Genomic health is dependent on long-term population demographic history
Bibliographic record
Abstract
Abstract Current genetic methods of population assessment in conservation biology have been challenged by genome-scale analyses due to their quantitatively novel insights. These analyses include assessments of runs-of-homozygosity (ROH), genomic evolutionary rate profiling (GERP), and mutational load. Here, we aim to elucidate the relationships between these measures using three divergent ungulates: the white-tailed deer, caribou, and mountain goat. The white-tailed deer is currently expanding, while caribou are in the midst of a significant decline. Mountain goats remain stable, having suffered a large historical bottleneck. We assessed genome-wide signatures of inbreeding using the inbreeding coefficient F and %ROH ( F ROH ) and identified evolutionarily constrained regions with GERP. Mutational load was estimated by identifying mutations in highly constrained elements (CEs) and sorting intolerant from tolerant (SIFT) mutations. Our results show that F and F ROH are higher in mountain goats than in caribou and white-tailed deer. Given the extended bottleneck and low N e of the mountain goat, this supports the idea that the genome-wide effects of demographic change take time to accrue. Similarly, we found that mountain goats possess more highly constrained CEs and the lowest dN/dS values, both of which are indicative of greater purifying selection; this is also reflected by fewer mutations in CEs and deleterious mutations identified by SIFT. In contrast, white-tailed deer presented the highest mutational load with both metrics, in addition to dN/dS, while caribou were intermediate. Our results demonstrate that extended bottlenecks may lead to reduced diversity and increased F ROH in ungulates, but not necessarily the accumulation of deleterious alleles, likely due to the purging of deleterious alleles in small populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".