Sex matters in massive parallel sequencing: Evidence for biases in genetic parameter estimation and investigation of sex determination systems
Bibliographic record
Abstract
Abstract Using massively parallel sequencing data from two species with different life history traits, American lobster ( Homarus americanus ) and Arctic Char ( Salvelinus alpinus ), we highlight how an unbalanced sex ratio in the samples and a few sex‐linked markers may lead to false interpretations of population structure and thus to potentially erroneous management recommendations. Here, multivariate analyses revealed two genetic clusters separating samples by sex instead of by expected spatial variation: inshore and offshore locations in lobster, or east and west locations in Arctic Char. To further investigate this, we created several subsamples artificially varying the sex ratio in the inshore/offshore and east/west groups and then demonstrated that significant genetic differentiation could be observed despite panmixia in lobster, and that F ST values were overestimated in Arctic Char. This pattern was due to 12 and 94 sex‐linked markers driving differentiation for lobster and Arctic Char, respectively. Removing sex‐linked markers led to nonsignificant genetic structure in lobster and a more accurate estimation of F ST in Arctic Char. The locations of these markers and putative identities of genes containing or nearby the markers were determined using available transcriptomic and genomic data, and this provided new information related to sex determination in both species. Given that only 9.6% of all marine/diadromous population genomic studies to date have reported sex information, we urge researchers to collect and consider individual sex information. Sex information is therefore relevant for avoiding unexpected biases due to sex‐linked markers as well as for improving our knowledge of sex determination systems in nonmodel species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".