Validation of a 60K SNP chip for caribou (Rangifer tarandus) for use in wildlife forensics, conservation, and management
Bibliographic record
Abstract
Large-scale genotyping platforms are currently being developed for several wild species. By querying thousands of polymorphic loci, genomics can be a useful ecological tool for describing and monitoring populations. Genomics is becoming increasingly useful as a forensic tool because of its ability to identify population of origin for purposes of enforcing anti-poaching laws. Our aim was to test the new SNP chip for caribou/reindeer ( Rangifer tarandus ) (Illumina iSelect caribou 60 K) under recommended and non-optimal sample conditions. Impact on signal detection (call rate) and error rate were assessed using reference samples. The SNP chip was shown to be robust, highly sensitive, reliable, and accurate at more than 10-fold below the recommended DNA input. Biological source of DNA had minor impact, even with fecal pellets given sufficient amount of host DNA. Hybridization of non- Rangifer samples as well as samples bearing DNA from two Rangifer samples both showed a drop in call rate and shifted levels of heterozygosity. Based on a population-targeted subset of SNPs included in the chip design, reassignment of 981 samples to a functional group (here to a caribou ecotype) was highly accurate (99.59 %) and the relative probability of reassignment error was estimated using the logarithm of odds score. Overall, the SNP chip is suitable for analysis of caribou/reindeer genomes even with suboptimal sampling and hence useful for population management and forensics. • The 60 K SNP chip for caribou ( Rangifer tarandus ) is highly robust and specific. • The method is insensitive to DNA degradation/fragmentation and reliable a 10-fold less input DNA than recommended. • The method is highly reliable with DNA from a range of tissue/sample sources. • The method provides excellent assignment power to the ecotypes found in the province of Quebec (Canada). • The method also demonstrates very efficient assignment at the population level.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".