III: Medium: Scalable Machine Learning for Genome-Wide Association Analyses

在过去的十年中，全基因组关联研究（GWAS）已经发现了与许多疾病以及其他复杂表型相关的遗传变异。尽管他们取得了成功，但我们对遗传变化如何影响表型的理解仍然存在重大差距。这些差距，再加上高通量技术的进步，以衡量遗传变异，促使GWAS的规模越来越大。然而，这些研究的规模和复杂性所带来的统计和计算挑战是实现其承诺的关键瓶颈。 可扩展ML的这些最新进展为GWAS领域的范式转移提供了潜力。然而，这些概念还有待于在GWAS建模和测试问题的上下文中进行严格的探索。探索这些领域的交集带来了全新的统计和计算挑战。该团队将开发一套针对大规模现代基因组学数据集的建模和测试方法。我们将建立的技术包括低秩矩阵近似，核方法和矩阵完成。他们还将提供为并行和分布式计算环境量身定制的开源软件，以促进方法的广泛采用。通过可扩展机器学习的透镜探索GWAS引入了几个研究方向，并需要开发新的算法和分析。首先，许多可扩展的ML研究的重点是预测的统计任务，而GWAS推理问题也强调假设检验和参数估计。在这些新的环境中描述可扩展ML方法的行为是一个具有挑战性的开放问题。该团队将开发原则性的GWAS建模和测试方法。这些结果也引起了可扩展ML社区的极大兴趣。其次，虽然可扩展的ML技术被设计为通用和领域不可知的，但GWAS设置引入了丰富的生物学驱动的领域知识，需要利用这些知识来提高推理的质量。将开发能够对这种先验知识进行编码，同时仍然允许有效推理的统计模型。最终，这些算法将被实现为这些核心建模和测试问题的高效并行和分布式算法，并开发利用现代计算基础设施的强大的开源实现。1所提出的方法将大大提高当前GWAS分析的可扩展性，一方面，同时使越来越现实的基因组模型的开发成为可能。合作和开放源代码的人工制品将使人类遗传学界能够广泛采用这些方法。该项目将导致基因组学和机器学习社区在加州大学洛杉矶分校和外部更密切的互动。

Over the past decade, genome-wide association studies (GWAS) have discovered genetic variants associated with numerous diseases as well as other complex phenotypes. Despite their success, major gaps remain in our understanding of how genetic changes affect phenotype. These gaps, coupled with advances in high-throughput technologies to measure genetic variation, have motivated GWAS of increasingly larger scale. However, the statistical and computational challenges posed by the scale and complexity of these studies present a critical bottleneck in realizing their promise. These recent advances in scalable ML provide the potential for paradigm-shifting advances in the field of GWAS. However, these concepts have yet to be rigorously explored in the context of the GWAS modeling and testing problems. Exploring the intersection of these domains introduces fundamentally new statistical and computational challenges. The team will develop a suite of modeling and testing methods that target massive modern genomics datasets. The techniques that we will build upon include low-rank matrix approximation, kernel methods and matrix completion. They will also provide open-source software tailored to parallel and distributed computing environments to facilitate wide-spread adoption of methods.Exploring GWAS through the lens of scalable machine learning introduces several research directions and requires the development of novel algorithms and analyses. Firstly, the focus of much scalable ML research has been on the statistical task of prediction, while GWAS inference problems also emphasize hypothesis testing and parameter estimation. Characterizing the behavior of scalable ML methods in these novel settings is a challenging open problem. The team will develop principled GWAS modeling and testing methods. The results to also be of great interest to the scalable ML community. Secondly, while scalable ML techniques are designed to be general purpose and domain-agnostic, the GWAS setting introduces rich biologically-motivated domain knowledge that needs to be leveraged to improve the quality of inference. Statistical models that are able to encode this prior knowledge while still permitting efficient inference will be developed. Ultimately the algorithms will be implemented as efficient parallel and distributed algorithms for these core modeling and testing problems, and develop robust open-source implementations that leverage modern computing infrastructure.1The proposed methods will dramatically improve the scalability of current GWAS analyses, on the one hand, while enabling the development of increasingly realistic genomic models, on the other. Collaborations and open-source artifacts will enable the wide-spread adoption of these methods by the human genetics community. This project will lead to a closer interaction of the genomics and machine learning communities at UCLA and outside.