Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease

阿尔茨海默病多组学数据整合的新统计方法 主要研究者：Chong Wu，Ph.D.（联系人）; Jonathan布拉德利博士 总结 一个基本的公共卫生需要是了解阿尔茨海默病（AD）的遗传基础， 加强筛查和预防性治疗。迄今为止，全基因组关联研究（GWAS） 确定了超过30个晚发性AD风险位点。这些确定的风险位点只能解释一小部分的 迟发性AD遗传性，这促使全转录组关联研究（TWAS）的发展。 TWAS通过利用个体水平的基因表达来测试预测的基因表达-性状关联 参考数据，并成功地提高了许多复杂性状的遗传风险位点的发现，包括 晚发性AD尽管已经提出并研究了许多TWAS相关方法，但关键差距仍然存在。 保持。首先，现有的TWAS方法主要需要表达参考面板，其可以是 样本量有限，并在开发预测模型时带来挑战。第二，虽然许多 表达预测模型已经建立，没有统计方法已经提出来建立一个新的 一种组合/使用现有表达预测模型的表达预测模型。这些关键的差距， 知识阻碍了TWAS检测基因-性状关联的统计能力，这是最终需要的 以获得对AD遗传基础的潜在变革性见解。作为对PAS-19-391的回应，该项目 总体目标是开发统计方法和软件，以提高TWAS的能力， AD的生物学见解我们的中心假设是最大化TWAS的功效， 样本量更大的汇总数据作为表达式参考面板或利用现有的 表达预测模型为了验证我们的中心假设，我们将1）开发表达预测模型 通过利用eQTL汇总数据，2）通过整合现有的表达预测来开发表达模型 模型，以及3）开发开源，跨平台，公开可用，易于使用的软件来实现 建议的方法。这项研究的首要目标不仅增强了广泛使用的方法的力量， TWAS还提供了对AD病理学的生物学见解。所提出的新方法也可以应用于 其他复杂的疾病。

Novel Statistical Methods for Multi-omics Data Integration in Alzheimer's Disease Principal Investigators: Chong Wu, Ph.D. (contact); Jonathan Bradley, Ph.D. Summary A fundamental public health need is to understand the genetic basis of Alzheimer’s disease (AD) to enable enhanced screening and preventive therapies. To date, genome-wide association studies (GWAS) have identified more than 30 late-onset AD risk loci. These identified risk loci only explain a modest proportion of the late-onset AD heritability, which motivates the development of transcriptome-wide association studies (TWASs). TWASs test for predicted gene expression-trait associations by leveraging individual-level gene expression reference data and successfully enhanced the discovery of genetic risk loci for many complex traits, including late-onset AD. Even though many TWASs related methods have been proposed and investigated, critical gaps remain. First, existing TWAS methods predominantly require expression reference panels, which can be limited in sample size and create a challenge when developing prediction models. Second, while many expression prediction models have been built, no statistical method has been proposed to build a new expression prediction model that combines/uses existing expression prediction models. These critical gaps in knowledge deter the statistical power of detecting gene-trait associations by TWAS, which is ultimately needed to gain potentially transformative insight into the genetic basis of AD. In response to PAS-19-391, this project’s overall objective is to develop statistical methods and software for improving the power of TWAS and offering biological insights into AD. Our central hypothesis is to maximize the power of TWAS either by using eQTL summary data with much larger sample sizes as the expression reference panel or by leveraging existing expression prediction models. To test our central hypothesis, we will 1) develop expression prediction models by leveraging eQTL summary data, 2) develop expression models by integrating existing expression prediction models, and 3) develop open-source, cross-platform, publicly available, easy-to-use software to implement the proposed methods. The overarching aim of this study not only enhances the power of the widely used method TWASs but also offers biological insights into AD pathology. The proposed new methods can also be applied to other complex diseases.