Abstract 599: Elucidating the Genetic Determinants of Extreme High-density Lipoprotein Phenotypes Using Next-generation Sequencing
Bibliographic record
Abstract
HDL cholesterol (HDL-C) levels strongly associate with cardiovascular disease risk, and as a complex trait, are influenced by genetic and environmental factors. Extreme HDL-C concentrations are largely genetically determined; monogenic disorders of HDL-C have been well-characterized, including the primary candidate genes driving each extreme phenotype, which typically show autosomal recessive or co-dominant inheritance. Within genes causing syndromes of both HDL-C extremes, numerous disease-causing variants have been identified and functionally validated. In a unique cohort of patients with extreme HDL-C profiles ( N =255), we applied our targeted next-generation sequencing panel LipidSeq TM , which is designed for clinical re-sequencing of genes associated with dyslipidemia and other metabolic disorders. We found that 20.6% and 11.8% of low ( N =136) and high ( N =119) HDL-C patients, respectively, carry heterozygous, large-effect mutations in pertinent genes explaining their phenotypes. To further characterize the genetic variation contributing to HDL-C levels, we next investigated the integrated polygenic contribution from multiple small-effect genetic variants using a polygenic trait score (PTS). We developed two scores to assess an individual’s burden of small-effect variants: one each for lowering and raising HDL-C levels. As a whole, low HDL-C patients had a significantly greater mean PTS for low HDL-C than normolipidemic controls ( P <0.001); furthermore, there was no difference in PTS among carriers and non-carriers of large-effect variants. In contrast, high HDL-C patients’ mean PTS for high HDL-C in carriers of large-effect variants was not different from non-carrier or controls, while PTS in non-carriers was significantly greater than controls (both P <0.001). The findings confirm the complexity of extreme HDL-C levels and the differences in contributions of rare large-effect and common small-effect variants to these extremes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".