A central challenge in biology is understanding the genetic basis of variation in complex, quantitative traits. In the yeast S. cerevisiae, environmental cues induce specific developmental responses including biofilm formation, filamentous growth, and sporulation. Natural yeast isolates vary dramatically in induction and extent of these phenotypes. The highly conserved cAMP/PKA cell signaling pathway is intimately involved in cellular differentiation, with signaling activity directly influencing all tree phenotypes. In this proposal, I outline a novel "candidate network" approach to characterize naturally occurring variation in the cAMP/PKA pathway in order to define how single locus and epistatic interactions contribute to the genetic architecture of key developmental phenotypes. First, I will identify natural variation likely to affect signaling in the cAMP/PKA pathway in a se of 93 yeast isolates (Aim 1). Preliminary analyses demonstrate abundant variation within all genes in this pathway, both in protein-coding and non-coding regions. I will adapt existing methods for predicting the functional effects of mutations and select 100 variants with strong predicted effects on function for further characterization. I will individually assess the functional impact f variants identified in Aim 1 in a common genetic background (Aim 2). Specifically, I will construct strains differing only at the nucleotide(s) in question, and will measure biofilm formation, filamentous growth, and sporulation efficiency in these strains. Finally, I will quantif the epistatic landscape of large-effect loci in the cAMP/PKA pathway (Aim 3). I will focus on two-locus epistatic interactions between the eight alleles with the largest effects on developmental phenotypes as identified in Aim 2. I will measure epistasis directly in a single genetic background and estimate it statistically across a variety of haploid genetic backgrounds. I will use F1 hybrids of the 93 yeast isolates to test whether the direction and magnitude of epistasis estimated for each pair of alleles is recapitulated across a large population of genetically divers diploid individuals. This exhaustive characterization of epistatic interactions will provide an unmatched view of the epistatic landscape of a core cellular pathway. Overall, this research will help to illuminate the genetic architecture of key developmental phenotypes that serve as model quantitative traits and will better define the functional effects of variants found in natural populations. Moreover, dysregulation of this pathway is common in several types of cancer, and an improved understanding of functional variation in the pathway could suggest novel strategies for cancer treatment.
Humans show quantitative variation for countless heritable traits with relevance to public health, such as blood pressure, cancer susceptibility, and drug metabolism and response. This project seeks to examine genetic variation in a molecular pathway involved in yeast cellular decision making that is conserved across organisms and known to play a role in human cancers. My characterization of the functional effects of genetic variation in this pathway will help us to better understand how specific genetic variants underpin trait variation and could suggest novel strategies for treating cancer.