课题基金 / 基金详情

项目摘要

项目成果

相似基金

相关文献

中文摘要
翻译
阿尔茨海默病多组学数据整合的新统计方法
英文摘要
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.
期刊论文(6)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1038/s41467-022-34016-y
发表时间: 2022-10-25
期刊: Nature communications
影响因子: 16.6
作者: []
通讯作者:
MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations.
MIMOSA:一种由改进的甲基化插补模型组成的资源,增强了识别 DNA 甲基化-表型关联的能力。
DOI: 10.1101/2023.03.20.23287418
发表时间: 2023
期刊: medRxiv : the preprint server for health sciences
影响因子: --
作者: [Melton,HunterJ, Zhang,Zichen, Deng,Hong-Wen, Wu,Lang, Wu,Chong]
通讯作者: Wu,Chong
SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.
SUMMIT-FA:使用功能注释改进转录组插补的新资源。
DOI: 10.1093/hmg/ddad205
发表时间: 2024
期刊: Human molecular genetics
影响因子: 3.5
作者: [Melton,HunterJ, Zhang,Zichen, Wu,Chong]
通讯作者: Wu,Chong
DOI: 10.1002/gepi.22425
发表时间: 2021-12
期刊: Genetic epidemiology
影响因子: 2.1
作者: [Bae YE, Wu L, Wu C]
通讯作者: Wu C
6
    国内基金
    海外基金
    新型F-18标记香豆素衍生物PET探针的研制及靶向Alzheimer's Disease 斑块显像研究
    • 批准号:
      81000622
    • 项目类别:
      青年科学基金项目
    • 资助金额:
      20.0万元
    • 批准年份:
      2010
    • 负责人:
      梁胜
    • 依托单位:
    阿尔茨海默病(Alzheimer's disease,AD)动物模型构建的分子机理研究
    • 批准号:
      31060293
    • 项目类别:
      地区科学基金项目
    • 资助金额:
      26.0万元
    • 批准年份:
      2010
    • 负责人:
      郭亚芬
    • 依托单位:
    跨膜转运蛋白21(TMP21)对引起阿尔茨海默病(Alzheimer'S Disease)的γ分泌酶的作用研究