Most human traits are complex/quantitative. Similarly, many common human diseases are complex; they typically are not caused by a small number of genes, but instead are influenced by hundreds if not thousands of genes. Little is known about quantitative traits due to conceptual, experimental, and analytical limitations. This proposal aims to address several key questions: 1) what are the genes that can drive a quantitative trait and how are they interrelated, 2) what are the genes that drive variation in a quantitative trait in natural populations, and 3) how do the phenotypes of each individual quantitative gene combine to determine the overall phenotype of the trait, i.e. are gene-gene interactions important. The induction of galactose and phosphate metabolic genes in the budding yeast Saccharomyces cerevisiae are classical Eukaryotic model systems for probing signaling. Preliminary results described in this proposal show that these responses are also complex traits. Our laboratory has developed high- throughput flow cytometry methods that are essential for accurately determining the effects of genes on quantitative traits both among natural variants and mutant strains. Building on our experimental strengths, we will combine fluorescence reporter strains with a series of deletion or dosage perturbation libraries. We will generate the most comprehensive list of quantitative genes yet in each of these traits, and assess the interplay of these quantitative genes within and between traits. Using allele swaps combined with bulk segregant analysis and classical linkage we will determine the extent to which alleles of quantitative genes vary in nature. By combining between zero to four alleles or deletion of quantitative genes, we will be able to directly test the importance of gene-gene interactions. This combination of approaches should greatly enhance our understanding of complex traits and have direct relevance for human disease.
Many human diseases are complex; many genes can influence the probability of disease, and each such gene may have only a small effect. Our knowledge of complex quantitative traits is poor. This project aims to use a simple model system, in which high- throughput experiments are relatively easy, to determine principles of quantitative traits.
| Hua, Bo; Springer, Michael (2018) Widespread Cumulative Influence of Small Effect Size Mutations on Yeast Quantitative Traits. Cell Syst 7:590-600.e6 |
| Richard, Magali; Chuffart, Florent; Duplus-Bottin, Hélène et al. (2018) Assigning function to natural allelic variation via dynamic modeling of gene network induction. Mol Syst Biol 14:e7803 |
| Lee, Kayla B; Wang, Jue; Palme, Julius et al. (2017) Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes. PLoS Genet 13:e1006766 |
