P6010 Identification of novel genetic variants in the equine collagenous lectin genes through targeted, next generation re-sequencing
Bibliographic record
Abstract
Infectious diseases are an important source of welfare and economic burden in horses. Collagenous lectins are a family of soluble pattern recognition receptors that play an important role in innate immune resistance to infectious disease. Through recognition of carbohydrate motifs on the surface of pathogens, some collagenous lectins can activate the lectin pathway of complement, providing an effective means of defense. They may also opsonize, agglutinate, or directly neutralize pathogens. Genetic polymorphisms in collagenous lectins have been shown in other species to predispose animals to a variety of infectious diseases. In this case-control study, we used a high-throughput, targeted re-sequencing approach to investigate the relationship between genetic variation in equine collagenous lectin genes and susceptibility to disease. DNA was isolated from the liver of normal (n = 35) and diseased (n = 54) horses submitted for post-mortem examination to the Ontario Veterinary College and the Animal Health Laboratory at the University of Guelph. Animals were grouped together by dominant pathological process and their DNA was pooled in equal amounts, for a total of 21 groups, each containing 4–5 horses. The exons, introns, upstream (up to 50 kb) and downstream (up to 3 kb) regulatory regions for the 11 equine collagenous lectin genes and related MASPs were targeted for re-sequencing. A custom-made Roche Nimblegen EZ Developer kit was used to prepare the library, which was subsequently sequenced on an Illumina MiSeq. In total, 3.4 Gb of sequence data was obtained with a mean read depth of 39x per horse. After implementing quality control filters, 5145 single nucleotide variants (SNVs) were identified, 4174 (81%) of which had not been previously reported in dbSNP (build 144). Of these, 82 were present in the coding regions (35 missense, 47 synonymous), 1530 in introns, 3509 in the upstream regulatory region, and 279 in the downstream regulatory region. In silico analysis of the missense SNVs identified 13 mutations with potential to disrupt collagenous lectin protein structure or function. The putative impact of SNVs on potential promoters was investigated, and allele frequency was compared between normal and diseased groups. This study contributes to the growing body of evidence that pooled, high-throughput sequencing is a viable strategy for cost-effective SNV discovery. The SNVs discovered in this experiment represent potential genetic contributions to disease susceptibility of horses, and will serve as candidates for further population-level genotyping.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".