Causal and integrative deep learning for Alzheimer's disease genetics

摘要 回应PAR-19-269《阿尔茨海默病遗传和表型的认知系统分析》 数据“，我们建议开发和应用更强大和健壮的机器学习方法来处理因果关系和 综合分析，特别是用于工具变量分析的深度学习方法，以确定原因 利用已发表的大规模研究对后Gwas时代阿尔茨海默病(AD)的风险/保护因素进行研究 全基因组测序(WGS)和其他基因组和神经成像数据。我们的主要动机是为了- 倾向于一种新兴的、日益流行的将fl与基因表达数据集成在一起的方法，称为 转录组范围的关联研究(TWAS)，旨在通过不仅通过以下方式改善GWAS的当前实践 增加统计能力，同时识别(假定的)因果基因，从而获得对遗传基础的洞察 常见的疾病和复杂的特征。三次抽样的统计原理是(两个样本)两阶段 在因果推断的辅助变量(IV)分析框架下的线性模型的最小二乘(2SLS)。 然而，在实践中，TWAS可能无法识别真正的原因基因，而由于违规而给出假阳性 其建模假设，例如由于静脉注射或基因表达的非线性效应，或由于无效的静脉注射(在 SNPs水平多效性的存在)。首先，我们提出发展线性模型和神经网络模型。 在基因组上整合大量的功能注释(例如，各种类型的功能基因组 以及来自ENCODE和路线图表观基因组学项目的表观遗传学数据)作为先验知识，以改善免疫系统。 发布/预测基因表达(或其他分子或成像内表型或复杂性状/疾病) SNPs，对应于2SLS的fi第一阶段。其次，我们提出了具有更强fl伸缩性的非线性神经网络 在存在无效IV的情况下的2SLS第二阶段的模型，其可能是具有直接(或 水平多效性)对结果的影响，如预期的广泛多效性所致。然后我们将组合 以上两个阶段中的方法，以形成更具fl灵活性和健壮性的神经网络方法，作为 2SLS用于因果推理。第三，我们考虑推断两个性状之间的因果方向，例如基因的表达- Sion和AD，允许SNPs和性状之间以及这两个性状之间的非线性关系。这一点很关键 在减少假阳性方面，例如由于反向因果关系，但在很大程度上没有得到充分的研究。第四，我们应用 新的(和现有的)方法，转录组，蛋白质组，神经成像和AD GWAS/WGS数据来识别(PU- AD的因果基因、蛋白质和脑感兴趣区(ROI)，同时构建相应的基因 内表型和AD风险的预测模型。最后，我们将开发和传播公开可用的 实施建议的分析方法的软件，例如作为Python程序或R包，以促进 由Sciencefic社区广泛使用。

Summary In response to PAR-19-269, “Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data”, we propose developing and applying more powerful and robust machine learning methods for causal and integrative analysis, especially deep learning approaches for instrumental variable analysis, to identify causal risk/protective factors for Alzheimer's disease (AD) in the post-GWAS era by leveraging published large-scale GWAS, whole-genome sequencing (WGS) and other omic and neuroimaging data. Our main motivation is to ex- tend an emerging and increasingly influential approach of integrating GWAS with gene expression data, called transcriptome-wide association studies (TWAS), aiming to improve over the current practice of GWAS by not only increasing statistical power, but also identifying (putative) causal genes, thus gaining insights into the genetic basis of common diseases and complex traits. The statistical principle underlying TWAS is the (two-sample) two-stage least squares (2SLS) for linear models in the framework of instrumental variable (IV) analysis for causal inference. In practice, however, TWAS may fail to identify true causal genes while giving false positives due to the violation of its modeling assumptions, e.g., due to non-linear effects of IVs or gene expression, or due to invalid IVs (in the presence of horizontal pleiotropy of SNPs). First, we propose developing linear models and neural network models incorporating a large number of functional annotations on the genome (e.g. various types of functional genomic and epigenetic data from the ENCODE and Roadmap Epigenomics projects) as prior knowledge to improve im- puting/predicting gene expression (or other molecular or imaging endophenotypes or complex traits/diseases) via SNPs, corresponding to the first stage of 2SLS. Second, we propose neural networks as more flexible non-linear models for the second stage of 2SLS in the presence of invalid IVs, which may be the SNPs having direct (or horizontal pleiotropic) effects on the outcome as expected from the wide-spread pleiotropy. Then we combine the approaches in the above two stages to form a more flexible and robust neural network approach as an extension of 2SLS for causal inference. Third, we consider inferring causal directions between two traits, e.g. a gene's expres- sion and AD, allowing non-linear relationships between SNPs and traits and between the two traits. This is critical in reducing false positives, e.g. due to reverse causation, but has been largely under-studied. Fourth, we apply the new (and existing) methods to transcriptomic, proteomic, neuroimaging and AD GWAS/WGS data to identify (pu- tative) causal genes, proteins and brain regions of interest (ROIs) for AD, while building the corresponding genetic prediction models for endophenotypes and AD risk. Finally, we will develop and disseminate publicly available software implementing the proposed analysis methods, e.g. as Python programs or R packages, to facilitate the wide use by the scientific community.