256 GENOMIC EVALUATION OF FERTILITY TRAITS AND DISCOVERY OF HAPLOTYPES THAT AFFECT FERTILITY OF US DAIRY CATTLE
Bibliographic record
Abstract
Genomic evaluations of dairy cattle became official in the United States in January 2009 for Holsteins and Jerseys, and later for Brown Swiss, Ayrshires, and Guernseys. Up to 33 yield, fitness, calving, and conformation traits are evaluated, and the fertility traits included daughter pregnancy rate and heifer and cow conception rates. Additional fertility traits, such as age at first calving and days from calving to first insemination, also are being studied. Male fertility (sire conception rate) is evaluated phenotypically rather than through genomics. Over 1 million animals have genotypes in the national database, which reflects collaboration with Canada and Europe. Most of the genotypes are from females and are from genotyping chips with <30 000 single nucleotide polymorphisms (SNP). To combine data across chips, genotypes are imputed to a set of >77 000 SNP. The imputation process involves dividing the chromosome into segments of approximately equal length and determining the paternal or maternal origin of the alleles. Because some segments were never homozygous, they were assumed to contain an abnormality that resulted in early embryonic death. If a decrease in sire conception rate could be associated with a bull that was a carrier of such a chromosomal segment, the haplotype was designated as affecting fertility. Once the region was identified, bioinformatic analysis was used to discover the causative variant for many of those haplotypes. Accuracy of genomic evaluations is determined by size of the reference population and heritability of the trait. The reference population for Holsteins includes >180 000 bulls and cows. Because fertility traits have low heritabilities, genomic information is particularly useful in improving evaluation accuracy. Accuracy of fertility evaluations is expected to increase further by discovering causative variants for various aspects of conception and gestation through investigation of sequence data.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".