Common and Rare <i>ABCA1</i> Variants Affecting Plasma HDL Cholesterol
Bibliographic record
Abstract
Mutations in ABCA1, a member of the ATP-binding cassette family, have been shown to underlie Tangier disease (TD) and familial hypoalphalipoproteinemia (FHA), which are genetic disorders that are characterized by depressed concentrations of plasma high density lipoprotein (HDL) cholesterol. An important question is whether common variants within the coding sequence of ABCA1 can affect plasma HDL cholesterol in the general population. To address this issue, we developed a screening strategy to find common ABCA1 variants. This strategy involved long-range amplification of genomic DNA by using coding sequences only, followed by deep sequencing into the introns. This method helped us to characterize a new set of amplification primers, which permitted amplification of virtually all of the coding sequence of ABCA1 and its intron-exon boundaries with a single DNA amplification program. With these new sequencing primers, we found 3 novel ABCA1 mutations: a frameshift mutation (4570insA, A1484S-->X1492), a missense mutation (A986D) in a TD family, and a missense mutation (R170C) in aboriginal subjects with FHA. We also used these sequencing primers to characterize 4 novel common amino acid variants in ABCA1, in addition to 5 novel common silent variants. We tested for association of the ABCA1 I/M823 variant with plasma HDL cholesterol in Canadian Inuit and found that M823/M823 homozygotes had significantly higher plasma HDL cholesterol compared with subjects with the other genotypes. The results provide proof of principle of the effectiveness of this approach to identify both rare and common ABCA1 genomic variants and also suggest that common amino acid variation in ABCA1 is a determinant of plasma HDL cholesterol in the general population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".