Quantum Computing for Network Medicine-Based Epistatic Disease Mechanism Mining - Ready for Quantum Computing?

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d126791e91">Most heritable diseases are polygenic and yield complex disease mechanisms. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs). Existing statistical methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs / disease genes in a candidate mechanism. We further show that this computationally demanding task can be accelerated with quantum computing. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas ( <a data-untrusted="" href="https://epistasis-disease-atlas.com" id="d126791e93" target="xrefwindow">https://epistasis-disease-atlas.com</a>). In summary, we demonstrate the potential of seamlessly integrated quantum computing techniques to accelerate mechanism mining. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of drug repurposing candidates and improved combination therapies.