Understanding the interplay between genomic Structural Variations and Phenotypes using the yeast model – PhenoVar

Understanding the genetic determinants of phenotypic variation is a major challenge in modern biology. The complexity of the genetic architecture controlling the traits varies from Mendelian inheritance to complex networks of interaction. The understanding of simple and complex traits is not only hampered by non-heritable factors such as the environment and epigenetic variation, but also confounded by the lack of complete knowledge concerning the genetic components of the traits. The causative genetic polymorphisms are not restricted to base substitutions, small insertions and deletions but also include large-scale Structural Variations (SVs) of chromosomes such as deletions, duplications, inversions and translocations.
There are mounting evidences that SVs play a major role in phenotypic variation. However, these genetic variants are the most difficult to identify and to interpret with respect to their functional consequences. In this context, several challenges are emerging from the fields of evolutionary and medical genomics. A first challenge is to obtain a complete, detailed and quantitative description of SVs in eukaryotic genomes. A second one is to mechanistically understand the functional impact of SVs on the phenotypic diversity. Finally, knowing to what extent the SVs functional consequences are modulated by both genetic background and environment is essential.
Although these questions have direct relevance to human health, they are difficult to address in higher eukaryotes because the realization of dedicated genetic experiments is limited by important technical and ethical difficulties. To overcome these limitations and to concomitantly go beyond the sole descriptive stages of genome sequencing, we propose to take advantage of the powerful yeast model to determine for the first time the functional landscape of SVs. The combination between the new single molecule sequencing methods and the small genome size of yeasts provides an unprecedented opportunity to obtain comprehensive maps of SVs at a base pair resolution in large cohort of strains selected to maximize the sampling of the species diversity.
In addition, we will use cutting edge genome editing technologies to generate large libraries of SVs in a given genetic background, which will allow disentangling the true phenotypic impact of SVs from the confounding contribution of single nucleotide variations (SNVs). These collections of SVs will be precisely phenotyped under multiple environmental conditions to test potential beneficial and/or deleterious fitness effects. Measures of offspring viability will provide direct estimate of the SVs cost and their role on the onset of reproductive isolation. The engineered libraries will be used to determine how the effects of SVs propagate genome-wide by measuring transcript and protein levels. These genome-wide omics datasets will provide mechanistic insights on how SVs impact phenotypes.
Furthermore, using the state-of-the-art quantitative genomic approaches, we will dissect how SVs interact with both the genetic background and the environment. Both designed crosses and analysis of wild populations will allow determining genetic and environmental modifiers.
The PhenoVar proposal represents the first attempt toward a deep understanding of the biological significance of SVs both at the structural and the functional levels. This project will have significant societal benefits in several areas. It has notably direct relevance to human health because SVs are associated with more than 30 developmental and hereditary disorders as well as being a hallmark of cancer. Most of the human diseases are complex traits and a better understanding of their genetic basis may lead to potential applications in personalized medicine. The PhenoVar proposal has also potential industrial applications since S. cerevisiae is a widely used microbe in both in traditional and modern biotechnology and SVs can be key target for trait improvement.