Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities

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. 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 tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to 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 investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes. Author summary In the biomedical field there is ever increasing availability of data from sequencing-based methods, such as whole-genome or whole-exome sequencing, that can greatly help elucidate the role of rare genetic variants in the aetiology of common diseases. However, state-of-the-art rare variant association methods are vastly underpowered in small to medium-sized studies and therefore novel methodologies are needed to leverage these datasets while integrating information from different genomic sources. To this end we present NETPAGE, a gene-based association testing method that models how the effect of rare deleterious variants spreads over gene interaction networks. NETPAGE is robust and flexible, and can be applied to different diseases, sample sizes, and types of traits (binary or continuous). We demonstrate the successful application of NETPAGE to two Alzheimer's disease cohorts of different sizes and sequencing methods, identifying connected hub genes and communities underlying biological processes and pathways involved in Alzheimer's and other neurodegenerative diseases, and that could be considered as potential drug targets.

Automated summary

NETPAGE is an integrative approach aimed at investigating biological pathways through which rare variation results in complex disease phenotypes. Applied to AD, it enabled the identification of a set of connected genes robustly associated with case-control status, as well as correlations with conversion risk in MCI subjects. Additionally, significant enrichments in gene sets with connections to AD were discovered.