networkGWAS: a network-based approach to discover genetic associations

Motivation: 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 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. Results: 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 permutations. networkGWAS successfully detects known associations on diverse synthetic phenotypes, as well as known and novel genes in phenotypes from Saccharomyces cerevisiae and Homo sapiens. It thereby enables the systematic combination of gene-based genome-wide association studies with biological network information. Availability and implementation: https://github.com/BorgwardtLab/networkGWAS.git.

Automated summary

Network-based genome-wide association studies aim to identify associations between genetic markers and complex traits by aggregating the effects of multiple markers and testing entire genes, pathways, or networks. However, current approaches have limitations such as greedy feature selection and lack of multiple testing correction. To address these issues, this study proposes networkGWAS, a computationally efficient and statistically reliable method that combines mixed models and neighborhood aggregation for network-based genome-wide association studies.