Polygenic determinants of severe hypertriglyceridemia
Bibliographic record
Abstract
Recent genome-wide association (GWA) studies have identified new genetic determinants of complex quantitative traits, including plasma triglyceride (TG). We hypothesized that common variants associated with mild TG variation identified in GWA studies would also be associated with severe hypertriglyceridemia (HTG). We studied 132 patients of European ancestry with severe HTG (fasting plasma TG > 10 mmol/l), who had no mutations found by resequencing of candidate genes, and 351 matched normolipidemic controls. We determined genotypes for: GALNT2 rs4846914, TBL2/MLXIPL rs17145738, TRIB1 rs17321515, ANGPTL3 rs12130333, GCKR rs780094, APOA5 rs3135506 (S19W), APOA5 rs662799 (-1131T > C), APOE (isoforms) and LPL rs328 (S447X). We found that: (i) genotypes, including those of APOA5 S19W, APOA5 -1131T > C, APOE, GCKR, TRIB1 and TBL2/MLXIPL, were significantly associated with severe HTG; (ii) odds ratios for these genetic variables were significant in both univariate and multivariate regression analyses, irrespective of the presence or absence of diabetes or obesity; (iii) a significant fraction-about one-quarter-of the explained variation in disease status was associated with these genotypes. Therefore, common SNPs (single nucleotide polymorphisms) that are associated with mild TG variation in GWA studies of normolipidemic subjects are also associated with severe HTG. Our findings are consistent with the emerging model of a complex genetic trait. At the extremes of a quantitative trait, such as severe HTG, are found the cumulative contributions of both multiple rare alleles with large genetic effects and common alleles with small effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".