Species-wide survey of the expressivity and complexity spectrum of traits in yeast.

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.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast.

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Non-additive genetic components contribute significantly to population-wide gene expression variation.

Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.

Non-additive genetic components contribute significantly to population-wide gene expression variation.

Gene expression variation, an essential step between genomic variation and phenotypic landscape, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence the heritability of expression traits remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originated from genetically divergent yeast isolates. We estimated the broad- and narrow-sense heritability across 5,087 transcript abundance traits and showed that non-additive components account for 36% of the phenotypic variance on average. By comparing allelic expression ratios in the hybrid and the corresponding parental pair, we identified regulatory changes in 25% of all cases, with a majority acting in trans. We further showed that trans-regulation could underlie coordinated expression variation across highly connected genes, resulting in significantly higher non-additive variance and most likely in some of the missing heritability of gene expression traits.

Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast.

Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.

Contrasting Genomic Evolution Between Domesticated and Wild Kluyveromyces lactis Yeast Populations.

The process of domestication has variable consequences on genome evolution leading to different phenotypic signatures. Access to the complete genome sequences of a large number of individuals makes it possible to explore the different facets of this domestication process. Here, we sought to explore the genome evolution of Kluyveromyces lactis, a yeast species well known for its involvement in dairy processes and also present in natural environments. Using a combination of short- and long-read sequencing strategies, we investigated the genomic variability of 41 K. lactis isolates and found that the overall genetic diversity of this species is very high (θw = 3.3 × 10-2) compared with other species such as Saccharomyces cerevisiae (θw = 1.6 × 10-2). However, the domesticated dairy population shows a reduced level of diversity (θw = 1 × 10-3), probably due to a domestication bottleneck. In addition, this entire population is characterized by the introgression of the LAC4 and LAC12 genes, responsible for lactose fermentation and coming from the closely related species, Kluyveromyces marxianus, as previously described. Our results highlighted that the LAC4/LAC12 gene cluster was acquired through multiple and independent introgression events. Finally, we also identified several genes that could play a role in adaptation to dairy environments through copy number variation. These genes are involved in sugar consumption, flocculation, and drug resistance, and may play a role in dairy processes. Overall, our study illustrates contrasting genomic evolution and sheds new light on the impact of domestication processes on it.

RNA Interference (RNAi ) as a Tool for High-Resolution Phenotypic Screening of the Pathogenic Yeast Candida glabrata.

After its discovery RNA interference (RNAi) has become a powerful tool to study gene functions in different organisms. RNAi has been applied at genome-wide scale and can be nowadays performed using high-throughput automated systems (robotics). The simplest RNAi process requires the expression of two genes (Dicer and Argonaute) to function. To initiate the silencing, constructs generating either double-strand RNA or antisense RNA are required. Recently, RNAi was reconstituted by expressing Saccharomyces castellii genes in the human pathogenic yeast Candida glabrata and was used to identify new genes related to the virulence of this pathogen.In this chapter, we describe a method to make the C. glabrata pathogenic yeast competent for RNAi and to use RNA silencing as a tool for low- or high-resolution phenotypic screening in this species.

Phased polyploid genomes provide deeper insight into the multiple origins of domesticated Saccharomyces cerevisiae beer yeasts.

Yeasts, and in particular Saccharomyces cerevisiae, have been used for brewing beer for thousands of years. Population genomic surveys highlighted that beer yeasts are polyphyletic, with the emergence of different domesticated subpopulations characterized by high genetic diversity and ploidy level. However, the different origins of these subpopulations are still unclear as reconstruction of polyploid genomes is required. To gain better insight into the differential evolutionary trajectories, we sequenced the genomes of 35 Saccharomyces cerevisiae isolates coming from different beer-brewing clades, using a long-read sequencing strategy. By phasing the genomes and using a windowed approach, we identified three main beer subpopulations based on allelic content (European dominant, Asian dominant, and African beer). They were derived from different admixtures between populations and are characterized by distinctive genomic patterns. By comparing the fully phased genes, the most diverse in our dataset are enriched for functions relevant to the brewing environment such as carbon metabolism, oxidoreduction, and cell wall organization activity. Finally, independent domestication, evolution, and adaptation events across subpopulations were also highlighted by investigating specific genes previously linked to the brewing process. Altogether, our analysis based on phased polyploid genomes has led to new insight into the contrasting evolutionary history of beer isolates.

nPhase: an accurate and contiguous phasing method for polyploids.

While genome sequencing and assembly are now routine, we do not have a full, precise picture of polyploid genomes. No existing polyploid phasing method provides accurate and contiguous haplotype predictions. We developed nPhase, a ploidy agnostic tool that leverages long reads and accurate short reads to solve alignment-based phasing for samples of unspecified ploidy ( https://github.com/OmarOakheart/nPhase ). nPhase is validated by tests on simulated and real polyploids. nPhase obtains on average over 95% accuracy and a contiguous 1.25 haplotigs per haplotype to cover more than 90% of each chromosome (heterozygosity rate ≥ 0.5%). nPhase allows population genomics and hybrid studies of polyploids.