Identification of four novel genes contributing to familial elevated plasma HDL cholesterol in humans
Bibliographic record
Abstract
Heritability estimates of 47-76% for plasma HDL cholesterol (HDLc) levels suggest that genetic variation plays a pivotal role in HDL metabolism ( 1-3 ). However, despite major advances in family-and population-based association studies ( 2, 4 ), most genetic causes of extreme HDLc levels in humans remain unknown. Cohen et al. ( 5 ) reported mutations in the established HDLc genes ABCA1 , APOA1 , and LCAT in only 12.4% of individuals with low HDLc (<5th percentile). Similarly, we reported that mutations in the known HDLc-regulating genes ABCA1 , APOA1 , and LCAT were found in 28.7% of unrelated individuals with low HDLc levels ( 10th percentile) ( 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- -D-galactosamine:polypeptide N -acetylgalactosaminyltransferase 2 ( GALNT2 ). We identifi ed 93 novel coding or splice-site variants in 72 candidate genes. Each variant was genotyped in the proband's family. Familybased association analyses were performed for variants with suffi cient power to detect signifi cance 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 identifi ed mutations in four novel genes that may play a role in regulating HDLc levels in humans. -Singaraja, R.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".