Identification of genetic variation in equine collagenous lectins using targeted resequencing
Bibliographic record
Abstract
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 host defense. Genetic polymorphisms in collagenous lectins have been shown in several species to predispose animals to a variety of infectious diseases. Infectious diseases are an important cause of morbidity in horses, however little is known regarding the role of equine collagenous lectins. Using a high-throughput, targeted re-sequencing approach, the relationship between genetic variation in equine collagenous lectin genes and susceptibility to disease was investigated. DNA was isolated from tissues obtained from horses submitted for post-mortem examination. Animals were divided into two populations, those with infectious or autoinflammatory diseases (n = 37) and those without (n = 52), and then subdivided by dominant pathological process for a total of 21 pools, each containing 4-5 horses. DNA was extracted from each horse and pooled in equimolar amounts, and the exons, introns, upstream (approximately 50 kb) and downstream (approximately 3 kb) regulatory regions for the 11 equine collagenous lectin genes and related MASP genes were targeted for re-sequencing. A custom target capture kit was used to prepare a sequencing library, which was sequenced on an Illumina MiSeq. After implementing quality control filters, 4559 variants were identified. Of these, 92 were present in the coding regions (43 missense, 1 nonsense, and 48 synonymous), 1414 in introns, 3029 in the upstream region, and 240 in the downstream region. In silico analysis of the missense short nucleotide variants identified 12 mutations with potential to disrupt collagenous lectin protein structure or function, 280 mutations located within predicted transcription factor binding sites, and 95 mutations located within predicted microRNA binding elements. Analysis of allelic association identified 113 mutations that segregated between the infectious/autoinflammatory and non-infectious populations. The variants discovered in this experiment represent potential genetic contributors to disease susceptibility of horses, and will serve as candidates for further population-level genotyping. This study contributes to the growing body of evidence that pooled, high-throughput sequencing is a viable strategy for cost-effective variant discovery.
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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 | 0.001 |
| 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) | 0.001 | 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".