Bioinformatic processing of RAD‐seq data dramatically impacts downstream population genetic inference
Bibliographic record
Abstract
Summary Restriction site‐associated DNA sequencing (RAD‐seq) provides high‐resolution population genomic data at low cost, and has become an important component in ecological and evolutionary studies. As with all high‐throughput technologies, analytic strategies require critical validation to ensure precise and unbiased interpretation. To test the impact of bioinformatic data processing on downstream population genetic inferences, we analysed mammalian RAD‐seq data (>100 individuals) with 312 combinations of methodology ( de novo vs. mapping to references of increasing divergence) and filtering criteria (missing data, HWE, F IS , coverage, mapping and genotype quality). In an effort to identify commonalities and biases in all pipelines, we computed summary statistics (nr. loci, nr. SNP, π, Het obs , F IS , F ST , N e and m) and compared the results to independent null expectations (isolation‐by‐distance correlation, expected transition‐to‐transversion ratio T s /T v and Mendelian mismatch rates of known parent–offspring trios). We observed large differences between reference‐based and de novo approaches, the former generally calling more SNPs and reducing F IS and T s /T v . Data completion levels showed little impact on most summary statistics, and F ST estimates were robust across all pipelines. The site frequency spectrum was highly sensitive to the chosen approach as reflected in large variance of parameter estimates across demographic scenarios (single‐population bottlenecks and isolation‐with‐migration model). Null expectations were best met by reference‐based approaches, although contingent on the specific criteria. We recommend that RAD‐seq studies employ reference‐based approaches to a closely related genome, and due to the high stochasticity associated with the pipeline advocate the use of multiple pipelines to ensure robust population genetic and demographic inferences.
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 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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".