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. 2021 Jan 7;17(1):e1008517.
doi: 10.1371/journal.pcbi.1008517. eCollection 2021 Jan.

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

Marzia Antonella Scelsi  1 , Valerio Napolioni  2 , Michael D Greicius  2 , Andre Altmann  1 ; Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Alzheimer’s Disease Sequencing Project (ADSP)
Affiliations

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

Marzia Antonella Scelsi et al. PLoS Comput Biol. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. NETPAGE combines network propagation with sparse regression to follow the pathways by which rare variants percolate through gene interaction networks.
In the network propagation process, the flow of information through the network is controlled by the diffusion length α. The matrix G in the formula represents the variation burden at iterations t and t+1; while D·N is a degree-normalised adjacency matrix of the gene interaction network (see Methods). The result of network propagation is a set of smoothed gene scores tested for association with disease status through sparse regression and stability selection.
Fig 2
Fig 2. A selection of simulation results.
The x and y axis represent the SNV frequencies in controls and cases respectively. The faceting allows to visualise the effect of other parameters (diffusion length, quantile normalisation). The colour-coding indicates the statistical significance of the difference in the hub gene smoothed score between controls and cases, in units of -log10 p-value (Bonferroni corrected). We investigated three mutation scenarios: scenario 1, only first neighbours mutated; scenario 2, only second neighbours mutated; scenario 3, both first and second neighbours mutated; the target gene is always unmutated. Cells marked with a black cross indicate parameter combinations where the smooth score of the target gene was not significantly different between cases and controls. This set of tile plots shows the effect of varying diffusion length quantile normalisation after network propagation with top 1% edges retained in scenario 3.
Fig 3
Fig 3. Stability selection results in ADNI (A) and ADSP (B), for a selection probability cutoff of 0.80.
In these stability path plots, the x axis represents values of the sparsity hyperparameter λ (see Methods, Eq 2) that controls the regularisation required by the LASSO; the y axis represents the selection probability of a gene. Selection probability paths (trajectories) for different genes are represented by different colours. Genes whose trajectories crossed the threshold of 0.80 selection probability were considered as robust predictors of case-control status and followed up in subsequent analyses. Panel (C) shows the ego network of CAMK2B (selected in ADSP). Nodes coloured in bright green were identified by NETPAGE; nodes coloured in coral red are genes linked to AD and other neurodegenerative disease in the literature.
Fig 4
Fig 4. Survival analysis with the smoothed score for the gene resulting from stability selection on the ADNI dataset.
The forest plot depicts hazard ratios from Cox proportional hazards model with confidence intervals and statistical significance. The score resulting from network propagation for PFAS was seen to be significantly associated with lower risk of conversion to AD.
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