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

生物库数据的特点是其数量、速度、多样性和准确性(4V)。两个最好的例子是美国退伍军人事务部的百万退伍军人项目(MVP)和英国生物库。这些数据很大，有多达100万个主题，占用了TB的存储(容量)。他们的样本规模和数据内容都在不断增加(速度)。它们包含不同的信息源：基因组、电子健康记录、可穿戴设备、图像，以及最近的新冠肺炎数据(种类繁多)。此外，它们充满了错误性和不准确性(准确性)。该项目寻求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调查人员最近在分析MVP和英国生物库数据方面的经验，并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的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、最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外联活动相结合，包括开发新的课程和软件以及指导学生。这些活动旨在让包括妇女和少数族裔在内的各种学生接触到用于大数据分析的最先进的统计和计算技术。将对三组问题进行调查。(1)电子健康档案和可穿戴设备在生物库中产生海量的纵向数据。在许多研究中，受试者内部对纵向结果的可变性是主要的科学兴趣。在研究血压变异性和血糖变异性对糖尿病并发症影响的基础上，PI提出了一种稳健和可扩展的方法，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。与已有方法相比，该方法对分布误判具有较强的鲁棒性，且速度快了数量级。计算可伸缩性使其成为基于生物库中大量纵向数据研究性状变异的有力工具。(2)PIS将开发一类新的在线学习算法，它将统计学中的优化-最小化原理与随机邻近迭代算法相结合。新的算法适用于更广泛的模型类别，而且显然更稳定和健壮。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在遗传学和生物统计学中起核心作用的大方差分量模型的小Bootstraps(BLB)方法。由于巨大协方差矩阵的倒置，这样的模型对于生物库数据是令人望而却步的。该方法将大量的方差分量模型分解成多个较小的模型，然后并行自举，然后求平均。新方法将能够量化生物库数据中复杂特征的遗传力和遗传相关性。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.