Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

By Insuk Lee, Martin Blom, Peggy I. Wang, Jung Eun Shim, Edward M. Marcotte. 2011

687 citations recorded by the source corpus.

Network "guilt by association" (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK-STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics (2016, methodology): “A common aggregation method is to use only the most significant SNP within a window encompassing the gene of interest, for example by assigning the maximum-of-chi-squares (MOCS) as the gene score statistic[3,13] (the contributing chi-squared values can be obtained from SNP p-values by using the inve”
STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets (2018, background): “The STRING database (‘Search Tool for Retrieval of Interacting Genes / Proteins’) belongs to this latter class, along with GeneMania (22), FunCoup (23), I2D (24), ConsensusPathDb (25), IMP (26) and HumanNet (27)––most of which have recently been reviewed and benchmarked in (7).”
Inductive matrix completion for predicting gene–disease associations (2014, background): “Recently, predicting gene–disease links based on network analysis has become popular (Lee et al., 2011; Li and Patra, 2010; Linghu et al., 2009; Singh-Blom et al., 2013; Vanunu et al., 2010; Wu et al., 2008).”
Network-based stratification of tumor mutations (2013, methodology): “First, we established a set of network communities (i.e., connected components enriched for edges shared within community members) in the input network (STRING, HumanNet or PathwayCommons) using the network community detection algorithm QCut 61 .”
The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible (2016, methodology): “…sets by ‘network smoothing’ (10,11), help define drug efficiency by network-based ‘drug-disease proximity measures’ (12), help to interpret the results of genome-wide association screens (13–17) and enable the discovery of new molecular players through the ‘guilt by association’ concept (18,19).”
deepNF: deep network fusion for protein function prediction (2017, methodology): “Most previous approaches for network integration either use probabilistic methods, like Bayesian inference [11, 17] or kernel-based methods [34, 21] to fuse different protein-protein network types, derived from different proteomic and genomic data sources, into a single network.”
Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders (2017, background): “…(Adzhubei et al., 2013; Moreau and Tranchevent, 2012; Sim et al., 2012; Thusberg et al., 2011), protein interaction network properties (Barabási et al., 2011), guilt-by-association in multi-omic studies (Lee et al., 2011), and patterns of gene expression (Butler et al., 2015), to name but a few.”
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks (2012, background): “Many state of the art algorithms for the gene prioritization problem use protein interaction or functional linkage networks [8,17,18], exploiting the tendency of genes causing similar diseases to lie close to one another in the network [16,19,20].”
Integrative approaches for finding modular structure in biological networks (2013, background): “…biomarkers derived through integrative network analyses also provide superior predictive performance in disease classification, especially when compared with single-gene disease markers that have been routinely annotated through genome-wide association studies (GWASs) 38,62,71,72,163,164 .”
TRRUST: a reference database of human transcriptional regulatory interactions (2015, methodology): “To measure the functional modularity of a group of target genes, we leveraged a genome-wide functional network for humans, HumanNet 18 .”
TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions (2017, methodology): “While network visualization for human TF target genes is based on HumanNet (10) (Figure 3A), that for mouse TF target genes is based on MouseNet v2 (11) (Figure 3B).”