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

阿尔茨海默病基因发现的多组学方法。

基本信息

批准号：
10201909
负责人：
Iuliana Ionita
金额：
$ 163.19万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10201909
关键词：
Affect Alzheimer&apos s Disease Amyloid Base Sequence Chromosomes Clinical Code Collection Communities Complex Computer software Computing Methodologies Data Data Set Dementia Deterioration Disease Gene Expression Gene Targeting Genes Genetic Genomics Genotype-Tissue Expression Project Goals Individual Lead Memory Methods Molecular Molecular Conformation Nature Neurofibrillary Tangles Pathologic Play Proteins Regulatory Element Resources Risk Role Senile Plaques Societies Software Tools Statistical Methods Testing Tissues Variant analytical tool base causal variant cognitive function cost dementia care design disorder risk epigenomics extracellular gene discovery genetic testing genome sequencing genome wide association study heterogenous data high dimensionality hyperphosphorylated tau improved large scale data man metabolomics multiple omics neuron loss new therapeutic target novel repository risk variant statistics targeted treatment tau Proteins therapeutic development tool trait transcriptome transcriptomics web portal whole genome

项目摘要

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.

阿尔茨海默病（AD）是一种复杂的异质性疾病，一部分是遗传的。了解在疾病中发挥作用的遗传机制是重要的因为它可以更好地理解潜在的分子机制，用于治疗开发的新基因靶点。我们提出了以基因为中心的方法，利用专门开发的各种组学数据集，对于AD（如AMP-AD），还有更通用的资源，如GTEx，PsychENCODE， ENCODE和Roadmap Epigenomics。我们将开发用于转录组范围的分位数工具关联研究（TWAS），这是TWAS的概括，以更复杂，标准TWAS中的线性假设可能失败的异质场景。我们将我们还利用WGS的数据，通过联合分析编码和监管，预测的调节元件的变异可能会影响基因的表达，考虑. 我们会把这些分析工具编入软件，免费提供给社区我们还将把它们应用于一些最大的AD遗传数据集， GWAS和WGS，并将在专门设计的门户网站。