Collaborative Research: Adaptive Testing and Rare-Event Analysis of High-Dimensional Data

协作研究：高维数据的自适应测试和罕见事件分析

• 批准号: 1711226
• 负责人: Wei Pan
• 金额: $ 20万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1711226&HistoricalAwards=false
• 关键词: Collaborative; Research; Adaptive; Testing; Rare

This project aims at developing adaptively powerful testing procedures for high-dimensional data with applications in genetics, genomics and neuroimaging. Due to recent biotechnological advances, large amounts of high-throughput and high-dimensional molecular and imaging data have been collected, resulting in a number of new and challenging statistical questions. One question is how polygenic testing in genome-wide association studies (GWAS) may be used to answer whether some of the millions of genetic variants are associated with a complex disease like Alzheimer's disease. The answer to this question is important to uncovering disease-related genes, and thus developing effective prevention and treatment strategies. The focus on rigorous hypothesis testing to avoid false discoveries, while maximizing the chance for true discoveries, is critical to modern genetic, genomic and other omic studies. The methods will be applied to data related to Alzheimer's disease, for which currently there is no cure, and more powerful analysis methods are urgently needed to unravel the underlying biology. Graduate students will be involved in the conduct of the research and development of the computational tools, and publicly available software packages will be developed for use by other biomedical researchers.This research will advance the frontiers of modern statistical methodology in hypothesis testing with high-dimensional data and related rare event assessment. Powerful adaptive methods for testing high-dimensional mean parameters in generalized linear models as well as high-dimensional covariance matrix structures will be developed. The adaptive test statistics are constructed based on high-dimensional high-order von Mises V-statistics and U-statistics, and will provide uniformly high power against sparse, dense, as well as moderately sparse or dense signals for flexible asymptotic regimes. Another thrust of the research deals with the challenging and important rare-event estimation problem in analysis of genome-wide molecular and neuroimaging data, where a high stringent statistical significance level is usually needed. To evaluate such small probabilities, the research will lead to theoretical tail probability approximations as well as efficient Monte Carlo methods using non-standard change-of-measure techniques.

该项目旨在为遗传学、基因组学和神经影像学中的高维数据开发自适应的强大测试程序。 由于最近的生物技术的进步，大量的高通量和高维的分子和成像数据已被收集，导致了一些新的和具有挑战性的统计问题。 一个问题是，全基因组关联研究（GWAS）中的多基因检测如何用于回答数百万遗传变异中的一些是否与阿尔茨海默病等复杂疾病相关。 这个问题的答案对于揭示疾病相关基因，从而制定有效的预防和治疗策略非常重要。对严格假设检验的关注，以避免错误的发现，同时最大限度地提高真正发现的机会，对现代遗传学，基因组学和其他组学研究至关重要。 这些方法将应用于与阿尔茨海默病相关的数据，目前还没有治愈方法，迫切需要更强大的分析方法来揭示潜在的生物学。研究生将参与计算工具的研究和开发，并将开发公开可用的软件包供其他生物医学研究人员使用。这项研究将推进高维数据假设检验和相关罕见事件评估的现代统计方法学前沿。强大的自适应方法测试高维平均参数在广义线性模型以及高维协方差矩阵结构将开发。 自适应测试统计量是基于高维高阶冯米塞斯V统计量和U统计量构建的，并且将针对稀疏、密集以及中等稀疏或密集信号提供一致的高功率，以实现灵活的渐进状态。研究的另一个重点是处理具有挑战性和重要性的罕见事件估计问题，在全基因组分子和神经影像数据分析中，通常需要高严格的统计显著性水平。为了评估这样的小概率，研究将导致理论上的尾部概率近似值以及使用非标准测量变化技术的有效蒙特卡罗方法。

项目成果

A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models

• 发表时间: 2020-07
• 期刊: Journal of machine learning research : JMLR
• 作者: Chong Wu;Gongjun Xu;Xiaotong Shen;W. Pan

Integration of Enhancer-Promoter Interactions with GWAS Summary Results Identifies Novel Schizophrenia-Associated Genes and Pathways

• DOI: 10.1534/genetics.118.300805
• 发表时间: 2018-05
• 期刊: Genetics
• 影响因子: 3.3
• 作者: Chong Wu;W. Pan

Improved Use of Small Reference Panels for Conditional and Joint Analysis with GWAS Summary Statistics

• DOI: 10.1534/genetics.118.300813
• 发表时间: 2018-06-01
• 期刊: GENETICS
• 影响因子: 3.3
• 作者: Deng, Yangqing;Pan, Wei
• 通讯作者: Pan, Wei

Testing Genetic Pleiotropy with GWAS Summary Statistics for Marginal and Conditional Analyses

• DOI: 10.1534/genetics.117.300347
• 发表时间: 2017-12-01
• 期刊: GENETICS
• 影响因子: 3.3
• 作者: Deng, Yangqing;Pan, Wei
• 通讯作者: Pan, Wei