Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA.
Environ Res. 2020 Apr;183:109275. doi: 10.1016/j.envres.2020.109275. Epub 2020 Feb 19.
Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.
环境全基因组关联研究(EWAS)是一种针对健康结果的非靶向、不可知和生成假设的方法,类似于全基因组关联研究(GWAS)。虽然 GWAS 的设计、方法和可重复性标准已经建立,但 EWAS 却带来了许多挑战。我们系统地回顾了已发表的 EWAS 文献,以对设计和方法中的范围、影响、分析方法类型和开放挑战进行分类。通过多次查询,在 Web of Science 和 PubMed 数据库中搜索 EWAS 文章,以确定 2010 年 1 月至 2018 年 12 月期间的 EWAS 文章,并按照系统评价和荟萃分析的首选报告项目(PRISMA)报告标准进行系统评价。符合纳入标准的 23 篇文章被纳入。对于每项研究,我们对数据源、研究结果的定义、环境变量集以及数据工程/分析方法进行了分类,例如邻里定义、变量标准化、处理多重假设检验、模型选择和验证。我们发现数据来源的利用有限,分析方法的异质性高,以及缺乏复制。尽管 EWAS 的应用前景广阔,但 EWAS 的进一步发展将需要改进数据源、标准化研究设计以及严格测试方法。