Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk
Bibliographic record
Abstract
High blood cholesterol concentration, mainly caused by high dietary cholesterol, is a potential risk factor for human health. Dairy products are important sources of human dietary cholesterol intake. Therefore, monitoring bovine milk cholesterol concentration is important for human health benefit. Genetic selection for improvement of cow milk cholesterol content requires understanding of the genetics of milk cholesterol. For this purpose, we performed analyses of additive and dominance effects of 126 potentially functional SNPs within 43 candidate genes with milk cholesterol content [expressed as mg of cholesterol in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk)]. The additive and dominance effects of SNPs rs380643365 in AGPAT1 (P = 0.04) and rs134357240 in SOAT1 (P = 0.035) genes associated significantly with CHL_fat. Moreover, five (rs109326954 and rs523413537 in DGAT1, rs109376747 in LDLR, rs42781651 in FAM198B and rs109967779 in ACAT2) and four (rs137347384 in RBM19, rs109376747 in LDLR, rs42016945 in PPARG and rs110862179 in SCAP) SNPs were significantly associated with CHL_milk (P < 0.05) based on additive and dominance effect analyses respectively. Rs109326954 and rs523413537 in DGAT1 explained a considerable portion of the phenotypic variance of CHL_milk (7.54 and 6.84% respectively), and might be useful in selection programs for reduced milk cholesterol content. Several significantly associated SNPs were in genes (such as ACAT2 and LDLR) involved in cholesterol metabolism in the liver or cholesterol transport, suggesting multiple mechanisms regulating milk cholesterol content. Nine and seven SNPs identified by additive or dominance effect analyses associated significantly with milk yield and fat yield respectively. Further analyses are required to better understand the consequences of these variants and their potential use in genomic selection of the studied traits.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".