Engineering polygenic traits in S. cerevisiae

The yeast Saccharomyces cerevisiae is used as a eukaryotic model organism in basic research and for biotechnological applications. Thousands of different yeast strains are stored in strain collections and due to their genetic diversity they represent an enormous pool of phenotypic variability. Most of the biotechnologically important phenotypes, such as tolerance to various environmental stresses and efficiency in the production of certain metabolites or proteins, are polygenic. Therefore, the identification of the genes that contribute to such a phenotype and its quantitative transfer between strains is not as simple as for Mendelian traits. However, the development of advanced methods for the analysis and editing of genomes in the last two decades, together with the availability of large strain collections, has paved the way for the characterization of the causal alleles for complex traits and for their rapid transfer to any strain of choice. In this project we aim at the characterization of the optimal set of genes for several polygenic traits, by exploitation of the genetic and phenotypic diversity of a large collection of yeast strains. The results will be used to optimize strains that have already been engineered for the synthesis of products of biotechnological importance. We will select subsets of genetically and phenotypically diverse strains from a collection of more than 1000 strains to investigate the relevant traits. An adapted variant of the synthetic genetic array procedure and extreme quantitative trait loci analysis, together with the continuous analysis of the phenotypes of interest, will be used in a sequential crossing procedure to accumulate the beneficial alleles of all starting strains in one segregant. The results of this proof-of-principle study will not only improve our understanding of the investigated phenotypes, but they will also illustrate the potential of the use of large strain collections for such studies in general. Finally, the possibility to determine a set of causal alleles required for a certain phenotype will be important for the engineering of improved industrial strains.