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We characterized previously identified RNA viruses (L-A, L-BC, 20S, and 23S), L-A-dependent M satellites (M1, M2, M28, and Mlus), and M satellite-dependent killer phenotypes in the Saccharomyces cerevisiae 100-genomes genetic resource population. L-BC was present in all strains, albeit in 2 distinct levels, L-BChi and L-BClo; the L-BC level is associated with the L-BC genotype. L-BChi, L-A, 20S, 23S, M1, M2, and Mlus (M28 was absent) were in fewer strains than the similarly inherited 2µ plasmid. Novel L-A-dependent phenotypes were identified. Ten M+ strains exhibited M satellite-dependent killing (K+) of at least 1 of the naturally M0 and cured M0 derivatives of the 100-genomes strains; in these M0 strains, sensitivities to K1+, K2+, and K28+ strains varied. Finally, to complement our M satellite-encoded killer toxin analysis, we assembled the chromosomal KHS1 and KHR1 killer genes and used naturally M0 and cured M0 derivatives of the 100-genomes strains to assess and characterize the chromosomal killer phenotypes., Competing Interests: Conflicts of interest The authors declare no conflict of interest., (© The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.)

Mitochondrial genome variation and its effects on phenotypes have been widely analyzed in higher eukaryotes but less so in the model eukaryote Saccharomyces cerevisiae Here, we describe mitochondrial genome variation in 96 diverse S. cerevisiae strains and assess associations between mitochondrial genotype and phenotypes as well as nuclear-mitochondrial epistasis. We associate sensitivity to the ATP synthase inhibitor oligomycin with SNPs in the mitochondrially encoded ATP6 gene. We describe the use of iso-nuclear F1 pairs, the mitochondrial genome equivalent of reciprocal hemizygosity analysis, to identify and analyze mitochondrial genotype-dependent phenotypes. Using iso-nuclear F1 pairs, we analyze the oligomycin phenotype- ATP6 association and find extensive nuclear-mitochondrial epistasis. Similarly, in iso-nuclear F1 pairs, we identify many additional mitochondrial genotype-dependent respiration phenotypes, for which there was no association in the 96 strains, and again find extensive nuclear-mitochondrial epistasis that likely contributes to the lack of association in the 96 strains. Finally, in iso-nuclear F1 pairs, we identify novel mitochondrial genotype-dependent nonrespiration phenotypes: resistance to cycloheximide, ketoconazole, and copper. We discuss potential mechanisms and the implications of mitochondrial genotype and of nuclear-mitochondrial epistasis effects on respiratory and nonrespiratory quantitative traits., (Copyright © 2019 by the Genetics Society of America.)

Natural selection has the potential to act on all phenotypes, including genomic mutation rate. Classic evolutionary theory predicts that in asexual populations, mutator alleles, which cause high mutation rates, can fix due to linkage with beneficial mutations. This phenomenon has been demonstrated experimentally and may explain the frequency of mutators found in bacterial pathogens. By contrast, in sexual populations, recombination decouples mutator alleles from beneficial mutations, preventing mutator fixation. In the facultatively sexual yeast Saccharomyces cerevisiae , segregating alleles of MLH1 and PMS1 have been shown to be incompatible, causing a high mutation rate when combined. These alleles had never been found together naturally, but were recently discovered in a cluster of clinical isolates. Here we report that the incompatible mutator allele combination only marginally elevates mutation rate in these clinical strains. Genomic and phylogenetic analyses provide no evidence of a historically elevated mutation rate. We conclude that the effect of the mutator alleles is dampened by background genetic modifiers. Thus, the relationship between mutation rate and microbial pathogenicity may be more complex than once thought. Our findings provide rare observational evidence that supports evolutionary theory suggesting that sexual organisms are unlikely to harbour alleles that increase their genomic mutation rate., (© 2017 The Author(s).)

