11 Genome-wide association study using repeated measures model for stillbirth in Holstein dairy cattle
Bibliographic record
Abstract
Abstract Reduced fertility is one of the main causes of economic losses in dairy farms. The financial losses are estimated in US$938 per stillbirth case in Holstein herds. The identification of genomic regions associated with stillbirth could help to develop better management and breeding strategies aiming to reduce the frequency of undesirable gestation outcomes. The same cow may show different outcomes in different gestations. Therefore, evaluation of a single time-point may provide a biased indication of the genetic causes of stillbirth. A weighted single-step genomic best linear unbiased prediction (WssGBLUP) was performed using the BLUPF90 software. A total of 14,145 cows (with three or more delivery records) were genotyped for 42,909 SNP markers mapped against the ARS-UCD1.2 bovine reference genome. Three rounds of WssGBLUP were performed using 50,541 stillbirth records (4.8±1.3 records per animal). In total, 5,796 stillbirth cases were observed out of the 50,541 records (~11%). The statistical model included: calf size, calf sex, and dam age class (< 36, 36–50, 50–63, 63–75, ³75 months) as fixed effects; and herd-year-season, cow additive genetic merit, cow permanent environment, and service sire as random effects. Nine windows explaining more than one percent of the total genetic variance were identified, where the top 3 windows explained 7.86% (BTA13:37558814-38550118), 4.92% (BTA18:59897080-60892357) and 2.17% (BTA12:80568320-81508005). The top candidate window, on BTA13, harbors the OVOL2 gene, which codifies a zinc-finger protein crucial for embryo development during the angiogenesis, heart formation, hematopoietic, endothelial, neural tube and placental development. Additionally, OVOL2 is crucial during the spermatogenesis and was already identified as down-regulated between ectopic and eutopic endometrial tissues in woman with endometriosis. These results pinpoint new genomic candidate regions for stillbirth in Holstein, helping to better understand the genetic mechanisms associated with fertility problems.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".