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

Abstract

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.

Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease

“…28 Most GWASs list the nearest gene for the locus; however, mechanistic research indicates that the nearest gene is often not causal, and identification of GWAS targets remains difficult. 29,30 The expression quantitative trait loci (eQTL) method has been developed to associate genetic variation with gene-expression changes. The method requires genotype-and tissue-specific gene-expression data from large numbers of healthy individuals and employs controlled computational methods to identify genotypedriven gene expression changes.…”

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

“…We first tested MaxMIF's ability to differentiate drivers from passengers in six Pan‐Cancer datasets, namely, AWG, bcgsc, bcm, broad, ucsc, and wustl, provided by different research groups from the TCGA consortium (see the details in Table S1, Supporting Information), using two independently developed PPI networks HumanNet24 and STRINGv10 25. We compared the 12 prioritizing results with those obtained by DNmax and DNsum (two algorithms in MUFFINN)21 using the same data and the same five reference cancer gene sets, that is, CGC (Cancer Genome Census),26 CGCpointMut, Rule2020,5 HCD,27 and MouseMut28, 29 (see the Supporting Information for details), with CGC being the most well‐known and confident cancer gene set.…”

“…Two independently developed PPI datasets HumanNet24 and STRINGv1025 were downloaded from their respective websites. Each of the interaction weight between two proteins was extracted and standardized with a value ranging from 0 to 1 and divided it by the largest weight.…”

netSmooth: Network-smoothing based imputation for single cell RNA-seq

“…We constructed a gene network from HumanNet 11 , a functional gene network where edges denote interactions between two genes. We constructed a smoothing graph by taking all edges from HumanNet, and producing a graph where all edge weights are set to 1.…”