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: 1915904
• 负责人: Ruzong Fan
• 金额: $ 17.99万
• 依托单位: Georgetown 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=1915904&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.

纵向遗传学研究为探索影响复杂性状的关键遗传和环境因素提供了宝贵的资源。结合性状随时间变化的纵向数据的遗传分析对于理解复杂疾病的遗传影响和生物学变异至关重要。近年来，许多遗传学研究在队列中进行，除了基因组序列数据外，还在一段时间内收集每个受试者对感兴趣的性状的多种测量。这些研究不仅提供了对疾病状况更准确的评估，而且使研究人员能够研究基因对性状和疾病进展轨迹的影响。该项目侧重于开发新的关联测试方法，以在基因水平上分析测序基因组数据。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。在纵向遗传研究和来自电子医疗记录和基因组学（eMERGE）网络的数据中，表型特征和遗传变异可被视为功能数据。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，基于基因的分析是一种比单个遗传变异检测更强大的基因定位工具。本项目旨在建立随机功能回归模型和纵向序列核关联检验（LSKAT），以分析群体样本和家系或隐相关样本的纵向性状，并分析多效性状。利用FDA技术和基于核的方法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，构建了方差-协方差结构来模拟个体特征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。建议的方法和软件将使用真实数据集和模拟研究进行测试和改进。将开发便于使用的软件来实施建议的方法，并将向公众提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A comparison study of COVID-19 outbreaks in the United States between states with Republican and Democratic Governors

美国共和党和民主党州长各州之间 COVID-19 疫情的比较研究

• DOI: 10.3396/ijic.v17.20940
• 发表时间: 2021
• 期刊: International Journal of Infection Control
• 作者: Tang, Wen;Wang, Shuqi;Xiong, Liyan;Fang, Mengyu;Chiu, Chi-yang;Loffredo, Christopher;Fan, Ruzong
• 通讯作者: Fan, Ruzong

Stochastic functional linear models and Malliavin calculus

随机函数线性模型和 Malliavin 微积分

• DOI: 10.1007/s00180-021-01142-y
• 发表时间: 2022
• 期刊: Computational Statistics
• 影响因子: 1.3
• 作者: Fan, Ruzong;Fang, Hong-Bin
• 通讯作者: Fang, Hong-Bin

Gene‐based association analysis of survival traits via functional regression‐based mixed effect cox models for related samples

通过相关样本的基于功能回归的混合效应 Cox 模型，对生存性状进行基于基因的关联分析

• DOI: 10.1002/gepi.22254
• 发表时间: 2019
• 期刊: Genetic Epidemiology
• 影响因子: 2.1
• 作者: Chiu, Chi‐yang;Zhang, Bingsong;Wang, Shuqi;Shao, Jingyi;Lakhal‐Chaieb, M'Hamed Lajmi;Cook, Richard J.;Wilson, Alexander F.;Bailey‐Wilson, Joan E.;Xiong, Momiao;Fan, Ruzong
• 通讯作者: Fan, Ruzong

Stochastic functional linear models for gene-based association analysis of quantitative traits in longitudinal studies

纵向研究中基于基因的数量性状关联分析的随机函数线性模型

• 发表时间: 2022
• 期刊: Statistics and its interface
• 影响因子: 0.8
• 作者: Bingsong Zhang, Shuqi Wang

Gene‐based analysis of bi‐variate survival traits via functional regressions with applications to eye diseases

通过功能回归对双变量生存特征进行基于基因的分析并应用于眼部疾病

• DOI: 10.1002/gepi.22381
• 发表时间: 2021
• 期刊: Genetic Epidemiology
• 影响因子: 2.1
• 作者: Zhang, Bingsong;Chiu, Chi‐Yang;Yuan, Fang;Sang, Tian;Cook, Richard J;Wilson, Alexander F.;Bailey‐Wilson, Joan E.;Chew, Emily Y.;Xiong, Momiao;Fan, Ruzong
• 通讯作者: Fan, Ruzong