Polygenic Versus Monogenic Causes of Hypercholesterolemia Ascertained Clinically
Bibliographic record
Abstract
OBJECTIVE: Next-generation sequencing technology is transforming our understanding of heterozygous familial hypercholesterolemia, including revision of prevalence estimates and attribution of polygenic effects. Here, we examined the contributions of monogenic and polygenic factors in patients with severe hypercholesterolemia referred to a specialty clinic. APPROACH AND RESULTS: We applied targeted next-generation sequencing with custom annotation, coupled with evaluation of large-scale copy number variation and polygenic scores for raised low-density lipoprotein cholesterol in a cohort of 313 individuals with severe hypercholesterolemia, defined as low-density lipoprotein cholesterol >5.0 mmol/L (>194 mg/dL). We found that (1) monogenic familial hypercholesterolemia-causing mutations detected by targeted next-generation sequencing were present in 47.3% of individuals; (2) the percentage of individuals with monogenic mutations increased to 53.7% when copy number variations were included; (3) the percentage further increased to 67.1% when individuals with extreme polygenic scores were included; and (4) the percentage of individuals with an identified genetic component increased from 57.0% to 92.0% as low-density lipoprotein cholesterol level increased from 5.0 to >8.0 mmol/L (194 to >310 mg/dL). CONCLUSIONS: In a clinically ascertained sample with severe hypercholesterolemia, we found that most patients had a discrete genetic basis detected using a comprehensive screening approach that includes targeted next-generation sequencing, an assay for copy number variations, and polygenic trait scores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".