Abstract

Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.

Highlights

  • Systematic screening of the phenotypic effects of combining pairs of mutations, relative to those of the single mutations, provides detailed information on the structure and function of genetic interaction networks [1]

  • The phenotypic suppression (PS) category is composed of the known positive interaction pairs (Sabw0), whereas the phenotypic enhancement (PE), synthetic sick (SS), and synthetic lethal (SL) categories represent with pairs of increasing degrees of negative interactions (Sabv0), with SL being the extreme case

  • At the time of the present analysis, the interactions extracted from this dataset were not yet stored in the BioGRID database, making the evaluation unsupervised in the sense that the information on the interaction classes is totally independent of the data used in their detection

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Summary

Introduction

Systematic screening of the phenotypic effects of combining pairs of mutations, relative to those of the single mutations, provides detailed information on the structure and function of genetic interaction networks [1]. Large-scale screening approaches for synthetic lethal interactions, such as those based on synthetic genetic arrays (SGA) or the diploid synthetic lethality analysis by microarray (dSLAM), have successfully been used in the past to map synthetic lethal interaction networks in model organisms such as yeast [2,3]. These system-level maps have greatly improved our understanding of how mutations in different genes interact with one another to produce synthetic lethal or sick phenotypes [4,5,6,7]. These large-scale genetic interaction screening efforts are providing a new understanding of how genes function as networks to regulate cellular processes, either by enhancement or suppression, holding much promise for addressing many fundamental questions, such as buffering of genetic variation and evolution of complex diseases [13,14,15,16]

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