Let's talk about sex: A rigorous statistical framework to assign the sex of individuals from reduced‐representation sequencing data
Bibliographic record
Abstract
Molecular markers have been used to identify the sex of sampled individuals for several decades, but the time-consuming development phase prevented their application in many systems. Recently, a growing number of papers have applied reduced-representation sequencing (RRS) protocols to the identification of sex-specific markers without the use of test crosses or prior genomic information. While such an approach has great advantages in terms of versatility and ease of use, the "shotgun sequencing" nature of RRS data sets leads to a high amount of missing data, which results in statistical challenges to the confident assignment of sex to individuals. In this issue of Molecular Ecology Resources, Stovall et al. (Molecular Ecology Resources, 18, 2018) provide a statistical framework to answer two questions: (1) how many individuals of one sex only must possess a genotype for this locus to be considered significantly sex-specific? and (2) How many sex-specific loci must an individual of unknown sex possess (in a given data set) to be confidently assigned a sex? The statistical pipeline introduced, and applied to samples of New Zealand fur seal (Arctocephalus forsteri) to identify 90 sex-specific loci, should be broadly applicable to a large number of species and constitutes a nice addition to the molecular ecology toolkit in the genomics era.
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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 | 0.049 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.016 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".