Genomics, EHRs, GPUs, and Next Generation Computational Statistics

摘要 统计遗传学未来的挑战是巨大的。数据集继续增长；对106个案例的研究 107个标记已经成为可行的，但当前的算法和软件无法扩展到这个规模。我们需要 重新思考和重建我们的许多统计分析技术和工具，以有效地进行扩展。此外，健康数据 很快就会从移动和可穿戴设备上普遍收集，大大增加了它的数量和实用价值。 精准健康和预测性医学进一步加大了赌注。同时，计算的本质 日新月异。为了利用硬件的进步，特别是无处不在的并行计算，新的 必须采用统计方法和算法以及新的方案编制范例。 这项更新建议的目标是应用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个对象的研究，例如美国能源部 退伍军人事务部的百万退伍军人计划(MVP)和英国生物库。生物库规模的数据集有许多本- Efits，特别是检测共同涉及的许多基因中每一个的微妙影响的潜在能力 疾病。fi的另一个好处是，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。要科学有效地实施fi 分析这些海量数据集需要当前统计遗传学工具的进步。有效统计 分析有多种形式：在更少迭代中收敛的算法、容纳所有数据的强大统计 可用数据，以及利用大规模并行计算硬件的计算方法 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 生物库规模的数据集，用于许多具有计算挑战性的统计遗传学任务，包括全基因组 使用来自电子健康记录(EHR)的特征数据进行关联研究(GWAS)。更广泛地说，我们的算法 Focus将惠及所有由计算统计和高维优化驱动的科学领域(fificfi)。 当然，为了使统计算法开发立即有用，它必须伴随着快速、 易于使用的软件。我们将及时交付开放源码软件，以(1)实现互动性和可重复性 具有信息性中间结果的分析，(2)提供高质量的图形，(3)扩展到大数据分析，(4) 支持并行和分布式计算，(5)适应快速硬件发展，(6)允许云计算， 以及(7)促进临床医生、遗传学家、统计学家和计算机科学家之间的轻松交流。近期 计算机语言的突破使所有这些目标都触手可及。 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 用于现代海量基因和EHR数据的软件。数值精度、计算效率fi精度和软件 可持续发展是我们的首要任务。我们将提供一个统一的、跨平台的、高水平的、可重现的、交互式的fi 即使对于生物库规模的数据集，分析环境也是快速和有效的fi。

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.