Genetic susceptibility to heart disease in Canada: lessons from patients with familial hypercholesterolemia
Bibliographic record
Abstract
Much of the recent progress in treating patients with heart disease due to narrowed coronary arteries has resulted from studying disease evolution in patients with rare monogenic forms of disease. For instance, autosomal dominant familial hypercholesterolemia (FH, MIM (Mendelian Inheritance in Man) 143890) typically results from heterozygous mutations in LDLR encoding the low-density lipoprotein (LDL) receptor. Deficient LDLR activity results in elevated circulating LDL cholesterol, which accumulates within blood vessel walls, forming arterial plaques that can grow and eventually occlude the arterial lumen. Heterozygous LDLR mutations are usually detected using exon-by-exon sequence analysis (EBESA) of genomic DNA, a technology that has identified approximately 50 mutations in heterozygous FH (HeFH) subjects in Ontario. However, approximately 35% of Ontario HeFH patients had no EBESA-identified LDLR mutation. The diagnostic gap relates both to the genetic heterogeneity of FH and also to inadequate sensitivity of EBESA to detect certain mutation types, such as large deletions or insertions in LDLR. By means of a dedicated method to detect copy number variations (CNVs), additional heterozygous mutations in LDLR ranging from approximately 500 to >15 000 bases were uncovered, accounting for most of the remainder of Ontario HeFH patients. The appreciation of the key role of genomic CNVs in disease coincides with recent genome-wide mapping studies demonstrating that CNVs are common in apparently healthy people. CNVs thus represent a new level of genomic variation that is both an important mechanism of monogenic disease and a contributor to genomic variation in the general population; as well, it may have implications for evolution, biology, and possibly susceptibility to common complex diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".