Evaluation of four methods to identify the homozygotic sex chromosome in small populations
Bibliographic record
Abstract
BACKGROUND: Whole genomes are commonly assembled into a collection of scaffolds and often lack annotations of autosomes, sex chromosomes, and organelle genomes (i.e., mitochondrial and chloroplast). As these chromosome types differ in effective population size and can have highly disparate evolutionary histories, it is imperative to take this information into account when analysing genomic variation. Here we assessed the accuracy of four methods for identifying the homogametic sex chromosome in a small population using two whole genome sequences (WGS) and 133 RAD sequences of white-tailed eagles (Haliaeetus albicilla): i) difference in read depth per scaffold in a male and a female, ii) heterozygosity per scaffold in a male and a female, iii) mapping to the reference genome of a related species (chicken) with annotated sex chromosomes, and iv) analysis of SNP-loadings from a principal components analysis (PCA), based on the low-depth RADseq data. RESULTS: The best performing approach was the reference mapping (method iii), which identified 98.12% of the expected homogametic sex chromosome (Z). Read depth per scaffold (method i) identified 86.41% of the homogametic sex chromosome with few false positives. SNP-loading scores (method iv) identified 78.6% of the Z-chromosome and had a false positive discovery rate of more than 10%. Heterozygosity per scaffold (method ii) did not provide clear results due to a lack of diversity in both the Z and autosomal chromosomes, and potential interference from the heterogametic sex chromosome (W). The evaluation of these methods also revealed 10 Mb of putative PAR and gametologous regions. CONCLUSION: Identification of the homogametic sex chromosome in a small population is best accomplished by reference mapping or examining differences in read depth between sexes.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".