Targeted genome-wide SNP genotyping in feral horses using non-invasive fecal swabs
Bibliographic record
Abstract
The development of high-throughput sequencing has prompted a transition in wildlife genetics from using microsatellites toward sets of single nucleotide polymorphisms (SNPs). However, genotyping large numbers of targeted SNPs using non-invasive samples remains challenging due to relatively large DNA input requirements. Recently, target enrichment has emerged as a promising approach requiring little template DNA. We assessed the efficacy of Tecan Genomics' Allegro Targeted Genotyping (ATG) for generating genome-wide SNP data in feral horses using DNA isolated from fecal swabs. Total and host-specific DNA were quantified for 989 samples collected as part of a long-term individual-based study of feral horses on Sable Island, Nova Scotia, Canada, using dsDNA fluorescence and a host-specific qPCR assay, respectively. Forty-eight samples representing 44 individuals containing at least 10 ng of host DNA (ATG's recommended minimum input) were genotyped using a custom multiplex panel targeting 279 SNPs. Genotyping accuracy and consistency were assessed by contrasting ATG genotypes with those obtained from the same individuals with SNP microarrays, and from multiple samples from the same horse, respectively. 62% of swabs yielded the minimum recommended amount of host DNA for ATG. Ignoring samples that failed to amplify, ATG recovered an average of 88.8% targeted sites per sample, while genotype concordance between ATG and SNP microarrays was 98.5%. The repeatability of genotypes from the same individual approached unity with an average of 99.9%. This study demonstrates the suitability of ATG for genome-wide, non-invasive targeted SNP genotyping, and will facilitate further ecological and conservation genetics research in equids and related species. Supplementary Information: The online version contains supplementary material available at 10.1007/s12686-022-01259-2.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".