Complex Genetic Interactions in a Quantitative Trait Locus

Abstract

Whether in natural populations or between two unrelated members of a species, most phenotypic variation is quantitative. To analyze such quantitative traits, one must first map the underlying quantitative trait loci. Next, and far more difficult, one must identify the quantitative trait genes (QTGs), characterize QTG interactions, and identify the phenotypically relevant polymorphisms to determine how QTGs contribute to phenotype. In this work, we analyzed three Saccharomyces cerevisiae high-temperature growth (Htg) QTGs (MKT1, END3, and RHO2). We observed a high level of genetic interactions among QTGs and strain background. Interestingly, while the MKT1 and END3 coding polymorphisms contribute to phenotype, it is the RHO2 3′UTR polymorphisms that are phenotypically relevant. Reciprocal hemizygosity analysis of the Htg QTGs in hybrids between S288c and ten unrelated S. cerevisiae strains reveals that the contributions of the Htg QTGs are not conserved in nine other hybrids, which has implications for QTG identification by marker-trait association. Our findings demonstrate the variety and complexity of QTG contributions to phenotype, the impact of genetic background, and the value of quantitative genetic studies in S. cerevisiae. Most of the differences in phenotype between unrelated members of a species are polygenic in nature. Because of their ubiquity and importance, these polygenic (or quantitative) traits have been intensively studied, and a variety of techniques have been proposed to identify and characterize quantitative trait genes (QTGs). Indeed, the main application of the recently published human HapMap project is to identify the genes responsible for diseases that are quantitative in nature. Using a well-defined Saccharomyces cerevisiae quantitative trait locus containing three QTGs (MKT1, END3, and RHO2), the authors used deletions to analyze the contributions of each gene to phenotype, singly and in combination, and found a variety of interactions. Expression analysis showed no difference in steady-state mRNA levels between alleles of the three genes. Homologous allele replacement identified the phenotypically relevant differences between alleles of each gene, which were single coding polymorphisms for two genes (MKT1 and END3) and the 3′ untranslated region of one gene (RHO2). Finally, analysis of multiple genetic backgrounds showed that the phenotypes conferred by these genetic variants were not conserved. The results show that the techniques proposed to identify QTGs, such as expression analysis and marker-trait association, have profound limitations, and that unbiased genome-wide approaches are needed to dissect quantitative traits. The results also demonstrate the complexity of the genetic interactions that affect quantitative traits and the value of the S. cerevisiae system in studying these traits.