TY - JOUR T1 - Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia A1 - Jia, Peilin A1 - Wang, Lily A1 - Fanous, Ayman H. A1 - Pato, Carlos N. A1 - Edwards, Todd L. A1 - The International Schizophrenia Consortium A1 - Zhao, Zhongming Y1 - 2012/07/05 N2 - Author Summary The recent success of genome-wide association studies (GWAS) has generated a wealth of genotyping data critical to studies of genetic architectures of many complex diseases. In contrast to traditional single marker analysis, an integrative analysis of multiple genes and the assessment of their joint effects have been particularly promising, especially upon the availability of many GWAS datasets and other high-throughput datasets for numerous complex diseases. In this study, we developed an integrative analysis framework for multiple GWAS datasets and demonstrated it in schizophrenia. We first constructed a GWAS-weighted protein-protein interaction (PPI) network and then applied a dense module search algorithm to identify subnetworks with combinatory disease effects. We applied combinatorial criteria for module selection based on permutation tests to determine whether the modules are significantly different from random gene sets and whether the modules are associated with the disease in investigation. Importantly, considering there are many complex diseases with multiple GWAS datasets available, we proposed a discovery-evaluation strategy to search for modules with consistent combined effects from two or more GWAS datasets. This approach can be applied to any diseases or traits that have two or more GWAS datasets available. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 7 UR - https://doi.org/10.1371/journal.pcbi.1002587 SP - e1002587 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002587 ER -