Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Recursion Pharmaceuticals, Salt Lake City, UT, USA.
Nat Commun. 2021 Sep 10;12(1):5369. doi: 10.1038/s41467-021-25680-7.
Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer's Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require "harmonized" phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer's Disease.
深度神经网络（DNN）可以捕捉变量之间的复杂关系，但由于它们需要大量的样本，因此其在理解基因表达与人类表型之间关系方面的潜力尚未得到充分挖掘。在这里，我们引入了一种分析框架，即 MD-AD（用于阿尔茨海默病神经病理学的多任务深度学习），该框架利用了 DNN 和多队列设置之间意想不到的协同作用。在这些设置中，传统的统计方法会阻碍真正的联合分析，因为这些方法需要“协调一致”的表型，并且往往会捕捉到队列层面的变化，从而掩盖了更微妙的真正疾病信号。相反，MD-AD 稀疏地整合了跨队列测量的相关表型，并学习了线性模型无法发现的基因与表型之间的相互作用，从而识别出比队列层面变化更微妙的信号，这些信号可以在动物模型和不同组织中独特地重现。我们表明，MD-AD 利用了小胶质细胞免疫反应与神经病理学之间的性别特异性关系，为炎症基因与阿尔茨海默病之间的关联提供了一个细致入微的背景。
