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 small as 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.

Public Health Relevance

NARRATIVE Relevance to public health: Genetic factors underlie susceptibility to virtually every human disease, and much of current biomedical research is based on the expectation that identifying these factors is a crucial step in improving diagnosis, prevention, and treatment. Identification is difficult because the genetic basis of common disorders is complex, with disease susceptibility influenced by multiple genes. The proposed research will improve our understanding of genetic complexity and provide critical guidance for studies of the genetic basis of common human diseases. The proposed research will also provide insights into the genetic basis of responses to drugs and stressful conditions.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Eckstrand, Irene A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Princeton University
Organized Research Units
United States
Zip Code
Albert, Frank W; Muzzey, Dale; Weissman, Jonathan S et al. (2014) Genetic influences on translation in yeast. PLoS Genet 10:e1004692
Albert, Frank W; Treusch, Sebastian; Shockley, Arthur H et al. (2014) Genetics of single-cell protein abundance variation in large yeast populations. Nature 506:494-7
Breunig, Jeffrey S; Hackett, Sean R; Rabinowitz, Joshua D et al. (2014) Genetic basis of metabolome variation in yeast. PLoS Genet 10:e1004142
Bloom, Joshua S; Ehrenreich, Ian M; Loo, Wesley T et al. (2013) Finding the sources of missing heritability in a yeast cross. Nature 494:234-7