Periodic Reporting for period 4 - PhenomeNal (Inheritance, expressivity and epistasis hidden behindthe phenotypic landscape of natural populations)
Periodo di rendicontazione: 2023-03-01 al 2024-08-31
Natural populations present an astonishing diversity of phenotypic variation in terms of morphology, physiology, behavior, and disease susceptibility. One major goal in biology is to identify the genetic causes of trait variation. It is now clear that the understanding of phenotypes is not only hampered by non-heritable factors such as the environment and epigenetic variation, but it is also confounded by the lack of complete knowledge concerning the genetic components of complex traits. This is increasingly evident as shown by recent studies of genotype-phenotype correlations in humans and other higher model eukaryotes such as A. thaliana and C. elegans, where identified causal loci in genome-wide association studies (GWAS) explained relatively little of the heritability of most complex traits. Multiple justifications for this “missing heritability” have been suggested, including a large number of variants with small effects, rare variants, poorly detected structural variants and low power to estimate gene-gene interactions.
Dissecting the genotype-phenotype relationship in humans, while extremely important, is very difficult not only due to genetic complexity, pleiotropy, and gene-environment interactions but also by large and complex genomes. As a consequence, the completion of an exhaustive catalogue of genetic variants is still far from being reached. The basic understanding of phenotypic diversity and the genetic architecture underlying it is facilitated by the use of microbial models, in particular the budding yeast S. cerevisiae with its physically small (12 Mb), genetically large (4,500 cM), and highly annotated genome. Interestingly, S. cerevisiae has decidedly greater genetic diversity than humans. Phenotypic diversity among yeast strains is significant, and variation is apparent among natural isolates at different levels. In addition, it is possible to study, test, and precisely measure a large number of phenotypes on the same set of strains. Due to its genome size and currently available tools, the genetic basis of phenotypic diversity is more readily identified and thoroughly characterized in S. cerevisiae than in higher eukaryotes.
A better understanding of the phenotype-genotype relationship requires a deeper knowledge of the effect of genetic variants across a large number of individuals. This is stating the obvious but the question is how can we reach this. Deeper mapping studies by linkage or association, as mentioned previously, will definitely bring some insight into this problem but will be insufficient. Alternative and new strategies are now essential and necessary.
In the framework of this project, we decided to marry classical but high-throughput genetic methods with new approaches based on population genomics to connect the phenotypic and allelic landscapes by taking advantage of the powerful budding yeast model system.
Dissecting the genotype-phenotype relationship in humans, while extremely important, is very difficult not only due to genetic complexity, pleiotropy, and gene-environment interactions but also by large and complex genomes. As a consequence, the completion of an exhaustive catalogue of genetic variants is still far from being reached. The basic understanding of phenotypic diversity and the genetic architecture underlying it is facilitated by the use of microbial models, in particular the budding yeast S. cerevisiae with its physically small (12 Mb), genetically large (4,500 cM), and highly annotated genome. Interestingly, S. cerevisiae has decidedly greater genetic diversity than humans. Phenotypic diversity among yeast strains is significant, and variation is apparent among natural isolates at different levels. In addition, it is possible to study, test, and precisely measure a large number of phenotypes on the same set of strains. Due to its genome size and currently available tools, the genetic basis of phenotypic diversity is more readily identified and thoroughly characterized in S. cerevisiae than in higher eukaryotes.
A better understanding of the phenotype-genotype relationship requires a deeper knowledge of the effect of genetic variants across a large number of individuals. This is stating the obvious but the question is how can we reach this. Deeper mapping studies by linkage or association, as mentioned previously, will definitely bring some insight into this problem but will be insufficient. Alternative and new strategies are now essential and necessary.
In the framework of this project, we decided to marry classical but high-throughput genetic methods with new approaches based on population genomics to connect the phenotypic and allelic landscapes by taking advantage of the powerful budding yeast model system.
To lay the foundation of the exploration of the genetic complexity of traits, we had completely sequenced and phenotyped a large collection of 1,011 natural yeast isolates, coming from diverse ecological niches across the world on plates containing different stressors. The generated dataset revealed an undescribed evolutionary history as well as the driving forces of genome evolution, and has provided insights into the genotype–phenotype relationship. It allowed to performed GWAS (genome-wide association analyses) in S. cerevisiae and provided a population genomic resource at a scale that matches those of other model organisms. The difference between the estimated genome-wide heritability and explained phenotypic variance gives an overview of the extent of missing heritability. Many SNPs (Single Nucleotide Polymorphims) are present at low frequencies, which echoes observations previously made in human GWAS and raises the question of whether rare SNPs have an important role in modulating the phenotypic landscape. These genome-wide association analyses, including an exhaustive catalog of genome content and CNVs (Copy Number Variants) present in the 1,011 genomes, highlighted the overall importance of these genetic variants on the phenotypic diversity. Finally, this resource laid the foundation for the next step of our research proposal, i.e. dissecting the genetic complexity of traits.
Based on the genomic and phenotypic data from the collection of 1,011 S. cerevisiae isolates, we selected a total of 55 natural isolates and we constructed a diallel by intercrossing these isolates. These strains were chosen to span much of the genetic diversity in the native range of the species. In total, we generated 3,025 hybrids, representing 2,970 heterozygous hybrids with a unique parental combination and 55 homozygous hybrids. All these hybrids were screened for high-resolution quantification of mitotic growth ability across 53 conditions including different carbon sources and chemical compounds impacting various physiological and cellular responses. In total, more than 160,00 hybrid/trait combinations were determined. Using this diallel panel, we performed GWAS and quantified the contribution of low-frequency variants across the selected growth conditions and found that among all the genetic variants detected by GWAS, 16.3% of them have a low-frequency in the initial population and explain a significant part of the phenotypic variance (21% on average). This particular diallel design also presented an intrinsic power to evaluate the additive vs. non-additive genetic components contributing to the phenotypic variation. We assessed the effect of intra-locus dominance on the non-additive genetic component and showed that dominance at the single locus level contributed to the phenotypic variation observed. Altogether, these results have major implications for our understanding of the genetic architecture of traits as they highlighted the extensive role of low-frequency variants on the phenotypic variation.
