Genomics, EHRs, GPUs, and Next Generation Computational Statistics

基因组学、EHR、GPU 和下一代计算统计

• 批准号: 10264804
• 负责人: Eric Sobel
• 金额: $ 64.43万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-26 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264804
• 关键词: Admixture; Algorithmic Analysis; Algorithmic Software; Algorithms; Area; Attention; Big Data Methods; Cloud Computing; Code; Communication; Computer software; Computers; Computing Methodologies; Data; Data Set; Disease; Documentation; Dose; Educational workshop; Electronic Health Record; Environment; Evolution; Fostering; Future; Genes; Genetic; Genetic Programming; Genetic study; Genomics; Genotype; Goals; Haplotypes; Human Genome Project; Language; Lead; Maps; Medicine; Methods; Minor; Mission; Modeling; Modernization; Nature; Precision Health; Reproducibility; Research Personnel; Scientist; Software Validation; Statistical Algorithm; Statistical Data Interpretation; Systems Analysis; Techniques; Testing; Training; United States Department of Veterans Affairs; Update; Variant; Veterans; algorithm development; biobank; cluster computing; community building; design; design and construction; electronic data; flexibility; genetic analysis; genetic information; genetic pedigree; genome wide association study; handheld mobile device; health data; high dimensionality; human disease; improved; innovation; insurance claims; mathematical model; next generation; open source; parallel computer; programs; sex; social; software development; statistics; theories; tool; trait; wearable device; webinar

Abstract The future challenges of statistical genetics are enormous. Data sets continue to grow; studies with 106 cases and 107 markers have become feasible, but current algorithms and software do not scale to this size. We need to rethink and rebuild many of our statistical analysis techniques and tools to scale effectively. In addition, health data will soon be commonly collected from mobile and wearable devices, dramatically increasing its volume and utility. Precision health and predictive medicine raise the stakes even further. Concurrently, the nature of computing is rapidly changing. To take advantage of hardware advances, particularly ubiquitous parallel computing, new statistical approaches and algorithms and new programming paradigms must be brought online. This renewal proposal targets the application of state-of-the-art statistical techniques and tools to develop genetic analysis algorithms that can scale to studies with millions of subjects, such as the US Department of Veterans Affairs' Million Veteran Program (MVP) and the UK Biobank. Biobank-scale data sets have many ben- efits, particularly the potential power to detect the subtle effects of each of the many genes involved in common diseases. Another benefit is that these data sets can be more representative of the populace by including large numbers of people from multiple ancestries, different social strata, and all sexes. To effectively and efficiently analyze these massive data sets requires advances in the current statistical genetics tools. Effective statistical analysis takes many forms: algorithms that converge in fewer iterations, powerful statistics that accommodate all available data, and computational methods that take advantage of massively parallel computing hardware such as graphics processing units (GPUs) and other coprocessors. We will deliver algorithms that can directly handle biobank-scale data sets for many computationally-challenging statistical genetics tasks, including genome-wide association studies (GWAS) with trait data from electronic health records (EHRs). More generally, our algorithm focus will benefit all scientific fields driven by computational statistics and high-dimensional optimization. Of course, for statistical algorithm development to be immediately useful it must be accompanied by fast, easy-to-use software. We will promptly deliver open-source software that (1) enables interactive and reproducible analyses with informative intermediate results, (2) provides quality graphics, (3) scales to big data analytics, (4) embraces parallel and distributed computing, (5) adapts to rapid hardware evolution, (6) allows cloud computing, and (7) fosters easy communication between clinicians, geneticists, statisticians, and computer scientists. Recent breakthroughs in computer languages bring all these goals within reach. Our overall objective is the design and construction of state-of-the-art statistical genetics algorithms and software for modern, massive genetic and EHR data. Numerical accuracy, computational efficiency, and software sustainability are our priorities. We will deliver a unified, cross-platform, high-level, reproducible, interactive analysis environment that is fast and efficient even for biobank-scale data sets.

抽象的 统计遗传学的未来挑战是巨大的。数据集继续增长； 106例研究 和107个标记已经变得可行，但是当前的算法和软件并未扩展到此尺寸。我们需要 重新考虑和重建我们许多统计分析技术和工具以有效扩展。此外，健康数据 通常很快就会从移动设备和可穿戴设备中收集，从而大大增加了其数量和实用性。 精确的健康和预测医学进一步增加了赌注。同时，计算的性质 正在迅速变化。利用硬件进步，尤其是无处不在的并行计算，新的 统计方法和算法以及新的编程范例必须在线上。 该更新建议针对的是最先进的统计技术和工具来开发 遗传分析算法可以扩展到数百万受试者（例如美国）的研究 退伍军人事务的百万退伍军人计划（MVP）和英国生物库。生物银行规模的数据集有许多BEN- 拟合，特别是检测共同基因中每个基因的微妙影响的潜在能力 疾病。另一个好处是，这些数据集可以通过包括大型 来自多个祖先，不同社会阶层和所有性别的人数。有效和有效 分析这些大规模数据集需要在当前的统计遗传学工具中进展。有效的统计 分析采用多种形式：算法在更少的迭代中收敛，强大的统计数据可容纳所有人 可用的数据以及利用大量并行计算硬件这样的计算方法 作为图形处理单元（GPU）和其他协处理器。我们将提供可以直接处理的算法 许多计算统计遗传学任务，包括全基因组的生物银行规模数据集 协会研究（GWAS）与电子健康记录（EHRS）的性状数据有关。更一般地，我们的算法 焦点将有益于由计算统计和高维优化驱动的所有科学领域。 当然，要使统计算法的开发立即有用，必须伴随快速， 易于使用的软件。我们将迅速提供开源软件（1）启用交互式和可重现 具有信息中间结果的分析，（2）提供了质量图形，（3）大数据分析的量表，（4） 拥抱并行和分布式计算，（5）适应快速硬件的演变，（6）允许云计算， （7）促进了临床医生，遗传学家，统计学家和计算机科学家之间的轻松交流。最近的 计算机语言的突破带来了所有这些目标。 我们的总体目标是设计和构建最先进的统计遗传学算法和 现代，大量遗传和EHR数据的软件。数值准确性，计算效率和软件 可持续性是我们的优先事项。我们将提供一个统一的，跨平台，高级，可重复的，互动的 即使对于生物银行规模的数据集，也是快速且有效的分析环境。