Predicting genetic modifier loci using functional gene networks

Abstract

Most phenotypes are genetically complex, with contributions from mutations in many different genes. Mutations in more than one gene can combine synergistically to cause phenotypic change, and systematic studies in model organisms show that these genetic interactions are pervasive. However, in human association studies such nonadditive genetic interactions are very difficult to identify because of a lack of statistical power-simply put, the number of potential interactions is too vast. One approach to resolve this is to predict candidate modifier interactions between loci, and then to specifically test these for associations with the phenotype. Here, we describe a general method for predicting genetic interactions based on the use of integrated functional gene networks. We show that in both Saccharomyces cerevisiae and Caenorhabditis elegans a single high-coverage, high-quality functional network can successfully predict genetic modifiers for the majority of genes. For C. elegans we also describe the construction of a new, improved, and expanded functional network, WormNet 2.Using this network we demonstrate how it is possible to rapidly expand the number of modifier loci known for a gene, predicting and validating new genetic interactions for each of three signal transduction genes. We propose that this approach, termed network-guided modifier screening, provides a general strategy for predicting genetic interactions. This work thus suggests that a high-quality integrated human gene network will provide a powerful resource for modifier locus discovery in many different diseases.

Extensive complementarity between gene function prediction methods

“…This reveals a considerable complementarity between the different methods: if methods are applied individually, some gene functions may be predicted highly accurately while the others not at all. A combination of genome sequence-based predictors is able to reach across many different GO functions, consistent with the success of past approaches that integrate across large-scale experimental data sources (Troyanskaya et al, 2003;Lee et al, 2004;Lanckriet et al, 2004;von Mering et al, 2005;Hu et al, 2009;Lee et al, 2010).…”

“…This was made evident in the analyses of gene/protein functional association networks, constructed using various sources of large-scale data. Integrating the individual networks resulted in gene modules that were more functionally consistent (Lee et al, 2004;von Mering et al, 2005) and could thus more accurately predict gene function (Troyanskaya et al, 2003;Hu et al, 2009) or phenotypic effects of gene perturbation (Lee et al, 2010).…”

Protein Interactome and Its Application to Protein Function Prediction

“…They both give useful information but should be separated according to the relevant evidence codes. There are also species-specific functional interaction databases (Lee et al, 2011;Lee et al, 2010a We have listed some of the major primary databases, meta-databases, and functional databases in Table 2. Comparisons among the primary databases are shown in Table 3.…”

Analysis of Protein Interaction Networks to Prioritize Drug Targets of Neglected-Diseases Pathogens

“…Thus, new approaches that integrate other types of data, including protein-protein interactions, text mining, homology-based, and functional genomics approaches (Lee et al, 2004, Chua et al, 2007, Lee et al, 2008a, Pena-Castillo et al, 2008, Linghu et al, 2009, Lee et al, 2010, Wu et al, 2010, Lee et al, 2011, Szklarczyk et al, 2011, have shown to be the most effective way to assign function to uncharacterized proteins that are components of the network (Fig. 7).…”

“…This approach to predict the protein/gene function is known as "guilty by association". Additionally, the integration of information related to diseases or specific phenotypes with network approaches also enhances the understanding of human diseases, pharmacology response, and phenotype prediction (Ideker and Sharan 2008, Lee et al, 2008a, Lee et al, 2010, Wang and Marcotte 2010, Lee et al, 2011.…”