Multi-omics approaches for gene discovery in Alzheimer's Disease.

阿尔茨海默病(AD)是一种复杂的、异质性的疾病，AD的风险受到影响 这部分是由基因决定的。了解在疾病中起作用的遗传机制是很重要的 因为它可以更好地理解潜在的分子机制，并可以识别 治疗开发的新基因靶点。 我们提出了以基因为中心的方法，利用专门开发的不同组学数据集 适用于AD(如AMP-AD)，也适用于更通用的资源，如GTEx、mental ENCODE Encode和路线图表观基因组学。我们将为整个转录组开发分位数工具 关联研究(TWAS)，它是TWAS的推广，到更复杂和 标准TWA线性假设很可能失败的异质场景。我们会 我还使用来自WGS的数据，通过联合分析编码和调节来开发基于基因的测试 预测的调控元件的变化可能会影响基因的表达 对价。 我们将把这些分析工具应用到软件包中，供 社区。我们还将把它们应用于一些最大的阿尔茨海默病现有基因数据集，两者 GWAS和WGS，并将在专门设计的 门户网站。

Alzheimer’s Disease (AD) is a complex, heterogeneous disorder, and risk to AD is influenced partly by genetics. Understanding the genetic mechanisms that play a role in disease is important as it can lead to a better understanding of the underlying molecular mechanisms, and can identify new gene targets for therapeutic development. We propose gene centric approaches that leverage diverse omics datasets developed specifically for AD (such as AMP-AD), but also more general resources such as GTEx, PsychENCODE, ENCODE, and Roadmap Epigenomics. We will develop quantile tools for transcriptome-wide association studies (TWAS), which are generalizations of TWAS to more complex and heterogenous scenarios where the linear assumptions in standard TWAS are likely to fail. We will also develop gene-based tests using data from WGS by jointly analyzing coding and regulatory variation in predicted regulatory elements likely to affect the expression of a gene under consideration. We will implement these analytical tools into software packages to be made freely available to the community. We will also apply them to some of the largest existing genetic datasets for AD, both GWAS and WGS, and will make the results available to the community on a specially designed web portal.