Exome Sequencing Identifies 2 Rare Variants for Low High-Density Lipoprotein Cholesterol in an Extended Family
Bibliographic record
Abstract
BACKGROUND: Exome sequencing is a recently implemented method to discover rare mutations for Mendelian disorders. Less is known about its feasibility to identify genes for complex traits. We used exome sequencing to search for rare variants responsible for a complex trait, low levels of serum high-density lipoprotein cholesterol (HDL-C). METHODS AND RESULTS: We conducted exome sequencing in a large French-Canadian family with 75 subjects available for study, of which 27 had HDL-C values less than the fifth age-sex-specific population percentile. We captured ≈50 Mb of exonic and transcribed sequences of 3 closely related family members with HDL-C levels less than the fifth age-sex percentile and sequenced the captured DNA. Approximately 82,000 variants were detected in each individual, of which 41 rare nonsynonymous variants were shared by the sequenced affected individuals after filtering steps. Two rare nonsynonymous variants in the ATP-binding cassette, subfamily A (ABC1), member 1 (ABCA1), and lipoprotein lipase genes predicted to be damaging were investigated for cosegregation with the low HDL-C trait in the entire extended family. The carriers of either variant had low HDL-C levels, and the individuals carrying both variants had the lowest HDL-C values. Interestingly, the ABCA1 variant exhibited a sex effect which was first functionally identified, and, subsequently, statistically demonstrated using additional French-Canadian families with ABCA1 mutations. CONCLUSIONS: This complex combination of 2 rare variants causing low HDL-C in the extended family would not have been identified using traditional linkage analysis, emphasizing the need for exome sequencing of complex lipid traits in unexplained familial cases.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".