ArticlePLoS genetics2024
Species-wide survey of the expressivity and complexity spectrum of traits in yeast.
Article in PLoS genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.NAR genomics and bioinformatics · 2026Article
- High-resolution genotype-free mapping of genetic variation with CRI-SPA-Map.Genome research · 2026Article
- Predictive models of the genetic bases underlying budding yeast fitness in multiple environments.bioRxiv : the preprint server for biology · 2025Article
- Machine learning reveals genes impacting oxidative stress resistance across yeasts.Nature communications · 2025Article
- Epistasis and cryptic QTL identified using modified bulk segregant analysis of copper resistance in budding yeast.Genetics · 2025Article
- Epistasis and cryptic QTL identified using modified bulk segregant analysis of copper resistance in budding yeast.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Assessing the complexity and expressivity of traits at the species level is an essential first step to better dissect the genotype-phenotype relationship. As trait complexity behaves dynamically, the classic dichotomy between monogenic and complex traits is too simplistic. However, no systematic assessment of this complexity spectrum has been carried out on a population scale to date. In this context, we generated a large diallel hybrid panel composed of 190 unique hybrids coming from 20 natural isolates representative of the S. cerevisiae genetic diversity. For each of these hybrids, a large progeny of 160 individuals was obtained, leading to a total of 30,400 offspring individuals. Their mitotic growth was evaluated on 38 conditions inducing various cellular stresses. We developed a classification algorithm to analyze the phenotypic distributions of offspring and assess the trait complexity. We clearly found that traits are mainly complex at the population level. On average, we found that 91.2% of cross/trait combinations exhibit high complexity, while monogenic and oligogenic cases accounted for only 4.1% and 4.7%, respectively. However, the complexity spectrum is very dynamic, trait specific and tightly related to genetic backgrounds. Overall, our study provided greater insight into trait complexity as well as the underlying genetic basis of its spectrum in a natural population.
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Registered trials
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