Development of non-invasive genomic tools for feral horses
Bibliographic record
Abstract
The expansion of genomic technologies has uncovered profound insights into the physiology, etiology, demography, and treatment of disease in human and livestock populations alike. However, these advances have yet to be realized in wildlife due to the unique technical limitations inherent in these populations, including sample availability and quality. Concurrently, novel methods in genomics have been developed that are amenable for processing genetic samples obtained from wildlife. Specifically, target enrichment approaches have been developed that can amplify and genotype samples of low DNA mass, while genome imputation has been demonstrated as a viable approach for inferring missing genotype information at no incremental cost. Though these technologies may help bridge the gap between wildlife genetics and genomics, they have yet to be thoroughly validated in such populations. In this study, I tested a recently developed target enrichment approach, termed ‘Allegro Targeted Genotyping’ (ATG), as well as genome imputation in a population of free-living horses on Sable Island, Nova Scotia, Canada. For target enrichment, I evaluated the concordance between the output genotypes versus those obtained using standard genotyping approaches at the same set of genetic loci. I then determined the accuracy of genome imputation using existing genetic data from this population using different software as well as varying parameters including reference population size, initial genotype density and selection method for the initial genotypes. Genotypes from ATG were repeatable and concordant with standard genotyping panels, while imputation accuracy was generally high (>99%) and largely impacted by reference population size and initial density used for imputation. Both approaches proved successful and will help facilitate the study of population-wide genomics in Sable Island horses and may be generalizable to other wildlife study systems given further validation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".