Advances in high-throughput sequencing have facilitated large-scale surveys of genomic variation in the budding yeast,Saccharomyces cerevisiae These surveys have revealed extensive sequence variation between yeast strains. However, much less is known about how such variation influences the amount and nature of variation for functional genomic traits within and between yeast lineages. We review population-level studies of functional genomic variation, with a particular focus on how population functional genomic approaches can provide insights into both genome function and the evolutionary process. Although variation in functional genomics phenotypes is pervasive, our understanding of the consequences of this variation, either in physiological or evolutionary terms, is still rudimentary and thus motivates increased attention to appropriate null models. To date, much of the focus of population functional genomic studies has been on gene expression variation, but other functional genomic data types are just as likely to reveal important insights at the population level, suggesting a pressing need for more studies that go beyond transcription. Finally, we discuss how a population functional genomic perspective can be a powerful approach for developing a mechanistic understanding of the processes that link genomic variation to organismal phenotypes through gene networks., (© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Saccharomyces cerevisiae, a well-established model for species as diverse as humans and pathogenic fungi, is more recently a model for population and quantitative genetics. S. cerevisiae is found in multiple environments-one of which is the human body-as an opportunistic pathogen. To aid in the understanding of the S. cerevisiae population and quantitative genetics, as well as its emergence as an opportunistic pathogen, we sequenced, de novo assembled, and extensively manually edited and annotated the genomes of 93 S. cerevisiae strains from multiple geographic and environmental origins, including many clinical origin strains. These 93 S. cerevisiae strains, the genomes of which are near-reference quality, together with seven previously sequenced strains, constitute a novel genetic resource, the "100-genomes" strains. Our sequencing coverage, high-quality assemblies, and annotation provide unprecedented opportunities for detailed interrogation of complex genomic loci, examples of which we demonstrate. We found most phenotypic variation to be quantitative and identified population, genotype, and phenotype associations. Importantly, we identified clinical origin associations. For example, we found that an introgressed PDR5 was present exclusively in clinical origin mosaic group strains; that the mosaic group was significantly enriched for clinical origin strains; and that clinical origin strains were much more copper resistant, suggesting that copper resistance contributes to fitness in the human host. The 100-genomes strains are a novel, multipurpose resource to advance the study of S. cerevisiae population genetics, quantitative genetics, and the emergence of an opportunistic pathogen., (© 2015 Strope et al.; Published by Cold Spring Harbor Laboratory Press.)

To better understand the quantitative characteristics and structure of phenotypic diversity, we measured over 14,000 transcript, protein, metabolite, and morphological traits in 22 genetically diverse strains of Saccharomyces cerevisiae. More than 50% of all measured traits varied significantly across strains [false discovery rate (FDR) = 5%]. The structure of phenotypic correlations is complex, with 85% of all traits significantly correlated with at least one other phenotype (median = 6, maximum = 328). We show how high-dimensional molecular phenomics data sets can be leveraged to accurately predict phenotypic variation between strains, often with greater precision than afforded by DNA sequence information alone. These results provide new insights into the spectrum and structure of phenotypic diversity and the characteristics influencing the ability to accurately predict phenotypes.

Noncoding genetic variation is known to significantly influence gene expression levels in a growing number of specific cases; however, the patterns of genome-wide noncoding variation present within populations, the evolutionary forces acting on noncoding variants, and the relative effects of regulatory polymorphisms on transcript abundance are not well characterized. Here, we address these questions by analyzing patterns of regulatory variation in motifs for 177 DNA binding proteins in 37 strains of Saccharomyces cerevisiae. Between S. cerevisiae strains, we found considerable polymorphism in regulatory motifs across strains (mean π = 0.005) as well as diversity in regulatory motifs (mean 0.91 motifs differences per regulatory region). Population genetics analyses reveal that motifs are under purifying selection, and there is considerable heterogeneity in the magnitude of selection across different motifs. Finally, we obtained RNA-Seq data in 22 strains and identified 49 polymorphic DNA sequence motifs in 30 distinct genes that are significantly associated with transcriptional differences between strains. In 22 of these genes, there was a single polymorphic motif associated with expression in the upstream region. Our results provide comprehensive insights into the evolutionary trajectory of regulatory variation in yeast and the characteristics of a compendium of regulatory alleles.