterol and low density lipoprotein cholesterol is described. In addition, detailed investigation of common genetic variants in known genes identified from genome-wide association studies in the context of other genetic, phenotypic and environmental factors are reported. In particular, INSIG2 variants were observed to act in concert with a trans-acting variant in the sorbin and SH3 domain containing 1 gene (SORBS1) to influence LDL-C and apoB levels in Quebec, European and South Asian population samples. In addtition, fat mass and obesity associated gene (FTO) variants were observed to influence adiposity-related traits, resting metabolic rate and plasma leptin levels. I also report the role of dietary intake in modifying the effect of 9p21 variants on myocardial infarction and cardiovascular disease in the multi-ethnic INTERHEART study and in a Finnish sample, “FINRISK”. Finally, the utility of exome sequencing in identifying the genetic cause of a mendelian lipid disorder is demonstrated in a study that identifies compound heterozygo
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".