MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, United Kingdom.
Cold Spring Harb Perspect Med. 2021 Jun 1;11(6):a039248. doi: 10.1101/cshperspect.a039248.
The advent of large-scale, phenotypically rich, and readily accessible data provides an unprecedented opportunity for epidemiologists, statistical geneticists, bioinformaticians, and also behavioral and social scientists to investigate the causes and consequences of disease. Computational tools and resources are an integral component of such endeavors, which will become increasingly important as these data continue to grow exponentially. In this review, we have provided an overview of computational software and databases that have been developed to assist with analyses in causal inference. This includes online tools that can be used to help generate hypotheses, publicly accessible resources that store summary-level information for millions of genetic markers, and computational approaches that can be used to leverage this wealth of data to study causal relationships.
大规模、表型丰富且易于获取的数据的出现为流行病学家、统计遗传学家、生物信息学家，以及行为和社会科学家提供了一个前所未有的机会，使他们能够研究疾病的原因和后果。计算工具和资源是这些努力的一个组成部分，随着这些数据呈指数级增长，它们将变得越来越重要。在这篇综述中，我们提供了一个用于辅助因果推断分析的计算软件和数据库概述。这包括可用于帮助生成假设的在线工具、存储数百万个遗传标记汇总信息的公开可用资源，以及可用于利用这些丰富的数据研究因果关系的计算方法。
