The genetic analysis
of complex disorders has undoubtedly led to
the identification of a wealth of associations between genes and specific
traits. However, moving from genetics to biochemistry one gene at
a time has, to date, rather proved inefficient and under-powered to
comprehensively explain the molecular basis of phenotypes. Here we
present a novel approach, weighted protein–protein interaction
network analysis (W-PPI-NA), to highlight key functional players within
relevant biological processes associated with a given trait. This
is exemplified in the current study by applying W-PPI-NA to frontotemporal
dementia (FTD): We first built the state of the art FTD protein network
(FTD-PN) and then analyzed both its topological and functional features.
The FTD-PN resulted from the sum of the individual interactomes built
around FTD-spectrum genes, leading to a total of 4198 nodes. Twenty
nine of 4198 nodes, called inter-interactome hubs (IIHs), represented
those interactors able to bridge over 60% of the individual interactomes.
Functional annotation analysis not only reiterated and reinforced
previous findings from single genes and gene-coexpression analyses
but also indicated a number of novel potential disease related mechanisms,
including DNA damage response, gene expression
regulation, and cell waste disposal and
potential biomarkers or therapeutic targets including EP300. These
processes and targets likely represent the functional core impacted
in FTD, reflecting the underlying genetic architecture contributing
to disease. The approach presented in this study can be applied to
other complex traits for which risk-causative genes are known as it
provides a promising tool for setting the foundations for collating
genomics and wet laboratory data in a bidirectional manner. This is
and will be critical to accelerate molecular target prioritization
and drug discovery.