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 to answer many of the basic questions about the genetic architecture of complex traits, including the number of loci underlying a trait, the distribution of allelic effect sizes, the prevalence of genetic interactions, and the distribution f allele frequencies in a population. 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. Our proposal focuses on biomedically relevant traits, and will therefore allow us to leverage the power of yeast genetics to better understand human biology and disease. Specifically, will generate a mapping panel of 4000 individual segregants for the well-studied BYxRM cross, as well as panels of 1000 segregants for 20 other crosses, chosen with a modified round-robin design in which each of 20 strains will be crossed to two other strains. We will genotype these panels by very highly multiplexed low-coverage whole-genome sequencing. We will then phenotype the panels using colony growth assays probing an extensive space of cell physiology. We have developed high-throughput automated phenotyping assays that will enable us to measure growth of tens of thousands of strains in hundreds of conditions over the proposed project period. The growth conditions we propose to test include antifungals, chemotherapeutics, nutrient depletion, small molecules that target specific cellular processes, and treatments that have been shown to be yeast 'phenologs' of disease-related human phenotypes. We will use these data to estimate broad-sense and narrow-sense heritability of each trait, carry out linkage analysis to detect loci with additive an epistatic effects, measure the distribution of effect sizes, and compute the fraction of heritabiliy explained by the detected loci. We will attempt to identify candidate genes and variants underlying the detected loci, and validate a subset of these with molecular genetics techniques such as allele replacements. We will select 10 highly heritable traits with substantial 'missing heritability', and use X-QTL to detect loci with smaller effects than possible with other approaches, and to improve the mapping resolution of already identified loci. We will examine the additive and interaction effect sizes of the new loci detected by X-QTL, build multiple-QTL models, and assess the fraction of heritability that can be explained by loci with effects as smallas 0.1% of phenotypic variance. Quantitative trait genes and nucleotides we identify will also be resequenced across a large panel of diverse yeast strains in order to determine the relative contributions of common and rare polymorphisms to complex trait variation in yeast, as well as to examine the allelic complexity of functional variation in these genes. Our studies will provide a broad view of genetic architectures of many complex traits and a very deep understanding of a subset of traits.
| Sadhu, Meru J; Bloom, Joshua S; Day, Laura et al. (2018) Highly parallel genome variant engineering with CRISPR-Cas9. Nat Genet 50:510-514 |
| Albert, Frank Wolfgang; Bloom, Joshua S; Siegel, Jake et al. (2018) Genetics of trans-regulatory variation in gene expression. Elife 7: |
| Jerison, Elizabeth R; Kryazhimskiy, Sergey; Mitchell, James Kameron et al. (2017) Genetic variation in adaptability and pleiotropy in budding yeast. Elife 6: |
| Ehrenreich, Ian M (2017) Epistasis: Searching for Interacting Genetic Variants Using Crosses. Genetics 206:531-535 |
| Forsberg, Simon K G; Bloom, Joshua S; Sadhu, Meru J et al. (2017) Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast. Nat Genet 49:497-503 |
| Sadhu, Meru J; Bloom, Joshua S; Day, Laura et al. (2016) CRISPR-directed mitotic recombination enables genetic mapping without crosses. Science 352:1113-6 |
| Bloom, Joshua S; Kotenko, Iulia; Sadhu, Meru J et al. (2015) Genetic interactions contribute less than additive effects to quantitative trait variation in yeast. Nat Commun 6:8712 |
| Treusch, Sebastian; Albert, Frank W; Bloom, Joshua S et al. (2015) Genetic mapping of MAPK-mediated complex traits Across S. cerevisiae. PLoS Genet 11:e1004913 |
| Breunig, Jeffrey S; Hackett, Sean R; Rabinowitz, Joshua D et al. (2014) Genetic basis of metabolome variation in yeast. PLoS Genet 10:e1004142 |
| Albert, Frank W; Muzzey, Dale; Weissman, Jonathan S et al. (2014) Genetic influences on translation in yeast. PLoS Genet 10:e1004692 |
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