ABSTRACT
Background State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes.
Results We applied NETPAGE to sporadic, late-onset Alzheimer’s disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through gene networks. The result of network propagation is a set of smoothed gene scores used to predict disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed mutation profile acted as a robust predictor of case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we showed tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms and gene sets with known connections to AD.
Conclusions The presented framework enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.
