Meta-analysis of heterogeneous data sources for genome-scale identification of risk genes in complex phenotypes

Abstract

Meta-analyses of large-scale association studies typically proceed solely within one data type and do not exploit the potential complementarities in other sources of molecular evidence. Here, we present an approach to combine heterogeneous data from genome-wide association (GWA) studies, protein-protein interaction screens, disease similarity, linkage studies, and gene expression experiments into a multi-layered evidence network which is used to prioritize the entire protein-coding part of the genome identifying a shortlist of candidate genes. We report specifically results on bipolar disorder, a genetically complex disease where GWA studies have only been moderately successful. We validate one such candidate experimentally, YWHAH, by genotyping five variations in 640 patients and 1,377 controls. We found a significant allelic association for the rs1049583 polymorphism in YWHAH (adjusted P = 5.6e-3) with an odds ratio of 1.28 [1.12-1.48], which replicates a previous case-control study. In addition, we demonstrate our approach's general applicability by use of type 2 diabetes data sets. The method presented augments moderately powered GWA data, and represents a validated, flexible, and publicly available framework for identifying risk genes in highly polygenic diseases. The method is made available as a web service at www.cbs.dtu.dk/services/metaranker.

Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits

“…Despite the incompleteness of current protein-protein interactions and our incomplete knowledge of disease gene associations, the GCM method validated in one of the four SNPs tested. This 25% validation success rate surpasses that of other candidate gene prediction methods (8,48,49).…”

“…In addition, nominal GWAS p values superimposed upon the human molecular network have been used to identify genes associated with multiple sclerosis (6), and the disease association protein-protein link evaluator (DAPPLE) has been used to find significant interactions among proteins encoded by genes in loci associated with other particular diseases (7) . Other approaches incorporate heterogeneous molecular data such as linkage studies, cross species conservation measures, gene expression data and protein-protein interactions to better understand GWAS results (8,9). Integrating molecular network information, pathway analyses, and GWAS data thus holds promise for identifying new susceptibility loci and improving the identification of relevant candidate genes.…”

“…MetaRanker predicts candidate genes by integrating complementary layers of protein interaction, linkage, GWAS, differential expression and disease data. These five different data sources are integrated into a single meta-evidence rank, quantifying the likelihood of genes being involved in a disease of interest (8). CANDID is designed to rank candidate genes by eight evaluation criteria, considering associated publications, protein domains, conservation, expression, interactions, linkages, SNP associations, and custom data (9).…”

“…To perform a comprehensive comparison of GCM to other methods, we implemented MetaRanker (8) and CANDID (9). MetaRanker predicts candidate genes by integrating complementary layers of protein interaction, linkage, GWAS, differential expression and disease data.…”

Pathway-based genome-wide association analysis of coronary heart disease identifies biologically important gene sets

“…7,[49][50][51] We note that similar gene-set enrichment approaches were used by others to evaluate particular pathways 52 or to prioritize candidate genes. 53 But, there is an inherent difficulty in defining the potential relevance of any pathway to a specific disease process. Incorporating more specific types of biological functions such as protein-protein interactions as done by Jensen et al 54 will certainly improve the functional relevance of detected gene sets.…”