Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Freising, Germany.
Institute for Advanced Study (Lichtenbergstrasse 2 a) Technical University of Munich, D-85748 Garching, Germany.
Nucleic Acids Res. 2024 Sep 23;52(17):10144-10160. doi: 10.1093/nar/gkae697.
Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational 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. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. 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 (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. 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 improved risk scores and combination therapies.
大多数遗传性疾病都是多基因的。为了理解潜在的遗传结构，发现基因组中单核苷酸多态性（SNP）之间的临床相关上位性相互作用（EI）至关重要。现有的 EI 检测统计计算方法大多由于高阶 EI 的组合爆炸而仅限于 SNP 对。通过 NeEDL（基于网络的局部搜索上位性检测），我们利用网络医学来告知选择 EI，与现有工具相比，这些 EI 在统计学上更显著，平均由五个 SNPs 组成。我们进一步表明，一旦量子计算硬件可用，这项计算密集型任务可以大大加速。我们将 NeEDL 应用于八种不同的疾病，并发现了一些已知与疾病有关的基因（受 SNP 上位性相互作用的影响），此外，这些结果在独立队列中具有可重复性。这些八种疾病的 EI 可以在 Epistasis Disease Atlas（https://epistasis-disease-atlas.com）中进行交互式探索。总之，NeEDL 展示了无缝集成的量子计算技术加速生物医学研究的潜力。我们的网络医学方法以前所未有的统计和生物学证据检测高阶 EI，为多基因疾病提供了独特的见解，并为开发改进的风险评分和联合疗法提供了基础。
