Identification of four novel genes contributing to familial elevated plasma HDL cholesterol in humans
Bibliographic record
Abstract
While genetic determinants strongly influence HDL cholesterol (HDLc) levels, most genetic causes underlying variation in HDLc remain unknown. We aimed to identify novel rare mutations with large effects in candidate genes contributing to extreme HDLc in humans, utilizing family-based Mendelian genetics. We performed next-generation sequencing of 456 candidate HDLc-regulating genes in 200 unrelated probands with extremely low (≤10th percentile) or high (≥90th percentile) HDLc. Probands were excluded if known mutations existed in the established HDLc-regulating genes ABCA1, APOA1, LCAT, cholesteryl ester transfer protein (CETP), endothelial lipase (LIPG), and UDP-N-acetyl-α-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 2 (GALNT2). We identified 93 novel coding or splice-site variants in 72 candidate genes. Each variant was genotyped in the proband's family. Family-based association analyses were performed for variants with sufficient power to detect significance at P < 0.05 with a total of 627 family members being assessed. Mutations in the genes glucokinase regulatory protein (GCKR), RNase L (RNASEL), leukocyte immunoglobulin-like receptor 3 (LILRA3), and dynein axonemal heavy chain 10 (DNAH10) segregated with elevated HDLc levels in families, while no mutations associated with low HDLc. Taken together, we have identified mutations in four novel genes that may play a role in regulating HDLc levels in humans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".