Targeted capture and resequencing of 1040 genes reveal environmentally driven functional variation in grey wolves
Bibliographic record
Abstract
In an era of ever-increasing amounts of whole-genome sequence data for individuals and populations, the utility of traditional single nucleotide polymorphisms (SNPs) array-based genome scans is uncertain. We previously performed a SNP array-based genome scan to identify candidate genes under selection in six distinct grey wolf (Canis lupus) ecotypes. Using this information, we designed a targeted capture array for 1040 genes, including all exons and flanking regions, as well as 5000 1-kb nongenic neutral regions, and resequenced these regions in 107 wolves. Selection tests revealed striking patterns of variation within candidate genes relative to noncandidate regions and identified potentially functional variants related to local adaptation. We found 27% and 47% of candidate genes from the previous SNP array study had functional changes that were outliers in sweed and bayenv analyses, respectively. This result verifies the use of genomewide SNP surveys to tag genes that contain functional variants between populations. We highlight nonsynonymous variants in APOB, LIPG and USH2A that occur in functional domains of these proteins, and that demonstrate high correlation with precipitation seasonality and vegetation. We find Arctic and High Arctic wolf ecotypes have higher numbers of genes under selection, which highlight their conservation value and heightened threat due to climate change. This study demonstrates that combining genomewide genotyping arrays with large-scale resequencing and environmental data provides a powerful approach to discern candidate functional variants in natural populations.
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".