Collaborative Research: Stochastic Models for Gene-based Association Analysis of Longitudinal Phenotypes with Sequence Data

合作研究：基于基因的纵向表型与序列数据关联分析的随机模型

• 批准号: 1916246
• 负责人: Zuoheng Wang
• 金额: $ 12万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916246&HistoricalAwards=false
• 关键词: Collaborative; Research; Stochastic; Models; Gene

Longitudinal genetic studies provide a valuable resource for exploring key genetic and environmental factors that affect complex traits over time. Genetic analysis of longitudinal data that incorporates trait variation over time is critical to understanding genetic influence and biological variations of complex diseases. In recent years, many genetic studies have been conducted in cohorts in which multiple measures on a trait of interest are collected on each subject over a period of time in addition to genome sequence data. These studies not only provide a more accurate assessment of disease condition but enable researchers to investigate the influence of genes on the trajectory of a trait and disease progression. This project focuses on the development of novel association testing methods to analyze sequencing genomic data at gene levels. The research will help provide insights into the underlying biology and progression of complex diseases.In longitudinal genetic studies and data from the Electronic Medical Records and Genomics (eMERGE) network, phenotypic traits and genetic variants may be viewed as functional data. Functional data analysis (FDA) can serve as a valuable tool for exploring key genetic and environmental factors that affect complex traits over time. In the presence of a large number of rare variants, gene-based analysis is a more powerful tool for gene mapping than testing of individual genetic variants. This project seeks to develop stochastic functional regression models and longitudinal sequence kernel association tests (LSKAT) to analyze longitudinal traits of population samples and pedigree or cryptically related samples, and to analyze pleiotropic traits. FDA techniques and kernel-based approaches are utilized to reduce the high dimensionality of sequencing data and draw useful information. A variance-covariance structure is constructed to model the measurement variation and correlations of an individual's trait based on the theory of stochastic processes and novel penalized spline models are used to estimate the trajectory mean function. The proposed methods and software will be tested and refined using real data sets and simulation studies. User-friendly software will be developed to implement the proposed methods and will be made publicly available.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.

纵向遗传研究为探索随着时间的推移影响复杂性状的关键遗传和环境因素提供了宝贵的资源。纵向数据的遗传分析，包括性状随时间的变化是至关重要的理解遗传影响和复杂疾病的生物变异。 近年来，许多遗传研究已经在群组中进行，其中除了基因组序列数据之外，还在一段时间内收集每个受试者对感兴趣性状的多个测量。 这些研究不仅提供了对疾病状况的更准确评估，而且使研究人员能够调查基因对性状和疾病进展轨迹的影响。 该项目的重点是开发新的关联测试方法，以分析基因水平上的测序基因组数据。 这项研究将有助于深入了解复杂疾病的潜在生物学和进展。在纵向遗传研究和电子病历和基因组学（eMERGE）网络的数据中，表型性状和遗传变异可以被视为功能数据。 功能数据分析（FDA）可以作为一种有价值的工具，用于探索随着时间的推移影响复杂性状的关键遗传和环境因素。 在存在大量罕见变异的情况下，基于基因的分析是比检测单个遗传变异更强大的基因定位工具。 本研究旨在建立随机函数回归模型和纵向序列核关联检验（LSKAT），以分析群体样本和系谱或隐性相关样本的纵向性状，并分析多效性性状。 FDA技术和基于核的方法被用来降低测序数据的高维数，并提取有用的信息。基于随机过程理论，构造了一个方差-协方差结构来描述个体特质的测量变异和相关性，并采用惩罚样条模型来估计轨迹均值函数。 将使用真实的数据集和模拟研究对所提出的方法和软件进行测试和改进。 该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use

• DOI: 10.1534/genetics.119.302598
• 发表时间: 2019-05
• 期刊: Genetics
• 影响因子: 3.3
• 作者: Weimiao Wu;Zhong Wang;Ke Xu;Xinyu Zhang;Amei Amei-Amei;J. Gelernter;Hongyu Zhao;Amy C. Justice;Zuoheng Wang