The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-2 associated variants. Two important derivations will be the translation of this information into a multiscale 3 understanding of pathogenic variants, and leveraging existing data to increase the power of existing and 4 future studies through prioritization. We explore edge prediction on heterogeneous networks-graphs 5 with multiple node and edge types-for accomplishing both tasks. First we constructed a network 6 with 18 node types-genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures 7 database) collections-and 19 edge types from high-throughput publicly-available resources. From this 8 network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology 9 between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the 10 probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individ-12 ual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional 13 signatures of perturbations, pathways, and protein interactions as fundamental mechanisms explaining 14 pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld mul-15 tiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined 16 our network predictions with statistical evidence of association to propose four novel MS genes, three 17 of which (JAK2, REL, RUNX3 ) validated on the masked GWAS. Furthermore, our predictions provide 18 biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all 19 predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic 20 associations and provides a powerful new approach for data integration across multiple domains. 21 Author Summary For complex human diseases, identifying the genes harboring susceptibility variants has taken on medical 23 importance. Disease-associated genes provide clues for elucidating disease etiology, predicting disease 24 risk, and highlighting therapeutic targets. Here, we develop a method to predict whether a given gene 25 and disease are associated. To capture the multitude of biological entities underlying pathogenesis, we 26 constructed a heterogeneous network, containing multiple node and edge types. We built on a technique 27 developed for social network analysis, which embraces disparate sources of data to make predictions from 28 heterogeneous networks. Using the compendium of associations from genome-wide studies, we learned 29 the influential mechanisms underlying pathogenesis. Our findings provide a novel perspective about 30 the existence of pervasive pleiotropy across complex diseases. Furthermore, we suggest transcriptional 31 signatures of perturbations are an underutilized resource amongst prioritization approaches. For multiple 32 sclerosis, we demonstrated our ability to prioritize future studies and discover novel susceptibility genes. Researchers can use these predictions to increase the statistical power of their studies, to suggest the 34 causal genes from a set of candidates, or to generate evidence-based experimental hypothesis. In the last decade, genome-wide association studies (GWAS) have been established as the main strategy 37 to map genetic susceptibility in dozens of complex diseases and phenotypes. Despite the undeniable 38 success of this approach, researchers are now confronted with the challenge of maximizing the scientific 39 contribution of existing GWAS datasets, whose undertakings represented a substantial investment of 40 human and monetary resources from the community at large. A central assumption in GWAS is that every region in the genome (and hence every gene) is a-42 priori equally likely to be associated with the phenotype in question. As a result, small effect sizes and 43 multiple comparisons limit the pace of discovery. However, rational prioritization approaches may afford 44 an increase in study power while avoiding the constraints and expense related to expanded sampling. One 45 such a way forward is the current trend on analyzing the combined contribution of susceptibility variants 46 in the context of biological pathways, rather than single SNPs [1, 2] . A less explored but potentially 47 revealing strategy is the integration of diverse sources of data to build more accurate and comprehensive 48 models of disease susceptibility. 49 Several strategies have been attempted to identify the mechanisms underlying pathogenesis and use 50 these insights to prioritize genes for genetic association analyses. Gene-set enrichment analyses iden-51 tify prevalent biological functions amongst genes contained in disease-associated loci [3] . Gene network 52 approaches search for neighborhoods of genes where disease-associated loci aggregate [4] . Literature min-53 ing techniques aim to chronicle the relatedness of genes to identify a subset of highly-related associated 54 genes [5] . These strategies generally rely on user-provided loci as the sole input and do not incorporate 55 broader disease-specific knowledge. Typically, the proportion of genome-wide significant discoveries in a 56 given GWAS is low, thus leaving little high-confidence signal for seed-based approaches to build from. To overcome this limitation, here we aimed at characterizing the ability of various information domains 58 to identify pathogenic variants across the entire compendium of complex disease associations. Using this 59 multiscale approach, we developed a framework to prioritize both existing and future GWAS analyses 60 and highlight candidate genes for further analysis. To approach this problem, we resorted to a method that integrated diverse information domains 62 naturally. Heterogeneous networks are a class of networks which contain multiple types of entities (nodes) 63 and relationships (edges), and provide a data structure capable of expressing diversity in an intuitive and 64 scalable fashion. However, current techniques available for network analysis have been developed for 65 homogeneous networks and are not directly extensible to heterogenous networks. Furthermore, research 66 into heterogeneous network analysis is in its early stages [6] . One of the few existing methods for 67 predicting edges on heterogeneous networks was developed by researchers studying social sciences to 68 predict future coauthorship [7] . In this work, we extended this methodology to predict the probability 69 that an association between a gene and disease exists. 70 4 Results 71 Constructing a heterogeneous network to integrate diverse information do-72 mains 73 Using publicly-available databases and standardized vocabularies, we constructed a heterogeneous net-74 work with 40,343 nodes and 1,608,168 edges (Figure 1) . Databases were selected based on quality, 75 reusability, and throughput. The network was designed to encode entities and relationships relevant 76 to pathogenesis. The network contained 18 node types (metanodes) and 19 edge types (metaedges), 77 displayed in Figure S2A . Entities represented by metanodes consisted of diseases, genes, tissues, patho-78
The first version of Hetionet was used for prioritizing of genes from loci associated with multiple sclerosis in GWAS REF .
Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes
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Biology