Design of a 9K illumina BeadChip for polar bears (<i><scp>U</scp>rsus maritimus</i>) from <scp>RAD</scp> and transcriptome sequencing
Bibliographic record
Abstract
Single-nucleotide polymorphisms (SNPs) offer numerous advantages over anonymous markers such as microsatellites, including improved estimation of population parameters, finer-scale resolution of population structure and more precise genomic dissection of quantitative traits. However, many SNPs are needed to equal the resolution of a single microsatellite, and reliable large-scale genotyping of SNPs remains a challenge in nonmodel species. Here, we document the creation of a 9K Illumina Infinium BeadChip for polar bears (Ursus maritimus), which will be used to investigate: (i) the fine-scale population structure among Canadian polar bears and (ii) the genomic architecture of phenotypic traits in the Western Hudson Bay subpopulation. To this end, we used restriction-site associated DNA (RAD) sequencing from 38 bears across their circumpolar range, as well as blood/fat transcriptome sequencing of 10 individuals from Western Hudson Bay. Six-thousand RAD SNPs and 3000 transcriptomic SNPs were selected for the chip, based primarily on genomic spacing and gene function respectively. Of the 9000 SNPs ordered from Illumina, 8042 were successfully printed, and - after genotyping 1450 polar bears - 5441 of these SNPs were found to be well clustered and polymorphic. Using this array, we show rapid linkage disequilibrium decay among polar bears, we demonstrate that in a subsample of 78 individuals, our SNPs detect known genetic structure more clearly than 24 microsatellites genotyped for the same individuals and that these results are not driven by the SNP ascertainment scheme. Here, we present one of the first large-scale genotyping resources designed for a threatened species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".