Whole-genome sequencing reveals the extent of heterozygosity in a preferentially self-fertilizing hermaphroditic vertebrate
Bibliographic record
Abstract
The mangrove rivulus, Kryptolebias marmoratus, is one of only two self-fertilizing hermaphroditic fish species and inhabits mangrove forests. While selfing can be advantageous, it reduces heterozygosity and decreases genetic diversity. Studies using microsatellites found that there are variable levels of selfing among populations of K. marmoratus, but overall, there is a low rate of outcrossing and, therefore, low heterozygosity. In this study, we used whole-genome data to assess the levels of heterozygosity in different lineages of the mangrove rivulus and infer the phylogenetic relationships among those lineages. We sequenced whole genomes from 15 lineages that were completely homozygous at microsatellite loci and used single nucleotide polymorphisms (SNPs) to determine heterozygosity levels. More variation was uncovered than in studies using microsatellite data because of the resolution of full genome sequencing data. Moreover, missense polymorphisms were found most often in genes associated with immune function and reproduction. Inferred phylogenetic relationships suggest that lineages largely group by their geographic distribution. The use of whole-genome data provided further insight into genetic diversity in this unique species. Although this study was limited by the number of lineages that were available, these data suggest that there is previously undescribed variation within lineages of K. marmoratus that could have functional consequences and (or) inform us about the limits to selfing (e.g., genetic load, accumulation of deleterious mutations) and selection that might favor the maintenance of heterozygosity. These results highlight the need to sequence additional individuals within and among lineages.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".