Genotype-by-environment interactions and causal gene fine-mapping for quantitative trait variation in yeast

Abstract

Genetic variation among individuals influences many important traits, including common human disease. Quantitative trait locus (QTL) mapping in model organisms has revealed that most quantitative traits are affected by multiple QTLs throughout the genome, and are therefore “complex” traits. Complex traits also include those affected by genotype-by-environment interactions (GxE), where the effect of a genetic variant on a trait depends on the environment. GxE influences numerous organismal traits across eukaryotic life. However, we have a limited understanding of how GxE shapes the molecular processes that give rise to organismal traits. This work covers two main areas using the yeast Saccharomyces cerevisiae: GxE through QTL mapping for the trait of protein degradation and dissecting QTLs to gene-level resolution. We characterized how GxE shapes protein degradation, an essential molecular process that influences numerous aspects of cellular and organismal physiology. We characterized GxE in the activity of the ubiquitin-proteasome system (UPS), the primary protein degradation system in eukaryotes. By mapping genetic influences on the degradation of six substrates that engage multiple distinct UPS pathways across eight diverse environments, we discovered extensive GxE in the genetics of UPS activity. Hundreds of locus effects on UPS activity varied depending on the substrate, the environment, or both. Most of these cases corresponded to loci that were present in one environment but not another (“presence / absence” GxE), while a smaller number of loci had opposing effects in different environments (“sign change” GxE). The number of loci exhibiting GxE, their genomic location, and the type of GxE (presence / absence or sign change) varied across UPS substrates. Loci exhibiting GxE were clustered at genomic regions that contain core UPS genes and especially at regions containing variation that affects the expression of thousands of genes, suggesting indirect contributions to UPS activity. Our results reveal highly complex interactions at the level of substrates and environments in the genetics of protein degradation.Identifying the causal genes within QTLs (quantitative trait genes; QTGs) is challenging because most QTLs are wide and can contain dozens of genes. Experimental fine-mapping approaches typically test causality one gene at a time. This process is both laborious and potentially biased towards genes previously shown to affect the trait. To systematically identify QTGs with high-throughput, we applied the reciprocal heterozygosity (RH) test genome-wide using transposon mutagenesis for the model complex trait of growth in a hybrid of two genetically different S. cerevisiae strains. We used Illumina sequencing of transposon insertion sites to count insertions at each open reading frame (ORF). Out of the 4,784 ORFs that carry DNA variants between the two parental strains of the hybrid, 4,440 contained at least one insertion, with 4,260 ORFs containing at least one insertion in both alleles. Using a custom computational pipeline and linear modeling, we identified 265 genes with at least a nominally significant (p < 0.05) allelic effect on growth, however, experimental validation did not recapitulate the results of the scan. Here, I discuss multiple possibilities for this result and propose improvements to the approach. Directly mapping QTGs will aid in understanding how genetic variation affects important cellular traits, such as growth, and can be readily applied to other phenotypes. This work contributes valuable insights to the field complex trait genetics by elucidating the genetic architecture of UPS activity in yeast. Furthermore, an approach to directly map QTGs will be fundamental in understanding causal mechanisms behind complex traits, and I have laid the groundwork to apply this approach to a hybrid of two S. cerevisiae strains.