Quantitative Epistasis Analysis and Pathway Inference from Genetic Interaction Data

Abstract

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ∼80% of the known relationships without any false positives.

The potential power of dynamics in epistasis analysis

“…Genepath [19] analysis. Many have also adopted the utilization of mathematical and logical models to represent the genotypic-phenotypic relationship, such as flux balance analysis [17,24,27], generalized linear model regression [20] and multi linear regression [21].…”

“…We now examine a classical method from genetics, which in its simplest form can be performed by handepistasis analysis [11,12,13]. Epistasis is a vital tool in functional genomics to enhance our understanding of the GRN components and their order of action [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

“…Traditional uses of epistasis to order genes within a pathway have become progressively quantitative. Quantitative phenotypic measures involves using a method to quantify and measure genetic interactions for various phenotypic traits, such as growth rate (fitness) [20,21], gene expression [21,22] or unfolded protein response in endoplasmic reticulum of yeast [23].…”

“…The absence or presence of genes in a network and their response to external stimuli (if any) influence the behavior of the network. To predict the behavior of a network, scientists may observe an output trait (or phenotypic consequence) produced by the genetic pathway in response to an external stimuli [11,19,20,21,22]. While many genes have been discovered, there still exists situations where the activity of the genes and the direction of regulation remain unknown.…”

“…More specifically, it refers to situations where the outcome of deleting two genes is "surprising" compared to the results of deleting each gene individually. While classical epistasis has yielded deeper insights on numerous genetic pathways [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], it is not without limitations. One limitation is that it is not always possible to infer the relationship between a pair of genes using epistasis analysis.…”