Quantitative genetics of natural <i>S. cerevisiae</i> strains upon sexual mating reveals heritable determinants of cellular fitness
Bibliographic record
Abstract
Abstract Quantitative genetics requires large datasets of diverse phenotyped-genotyped strains from the same species. A special need is for such archived biological material and computerized data in sexually reproducing individuals from a species. Here we leverage sexual mating among close to 100 diverse natural isolates of the yeast S. cerevisiae that form about 4,000 offspring combinations in several ecologically relevant growth conditions. In a first genetic study of this new resource we focus on fitness measurements and its modes of inheritance as a quantitative trait from parents to offspring. We employ genomic barcoding of all strains and a barcode recombination technique to follow offspring of each successful mate combination. For all parents, and separately for all offspring we measure fitness under each condition. We focus on the inheritance of fitness, the ultimate evolutionary trait, and its inheritance as a quantitative trait upon sexual mating. Predicting offspring fitness given parental parameters is a major challenge as it is likely multi-factorial. We find that offspring fitness in fermentable carbon source correlates positively, yet modestly, with parental fitness, while on non-fermentable carbon, offspring fitness shows no detectable correlation with parental fitness. Instead, the non-fermentable condition, fitness of offspring increases sharply with genetic distance between their parents, suggesting that outbreeding maximizes offspring fitness irrespective of parental fitness at that condition. The number of minor alleles in the genome of each offspring, analogous to polygenic risk score in classical genetics, negatively correlates with offspring fitness in both conditions. Modeling of fitness inheritance shows that modes of inheritance are explained by either a dominance or a co-dominance models, in the fermentable and non-fermentable conditions respectively. Our newly suggested biological resource and data provide new foundations for a quantitative research in genetics and evolution upon sexual mating. Furthermore, our barcoded strains and mating tracking method provide an important research resource for the yeast community.
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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 | 0.001 | 0.001 |
| 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 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".