A critical analysis of disease-associated DNA polymorphisms in the genes of cattle, goat, sheep, and pig
Bibliographic record
Abstract
Genetic variations through their effects on gene expression and protein function underlie disease susceptibility in farm animal species. The variations are in the form of single nucleotide polymorphisms, deletions/insertions of nucleotides or whole genes, gene or whole chromosomal rearrangements, gene duplications, and copy number polymorphisms or variants. They exert varying degrees of effects on gene action, such as substitution of an amino acid for another, shift in reading frame and premature termination of translation, and complete deletion of entire exon(s) or gene(s) in diseased individuals. These factors influence gene function by affecting mRNA splicing pattern or by altering/eliminating protein function. Elucidating the genetic bases of diseases under the control of many genes is very challenging, and it is compounded by several factors, including host x pathogen x environment interactions. In this review, the genetic variations that underlie several diseases of livestock (under monogenic and polygenic control) are analyzed. Also, factors hampering research efforts toward identification of genetic influences on animal disease identification and control are highlighted. A better understanding of the factors analyzed could be better harnessed to effectively identify and control, genetically, livestock diseases. Finally, genetic control of animal diseases can reduce the costs associated with diseases, improve animal welfare, and provide healthy animal products to consumers, and should be given more attention.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".