Genome-Wide Association Study for the Capacity to Skip the Dry Period in Dairy Goats
Bibliographic record
Abstract
Lactation is a challenging life stage for dairy animals, as they need to cope with milk production and, in most cases, simultaneous pregnancy. The dry period between two consecutive lactations can be a producer choice, based on, for instance, animal performance or a physiological requirement when animals dry off spontaneously. The goals of this research were to estimate genetic parameters and perform a genome-wide association study in Saanen goats for the capacity to skip the dry period between lactations to identify genes and QTLs underlying this trait. A total of 249 Saanen dairy goats had the length of their dry period determined over lactations, with some (n = 54) showing the capacity to skip the dry period, i.e., having a dry period of a single day. The estimated heritability for the capacity to skip the dry period was moderate (0.25, SE = 0.13). Three SNPs significantly associated with the capacity to skip dry period were identified, which are located close to the OSMR gene, reported to be associated with mammary involution, and a known QTL for cannon bone circumference. The three SNPs were also confined to a very conserved region on chromosome 20, which harbors several genes associated with milk-related traits. The OSMR gene seems to be a good candidate gene for the capacity to skip the dry period, and the genomic region where it is located appears to also be important for milk production traits.
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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 | 0.001 |
| 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".