Determination of lipoprotein(a) kringle repeat number from genomic DNA: copy number variation genotyping using qPCR
Bibliographic record
Abstract
Plasma lipoprotein(a) [Lp(a)] concentration is related to risk of cardiovascular disease. The defining protein component of Lp(a) particles, apolipoprotein(a) [apo(a)], is encoded by the LPA gene. Apo(a) is extremely heterogeneous in size due to a common copy number variation, leading to a variable number of kringle-IV type 2 (KIV2)-like domains. Alleles with fewer KIV2 repeats, encoding smaller apo(a) isoforms, are associated with higher plasma Lp(a) concentrations. Two principal methods to detect variation in KIV2 repeat number are electrophoresis with immunoblotting to detect apo(a) protein isoforms or pulse-field electrophoresis of unamplified genomic DNA to detect the variation of the LPA gene. Both methods are technically challenging, laborious, and time consuming. Here, we report a rapid method to determine the number of KIV2 repeats in LPA from genomic DNA using quantitative real-time polymerase chain reaction (qPCR). With qPCR, we found KIV2 repeat number was correlated with both apo(a) isoform size as determined by immunoblotting (r(s) = 0.50, P < 1 x 10(-6)) and with plasma Lp(a) concentration (r(s) = 0.30, P < 1 x 10(-6)). The qPCR technique permits rapid evaluation of apo(a) size from genomic DNA, and thus would provide an adjunctive genomic variable, in addition to LPA single nucleotide polymorphisms, for evaluating the genetic determinants of plasma Lp(a) concentration in genetic epidemiology studies of cardiovascular disease outcomes.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".