, 2007). The main challenge in the analysis of rare genetic variations, such as de novo CNVs, is precisely their rarity, i.e., the fact that a vast majority of the observed genetic events are unique. Consequently, each rare variant by itself is not statistically significant, so an integrative conceptual framework is required to understand their overall functional impact. We hypothesized that recently obtained genome-wide de novo

CNV data (Levy et al., 2011) could allow identification of the underlying biological pathways and processes if considered in the context of functional biological networks (Feldman et al., 2008 and Iossifov et al., 2008). Here, we develop a method for network-based analysis of genetic associations (NETBAG) JAK inhibitor and demonstrate its utility in autism. The presented approach can determine whether the observed rare events en masse affect a significantly interconnected functional network of human genes. To implement our approach, we first built a background network that

connects any pair of human genes with a weighted edge encapsulating our a priori expectation that the two genes participate in the same genetic phenotype (see Experimental Procedures and Supplemental AZD2281 ic50 Experimental Procedures). This background network was based on a combination of various functional descriptors, such as shared gene ontology (GO) annotations (Ashburner et al., 2000), functional pathways in KEGG (Kanehisa and Goto, 2000), shared interaction partners and coevolutionary patterns (see Experimental Procedures). Similar methods have been previously used to build functional networks in humans and several model organisms (Lee et al., 2004 and Lee et al., 2008).

In contrast to the aforementioned studies, edges in our network represent the likelihood that two genes participate in a similar genetic phenotype rather else than necessarily share cellular functions. Importantly, no deliberate biases toward genes previously implicated in autism or biological functions related to nervous system were used in building the network. The likelihood network was assembled using a large set of known disease-gene associations that were carefully curated for our previous study (Feldman et al., 2008). This set contains 476 genes associated with 132 different genetic diseases (see Experimental Procedures). Using the constructed network, we searched for functionally connected clusters of human genes affected by de novo CNVs (Figure 1). The genes within the observed CNV regions were first mapped to the nodes corresponding to these genes in the network (Figure 1B). Clusters of genes were assigned scores based on the strength of their connections, and a greedy search algorithm (see Experimental Procedures) was then used to find high-scoring clusters of genes within the CNV regions (Figure 1C).