Excess of Rare Variants in Non–Genome-Wide Association Study Candidate Genes in Patients With Hypertriglyceridemia
Bibliographic record
Abstract
BACKGROUND: Rare variant accumulation studies can implicate genes in disease susceptibility when a significant burden is observed in patients versus control subjects. Such analyses might be particularly useful for candidate genes that are selected based on experiments other than genome-wide association studies (GWAS). We sought to determine whether rare variants in non-GWAS candidate genes identified from mouse models and human mendelian syndromes of hypertriglyceridemia (HTG) accumulate in patients with polygenic adult-onset HTG. METHODS AND RESULTS: We resequenced protein coding regions of 3 genes with established roles (APOC2, GPIHBP1, LMF1) and 2 genes recently implicated (CREB3L3 and ZHX3) in TG metabolism. We identified 41 distinct heterozygous rare variants, including 29 singleton variants, in the combined sample; in total, we observed 47 rare variants in 413 HTG patients versus 16 in 324 control subjects (odds ratio=2.3; P=0.0050). Post hoc assessment of genetic burden in individual genes using 3 different tests suggested that the genetic burden was most prominent in the established genes LMF1 and APOC2, and also in the recently identified CREB3L3 gene. CONCLUSIONS: These extensive resequencing studies show a significant accumulation of rare genetic variants in non-GWAS candidate genes among patients with polygenic HTG, and indicate the importance of testing specific hypotheses in large-scale resequencing studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".