Comprehensive Analysis of Genomic Variation in the <i>LPA</i> Locus and Its Relationship to Plasma Lipoprotein(a) in South Asians, Chinese, and European Caucasians
Bibliographic record
Abstract
BACKGROUND: Functional copy number variation in the apolipoprotein(a) gene (LPA) underlies a variable number of protein kringle domains repeated in tandem in the lipoprotein(a) [Lp(a)] particle. Genomic analysis of LPA, including both single-nucleotide polymorphisms (SNPs) and kringle IV type 2 (KIV-2) copy number, has yet to be performed. METHODS AND RESULTS: First, we genotyped 49 SNPs within 100 kb of LPA in a multiethnic sample comprising South Asians (n=330), Chinese (n=304), and European Caucasians (n=272). Second, using quantitative polymerase chain reaction, we estimated the KIV-2 copy number in each sample. European Caucasians had the lowest KIV-2 copy number but displayed the strongest correlation between KIV-2 copy number and plasma Lp(a) concentration (r(s)=-0.31, P=4.2 x 10(-7)). SNP rs10455872, only prevalent in European Caucasians, was strongly associated with both plasma Lp(a) concentration (P=4.2 x 10(-29)) and KIV-2 copy number (P=7.2 x 10(-5)). LPA SNP rs6415084, within the same haplotype block as the KIV-2 variation, was significantly associated with both Lp(a) concentration and KIV-2 copy number in the same direction in all 3 ethnicities [Lp(a), P=5.3 x 10(-7); KIV-2, P=2.6 x 10(-4)]. SNPs and KIV-2 copy number together explain a larger proportion of variation in plasma Lp(a) concentrations in European Caucasians (36%) than in Chinese (27%) or South Asians (21%). CONCLUSIONS: LPA SNPs are in linkage disequilibrium with KIV-2 copy number, but KIV-2 copy number explains an increment in plasma Lp(a) variation over SNPs alone. Thus, both SNPs and KIV-2 copy number should be included in future genetic epidemiology studies of Lp(a).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".