758 Understanding the nature of complex phenotypes in beef cattle using systems biology
Bibliographic record
Abstract
In recent years, breeders have combined the use of phenotypic appraisal and the estimation of breeding values (EPD) to make genetic selection decisions in beef and dairy cattle that have resulted in a steady genetic gain of 2% per year. However, the most extensive application of genomics has occurred in the dairy industry with the estimation of molecular breeding values that has improved selection efficiency to a much higher order of magnitude. Despite a growing molecular and physiological understanding of complex traits, little is known about the genes determining the traits and their precise function, and a significant unexplained source of variation of phenotypes remains in livestock. Within this context, a more complete understanding of the genes and regulatory pathways and networks involved in economically important traits (i.e. fertility and reproduction, feed efficiency, meat quality and carcass traits) in beef cattle will provide knowledge to help improve genetic selection and reproductive management. Currently, with all the new available technologies in livestock combined with statistical methodologies, the integration of structural and functional genomics information with other –OMICS into a systems biology approach has allowed development of a better biological understanding of phenotypes complementing the traditional genetic tools and further advance identification of functional genes. As part of the genomics tool box and the HD-genotyping SNP chips, whole genome sequencing technologies are now available in cattle and extensively utilized in genetic improvement. As a part of high throughput tools available for genomic analysis, RNA-Sequencing allows measuring not only gene expression, but also examining genome structure identifying SNP and other structural variation such as insertions, deletions and splice variants. The expectation is that the integration of all these types of genomic data will accelerate the genetic improvement by improving accuracy of selection and reducing the generation interval. Combining the information from the –OMICS technologies (i.e., transcriptomics, metagenomics, metabolomics, amongst several others) together with metabolic pathways and functional/biological analysis into a systems biology approach allows the identification of functional SNP increasing the accuracy of selection. The particular benefits of new integrated high-throughput genomics technologies within a systems biology approach will most likely be used to accelerate the genetic improvement of those traits that are difficult to measure such as health, feed efficiency, methane emission and fertility and reproduction traits in beef cattle.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".