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
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it