Identification of polymorphisms in the bovine collagenous lectins and their association with infectious diseases in cattle
Bibliographic record
Abstract
Infectious diseases are a significant issue in animal production systems, including both the dairy and beef cattle industries. Understanding and defining the genetics of infectious disease susceptibility in cattle is an important step in the mitigation of their impact. Collagenous lectins are soluble pattern recognition receptors that form an important part of the innate immune system, which serves as the first line of host defense against pathogens. Polymorphisms in the collagenous lectin genes have been shown in previous studies to contribute to infectious disease susceptibility, and in cattle, mutations in two collagenous lectin genes ( MBL1 and MBL2 ) are associated with mastitis. To further characterize the contribution of variation in the bovine collagenous lectins to infectious disease susceptibility, we used a pooled NGS approach to identify short nucleotide variants (SNVs) in the collagenous lectins (and regulatory DNA) of cattle with ( n = 80) and without ( n = 40) infectious disease. Allele frequency analysis identified 74 variants that were significantly ( p < 5 × 10 −6 ) associated with infectious disease, the majority of which were clustered in a 29-kb segment upstream of the collectin locus on chromosome 28. In silico analysis of the functional effects of all the variants predicted 11 SNVs with a deleterious effect on protein structure and/or function, 148 SNVs that occurred within potential transcription factor binding sites, and 31 SNVs occurring within potential miRNA binding elements. This study provides a detailed look at the genetic variation of the bovine collagenous lectins and identifies potential genetic markers for infectious disease susceptibility.
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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 | 0.000 |
| 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".