Zinc Finger Protein 202, genetic variation, and HDL cholesterol in the general population
Bibliographic record
Abstract
Zinc Finger Protein 202 (ZNF202) is a transcriptional repressor that binds elements found predominantly in genes involved in HDL metabolism. We tested the following hypotheses: 1) frequencies of single-nucleotide polymorphisms (SNPs) and haplotypes in ZNF202 differ between individuals with low and high HDL cholesterol; and 2) SNPs in ZNF202 affect HDL cholesterol levels in the general population. We screened the promoter and protein-coding exons of ZNF202 in individuals with the highest 1% (n = 95) and lowest 1% (n = 95) HDL cholesterol among 9,259 Danish adults. None of the 10 SNPs identified differed in frequency as single sites or as haplotypes between low and high HDL cholesterol groups. In accordance with this, seven mutations were equally frequent (4-5%) in individuals with low or high HDL cholesterol. Finally, for all five SNPs identified in the coding region, we determined the association of genotype with HDL cholesterol in 9,259 individuals from the general population. Four SNPs were not associated with variation in HDL cholesterol, although c.*2T>G homozygosity was associated with a discrete effect on HDL cholesterol in men. We show that genetic variation in ZNF202 is common in the general population. However, SNPs in the protein-coding region of ZNF202 do not make a major contribution to HDL cholesterol levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".