52 Genomic selection in the beef industry: Current achievements and future directions
Bibliographic record
Abstract
Abstract In the past decade, genomic testing of beef cattle has evolved from applications in research to a routine practice for many beef cattle seedstock breeders. Testing for lethal genetic conditions or parentage was many breeders’ first experience with genomic testing. While the American Angus Association (AAA) began utilizing 384 SNP genotypes in genetic evaluations in 2009, the adoption of genotyping with higher density (~50,000 SNP) arrays by AAA in 2010 launched large-scale genotyping of Angus cattle for genetic evaluation. AAA transitioned from semi-annual to weekly genetic evaluations in 2010, and cost of genotyping decreased from $139 per animal in 2011, to $37 in 2017. In fiscal year 2018, AAA members genotyped over 160,000 animals for genetic evaluation, and as of April 2019, the AAA and Canadian Angus Association joint genetic evaluation includes over 635,000 genotyped animals. Now genotyping arrays with Angus-specific SNP content are used. The primary benefit to Angus breeders has been increased accuracy of genetic prediction for young animals, especially for traits with limited phenotypic information such as carcass traits, feed intake and mature cow size. Future benefits from genotyping include identification and selection against embryonic lethal alleles, better characterization of inbreeding, and selection tools for additional traits relevant to or measured in unique environments. Electronic sensors and other novel approaches may yield previously unmeasurable phenotypes for health and efficiency traits, which can be extended to wider populations for selection using genomics. New techniques such as DNA pooling and genotyping by sequencing may reduce costs enabling widespread testing in commercial cow-calf and cattle feeding enterprises. The application of genomic selection has clearly been a significant advancement in genetic selection in Angus cattle in the past ten years. This early adoption will expedite subsequent genomic tools at an increasing rate and will foster innovation.
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 | 0.011 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".