DMS/NIGMS 2: Statistical Methods and Computational Algorithms for Biobank Data

DMS/NIGMS 2：生物样本库数据的统计方法和计算算法

• 批准号: 2054253
• 负责人: Hua Zhou
• 金额: $ 95.58万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054253&HistoricalAwards=false
• 关键词: DMS; NIGMS; Statistical; Methods; Computational

Biobank data is characterized by its volume, velocity, variety, and veracity (4V). Two prime examples are the Million Veteran Project (MVP) at US Veterans Affairs (VA) and UK Biobank. The data are big, with up to a million subjects and occupying terabytes of storage (volume). Their sample sizes and data content keep increasing (velocity). They contain heterogeneous sources of information: genome, electronic health record (EHR), wearable devices, images, and most recently, COVID-19 data (variety). Furthermore, they are fraught with missingness and inaccuracy (veracity). This project seeks to develop novel statistical methods and computational algorithms that address specific aspects of 4V. The methods are motivated by the principal investigators' recent experience in analyzing MVP and UK Biobank data, and are generalizable to any biobank or other generic big data. The methods provide solutions to some of the most pressing issues in biobank data analysis. The work will push forward several frontiers in statistics, optimization, and genetics. The research will be integrated with substantial education and outreach activities, including developing new courses and software and mentoring students. These activities aim to expose a diverse set of students, including women and minorities, to state-of-the-art statistical and computational techniques for big data analysis. Three sets of problems are to be investigated. (1) Electronic health records and wearable devices generate a vast amount of longitudinal data in biobanks. In many studies, the within-subject variability of a longitudinal outcome is the primary scientific interest. Motivated by studies of the impacts of blood pressure variability and glycemic variability on diabetes complications, the PIs propose a robust and scalable method for the estimation and inference of the effects of both time-varying and time-invariant predictors on within-subject variance. Compared to existing approaches, the method is robust to the distribution misspecification and orders of magnitude faster. Computational scalability makes it a powerful tool for studying trait variability based on massive longitudinal data in biobanks. (2) The PIs will develop a new class of online learning algorithms, which combine the majorization-minimization principle in statistics and the stochastic proximal iteration algorithm. The new algorithms apply to a broader class of models and are demonstrably more stable and robust. They help solve the volume issue and will be applied to genome-wide association studies of massive biobank data. (3) The PIs propose a bag of little bootstraps (BLB) approach for estimating massive variance component models, which play a central role in genetics and biostatistics. Fitting such models is prohibitive for biobank data because of the inversion of the giant covariance matrix. The BLB approach breaks the massive variance component model into many smaller ones, which are bootstrapped in parallel and then averaged. The new method will enable quantifying heritability and genetic correlation of complex traits in biobank data.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.

生物银行数据的特点是其体积，速度，种类和准确性（4V）。两个主要的例子是美国退伍军人事务部（VA）和英国生物银行的百万退伍军人项目（MVP）。数据很大，多达一百万个主题，占用TB的存储（体积）。他们的样本量和数据内容不断增加（速度）。它们包含不同的信息来源：基因组、电子健康记录（EHR）、可穿戴设备、图像，以及最近的COVID-19数据（各种）。此外，它们充满了遗漏和不准确（准确性）。该项目旨在开发新的统计方法和计算算法，以解决4V的特定方面。这些方法的动机是主要研究人员最近在分析MVP和英国生物库数据方面的经验，并且可推广到任何生物库或其他通用大数据。这些方法为生物库数据分析中一些最紧迫的问题提供了解决方案。这项工作将推动统计学，优化和遗传学的几个前沿领域。这项研究将与大量的教育和推广活动相结合，包括开发新的课程和软件以及指导学生。这些活动旨在使包括妇女和少数民族在内的各种学生接触最先进的统计和计算技术，以进行大数据分析。要研究三组问题。(1)电子健康记录和可穿戴设备在生物库中产生了大量的纵向数据。在许多研究中，纵向结果的受试者内变异性是主要的科学兴趣。受血压变异性和血糖变异性对糖尿病并发症的影响的研究的启发，PI提出了一种稳健且可扩展的方法，用于估计和推断时变和时不变预测因子对受试者内方差的影响。与现有方法相比，该方法对分布误设具有较强的鲁棒性，且速度快了几个数量级。计算可扩展性使其成为基于生物库中海量纵向数据研究性状变异性的有力工具。(2)PI将开发一类新的在线学习算法，它联合收割机了统计学中的优化-最小化原理和随机邻近迭代算法。新算法适用于更广泛的模型类别，并且证明更加稳定和强大。它们有助于解决数量问题，并将应用于海量生物库数据的全基因组关联研究。(3)PI提出了一种用于估计大规模方差分量模型的BLB方法，该方法在遗传学和生物统计学中起着核心作用。由于巨大的协方差矩阵的反演，拟合这样的模型对于生物库数据是禁止的。BLB方法将大方差分量模型分解为许多较小的模型，这些模型并行自举，然后平均。该奖项反映了NSF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

Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data.

有效的算法和实施纵向和竞争风险数据的半参数联合模型：与大规模生物库数据的应用。

• DOI: 10.1155/2022/1362913
• 发表时间: 2022
• 期刊: Computational and mathematical methods in medicine
• 作者: Li S;Li N;Wang H;Zhou J;Zhou H;Li G
• 通讯作者: Li G

Risk controlled decision trees and random forests for precision Medicine.

• DOI: 10.1002/sim.9253
• 发表时间: 2022-02-20
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Doubleday K;Zhou J;Zhou H;Fu H
• 通讯作者: Fu H

VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.

• DOI: 10.1214/21-aoas1491
• 发表时间: 2021-12
• 期刊: The annals of applied statistics
• 作者: Kim J;Shen J;Wang A;Mehrotra DV;Ko S;Zhou JJ;Zhou H
• 通讯作者: Zhou H

ORTHOGONAL TRACE-SUM MAXIMIZATION: TIGHTNESS OF THE SEMIDEFINITE RELAXATION AND GUARANTEE OF LOCALLY OPTIMAL SOLUTIONS.

正交迹和最大化：半定松弛的严格性和局部最优解的保证。

• DOI: 10.1137/21m1422707
• 发表时间: 2022
• 期刊: SIAM journal on optimization : a publication of the Society for Industrial and Applied Mathematics
• 作者: Won,Joong-Ho;Zhang,Teng;Zhou,Hua
• 通讯作者: Zhou,Hua

A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank.

• DOI: 10.1093/jamiaopen/ooad006
• 发表时间: 2023-04
• 期刊: JAMIA open
• 影响因子: 2.1