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Abstract:
While 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 observed phenotypic variation. One possible strategy to detect stronger associations 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 suffers 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. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose networkGWAS, 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-calibrated p-values, which are obtained through circular and degree-preserving network permutation schemes. networkGWAS successfully detects known associations on semi-simulated common variants from A. thaliana and on simulated rare variants from H. sapiens, as well as neighborhoods of genes involved in stress-related biological processes on a stress-induced phenotype from S. cerevisiae. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information. Availability https://github.com/BorgwardtLab/networkGWAS.git Contact giulia.muzio{at}bsse.ethz.ch, karsten.borgwardt{at}bsse.ethz.ch
