AbstractMotivationWhile the search for associations between genetic markers and complex traits has led to the discovery of tens of thousands of trait-related genetic variants, the vast majority of these only explain a small fraction of the observed phenotypic variation. One possible strategy to overcome this while leveraging biological prior is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffer from a vast search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings.ResultsTo address the shortcomings of current approaches of network-based genome-wide association studies, we proposenetworkGWAS, a computationally efficient and statistically sound approach to network-based genome-wide association studies using mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibratedP-values, which are obtained through circular and degree-preserving network permutations.networkGWASsuccessfully detects known associations on diverse synthetic phenotypes, as well as known and novel genes in phenotypes fromSaccharomycescerevisiaeandHomo sapiens. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information.Availability and implementationhttps://github.com/BorgwardtLab/networkGWAS.git.