This year, we took advantage of the diallel panel to assess the overall genetic complexity of traits and the prevalence of phenotypic expressity at a population-scale. In this context, we first selected a subset of the large diallel hybrid panel in order to have 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 (corresponding to 40 full tetrads) was obtained, leading to a total of 30,400 offspring individuals. Their mitotic growth has been assessed on 40 growth conditions inducing various cellular stress. As the phenotypic distribution of the offspring of a given cross allows to infer the inheritance patterns to a trait, we assessed the inheritance patterns for 3,841 cross/trait combinations and revealed that while complex inheritance were the most common, 11% of the cross/traits combinations had their phenotypic variation controlled by a single gene with a large effect and 4% displayed digenic interactions. We identified 26 major effect loci on various traits and parental genetic backgrounds. Measurement of the extent of expressivity was performed by investigating the variation of inheritance patterns throughout all the crosses having one parent who carries one of these loci. We found that trait complexity was highly dynamic and tightly linked to the genetic background. Indeed, 22 out of the 26 major effect loci were subjected to various level of expressivity with one to nine crosses showing departure from monogenic inheritance.
Altogether, the obtained results allowed us to have a first new insight into the genetic architecture of complex traits.
Based on the genomic and phenotypic data from the collection of 1,011 S. cerevisiae isolates, we selected a total of 55 natural isolates and we constructed a diallel by intercrossing these isolates. These strains were chosen to span much of the genetic diversity in the native range of the species. In total, we generated 3,025 hybrids, representing 2,970 heterozygous hybrids with a unique parental combination and 55 homozygous hybrids. All these hybrids were screened for high-resolution quantification of mitotic growth ability across 53 conditions including different carbon sources and chemical compounds impacting various physiological and cellular responses. In total, more than 160,00 hybrid/trait combinations were determined. Using this diallel panel, we performed GWAS and quantified the contribution of low-frequency variants across the selected growth conditions and found that among all the genetic variants detected by GWAS, 16.3% of them have a low-frequency in the initial population and explain a significant part of the phenotypic variance (21% on average). This particular diallel design also presented an intrinsic power to evaluate the additive vs. non-additive genetic components contributing to the phenotypic variation. We assessed the effect of intra-locus dominance on the non-additive genetic component and showed that dominance at the single locus level contributed to the phenotypic variation observed. Altogether, these results have major implications for our understanding of the genetic architecture of traits as they highlighted the extensive role of low-frequency variants on the phenotypic variation.
This year, we took advantage of the diallel panel to assess the overall genetic complexity of traits and the prevalence of phenotypic expressity at a population-scale. In this context, we first selected a subset of the large diallel hybrid panel in order to have 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 (corresponding to 40 full tetrads) was obtained, leading to a total of 30,400 offspring individuals. Their mitotic growth has been assessed on 40 growth conditions inducing various cellular stress. As the phenotypic distribution of the offspring of a given cross allows to infer the inheritance patterns to a trait, we assessed the inheritance patterns for 3,841 cross/trait combinations and revealed that while complex inheritance were the most common, 11% of the cross/traits combinations had their phenotypic variation controlled by a single gene with a large effect and 4% displayed digenic interactions. We identified 26 major effect loci on various traits and parental genetic backgrounds. Measurement of the extent of expressivity was performed by investigating the variation of inheritance patterns throughout all the crosses having one parent who carries one of these loci. We found that trait complexity was highly dynamic and tightly linked to the genetic background. Indeed, 22 out of the 26 major effect loci were subjected to various level of expressivity with one to nine crosses showing departure from monogenic inheritance.
Altogether, the obtained results allowed us to have a first new insight into the genetic architecture of complex traits.
As mentioned previously, the genetic variants identified via studies of genotype-phenotype correlations in humans and other model eukaryotes such as genome-wide association studies (GWAS) explain relatively little of the heritability of most complex traits. Multiple justifications for this “missing heritability” have been suggested, including the presence of rare variants as well as the low power to estimate non-additive effects. The contribution of rare and low-frequency variants to traits is largely unexplored. In humans, these genetic variants are widespread but only few of them were associated with some specific traits and diseases. Recent studies have highlighted the role of rare variants but reached conflicting conclusions about their contribution. Crucially, human studies must rely on genome-wide statistical estimates of this quantity from mixed models, because they are unable to identify causal rare variants, and they confound variant allele frequency and effect size by virtue of the study designs. Our study overcomes these limitations and allowed us to compare the contributions of rare and common variants at many specific mapped loci and with unbiased estimates of effect sizes. Analyses of the generated diallel hybrid panel allowed for a population-level sampling of the genetic and phenotypic diversity within the S. cerevisiae species. We found that additive genetic components explain the majority of phenotypic variation at a population-scale, with additivity and non-additivity explaining 51% and 29% on average, respectively. Interestingly, as our hybrid panel consists of heterozygous diploids, we also could assess the effect of intra-locus dominance on the non-additive genetic component. Finally, by performing GWAS on the diallel panel, we showed that 16.3% of all the associated variants have a low-frequency in the initial population, and these variants explain a significant portion of the phenotypic variance (21% on average). These results clearly show that low-frequency variants contribute to a large part of the phenotypic variation observed in a population.