New Statistical Methods for High-Dimensional Association Tests with Applications to Large-Scale Genetic Data

高维关联测试的新统计方法及其在大规模遗传数据中的应用

基本信息

  • 批准号:
    1902903
  • 负责人:
  • 金额:
    $ 100万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-06-15 至 2023-05-31
  • 项目状态:
    已结题

项目摘要

The research tackles several challenges in large-scale biomedical studies through the development and implementation of powerful and theoretically sound statistical methods to detect robust biomarker associations. The developed methods are motivated by and will have significant impact on large-scale genetic association studies, and are also broadly applicable to other disciplines (e.g. survey sampling and mental health imaging studies). The developed methods will be applied to detect novel genetic biomarkers that are associated with multiple cardiometabolic traits, and generate novel hypotheses for biological and clinical investigation. The project will also solve some long-standing problems in statistics, and will provide theoretically sound and much more powerful methods than the commonly used ones. The project team will integrate the research results into training the next generation undergraduate and graduate students in the fast growing field of biomedical data science. This project will also promote teaching, training and learning, and broaden the participation of students from under-represented groups.Recent methodological and computational advances have facilitated the applications of statistical methods to analyze simple (primarily single) disease outcomes in large-scale genome-wide association studies in the field. However, these studies have identified only a small proportion of the risk variants and there likely remain many more common variants with modest effect sizes and/or rare variants yet to be discovered. Existing methods and statistical theories are not adequate for analyzing high-dimensional association with clustered outcomes (e.g. longitudinal outcomes), and multiple correlated and/or secondary outcomes. This project aims to address this urgent need by developing new statistical methods with solid theoretical foundation to integrate multiple correlated phenotypes to identify novel genetic variants for complex traits. In particular, the project will (1) develop powerful statistical methods for testing high-dimensional association with clustered outcomes; (2) develop theoretically sound and powerful statistical methods for testing association with multiple secondary traits; and (3) develop and apply a unified modeling framework that applies our developed statistical methods, leverages the whole genome sequencing data, and integrates functional annotation data to help identify and dissect the role of rare variants on the cardiometabolic traits. The objective of the education plan is to integrate the latest research development in statistical genetics into existing/new courses to prepare students for their future professions in biomedical/health informatics. The research will also include software development to implement the methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该研究通过开发和实施强大且理论上合理的统计方法来检测强大的生物标志物关联,从而解决了大规模生物医学研究中的几个挑战。所开发的方法的动机和将有重大影响的大规模遗传关联研究,也广泛适用于其他学科(如调查抽样和心理健康成像研究)。所开发的方法将用于检测与多种心脏代谢特征相关的新型遗传生物标志物,并为生物学和临床研究产生新的假设。 该项目还将解决一些长期存在的统计问题,并将提供理论上合理和比常用方法更强大的方法。该项目团队将把研究成果整合到快速发展的生物医学数据科学领域,培养下一代本科生和研究生。该项目还将促进教学、培训和学习,并扩大代表性不足群体的学生的参与。最近的方法和计算进步促进了统计方法在该领域大规模全基因组关联研究中分析简单(主要是单一)疾病结果的应用。然而,这些研究只确定了一小部分风险变异,可能还有许多更常见的变异,其效应大小适中和/或罕见变异尚未发现。现有的方法和统计理论不足以分析与聚类结果(例如纵向结果)以及多个相关和/或次要结果的高维关联。该项目旨在通过开发具有坚实理论基础的新统计方法来解决这一迫切需求,以整合多个相关表型来识别复杂性状的新遗传变异。特别是,该项目将(1)开发强大的统计方法来测试与聚类结果的高维关联;(2)开发理论上合理和强大的统计方法来测试与多个次要性状的关联;以及(3)开发和应用统一的建模框架,该框架应用我们开发的统计方法,利用全基因组测序数据,并整合了功能注释数据,以帮助识别和剖析罕见变异对心脏代谢特征的作用。教育计划的目标是将统计遗传学的最新研究进展纳入现有/新课程,为学生未来的生物医学/健康信息学专业做好准备。该研究还将包括软件开发,以实现该方法。该奖项反映了NSF的法定使命,并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Statistical Methods in Genome-Wide Association Studies
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Baolin Wu其他文献

Near-infrared light-triggered theranostics for tumor-specifc enhanced multimodal imaging and photothermal therapy
用于肿瘤特异性增强多模态成像和光热治疗的近红外光触发治疗诊断学
  • DOI:
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    8
  • 作者:
    Bo Wu;Bing Wan;Shuting Lu;Kai Deng;Xiaoqi Li;Baolin Wu;Yushuang Li;Rufang Liao;Shiwen Huang;Haibo Xu
  • 通讯作者:
    Haibo Xu
Biotreatment of chromite ore processing residue by Pannonibacter phragmitetus BB
芦氏潘诺杆菌 BB 生物处理铬铁矿加工残渣
Disrupted brain functional networks in patients with end-stage renal disease undergoing hemodialysis.
接受血液透析的终末期肾病患者的大脑功能网络遭到破坏。
  • DOI:
    10.1002/jnr.24725
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Baolin Wu;Xuekun Li;Meng Zhang;Feifei Zhang;Xipeng Long;Qiyong Gong;Zhiyun Jia
  • 通讯作者:
    Zhiyun Jia
Principal component based adaptive association test of multiple traits using GWAS summary statistics
使用 GWAS 摘要统计对多个性状进行基于主成分的自适应关联测试
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bin Guo;Baolin Wu
  • 通讯作者:
    Baolin Wu
High Minimum Inter-Execution Time Sigmoid Event-Triggered Control for Spacecraft Attitude Tracking With Actuator Saturation
具有执行器饱和度的航天器姿态跟踪的高最小执行间时间 Sigmoid 事件触发控制

Baolin Wu的其他文献

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