High-throughput identification of causal variants underlying quantitative traits in yeast

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PROJECT SUMMARY / ABSTRACT The broad objective of the proposed research is to achieve comprehensive dissection of the genetic basis of many complex phenotypes in the yeast S. cerevisiae, arguably the most powerful eukaryotic model system due to its small genome, ease of genetic manipulation, and the ability to generate very large sample sizes. Evolutionary conservation has also ensured that many yeast traits have direct parallels to biomedically important human phenotypes. We seek comprehensively identify the DNA sequence variants underlying a variety of traits, study the distribution of their effect sizes and their frequencies in a population, and build rules for predicting the functional effects of variants of unknown significance. Success in answering these questions will provide critical guidance for the design of genotype-phenotype studies in humans and other organisms of medical, biological, and agricultural interest, and enable improved diagnostic accuracy based on genome sequencing of patients. Specifically, will use a resource we built that consists of nearly 15,000 genotyped and phenotyped segregants from crosses between 16 diverse yeast strains to identify causal genes and prioritize individual putative causal genetic variants. We will then identify specific causal variants by directly engineering thousands of candidate variants in bulk. We will use methods we have developed for massively parallel targeted editing by CRISPR/Cas9 to engineer pools of yeast cells, each carrying one of 2000 natural variants. We will subject the edited pools of yeast cells to selective conditions and track the phenotypic consequences of the introduced variants over time by short read sequencing of DNA barcodes identifying each edit. We will then extend massively parallel targeted editing to generate all variants discovered in the panel of 16 diverse yeast strains. We will assay the effects of single nucleotide polymorphisms (SNPs), small scale insertions or deletions (indels), and haplotype effects of closely linked variants. We will include non-coding variants to better understand their effects on fitness. We will extend our engineering toolkit to employ versions of Cas9 and related enzymes that have different recognition sites, and by using Cas9-based “base editors” that allow generation of specific classes of mutations. Ultimately, we will edit and measure the phenotypic consequences of hundreds of thousands of natural genetic variants, which will provide a deep understanding of the genetic basis of many traits and enable us to develop accurate algorithms that predict the functional effect of any genetic variant.