Contrasting whole-genome and reduced representation sequencing for population demographic inference: an alpine mammal example
Bibliographic record
Abstract
Genomic approaches to the study of population demography rely on accurate SNP calling and by-proxy the site frequency spectrum (SFS). Two main questions for the design of such studies remain poorly investigated: do reduced genomic sequencing summary statistics reflect that of whole genome, and how do sequencing strategies and derived summary statistics impact demographic inferences? To address those questions, we applied the ddRAD sequencing approach to 254 individuals and whole genome resequencing approach to 35 mountain goat (Oreamnos americanus) individuals across the species range with a known demographic history. We identified SNPs with 5 different variant callers and used ANGSD to estimate the genotype likelihoods (GLs). We tested combinations of SNP filtering by linkage disequilibrium (LD), minor allele frequency (MAF) and the genomic region. We compared the resulting suite of summary statistics reflective of the SFS and quantified the relationship to demographic inferences by estimating the contemporary effective population size (Ne), isolation-by-distance and population structure, FST, and explicit modelling of the demographic history with δaδi. Filtering had a larger effect than sequencing strategy, with the former strongly influencing summary statistics. Estimates of contemporary Ne and isolation-by-distance patterns were largely robust to the choice of sequencing, pipeline, and filtering. Despite the high variance in summary statistics, whole genome and reduced representation approaches were overall similar in supporting a glacial induced vicariance and low Ne in mountain goats. We discuss why whole genome resequencing data is preferable, and reiterate support the use of GLs, in part because it limits user-determined filters.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".