| 蒋方圆,王丽娟,孙静,余丽丽,周璇,朱益民,李雪.全表型组关联研究方法学研究进展[J].Chinese journal of Epidemiology,2022,43(7):1154-1161 |
| 全表型组关联研究方法学研究进展 |
| Research progress in the methodology used in phenome-wide association studies |
| Received:November 04, 2021 |
| DOI：10.3760/cma.j.cn112338-20211104-00853 |
| KeyWord: 全表型组关联研究 生物医疗大数据 电子化病历 多效性 |
| English Key Word: Phenome-wide association study Biomedical big data Electronic medical records Pleiotropy |
| FundProject: |
| Author Name | Affiliation | E-mail | | Jiang Fangyuan | Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | | | Wang Lijuan | Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | | | Sun Jing | Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | | | Yu Lili | Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | | | Zhou Xuan | Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | | | Zhu Yimin | Department of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | | | Li Xue | Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China | xueli157@zju.edu.cn |
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| Abstract: |
| 全表型组关联研究（PheWAS）是一种反向遗传学分析方法，旨在研究哪些表型可能与给定的遗传变异相关联。随着生物医疗数据库和电子病历信息的开放获取，PheWAS已逐渐成为探索暴露因素与多种健康结局之间关联的有效方法。这种方法具有同时探索某一种暴露与多种疾病表型之间的统计学关联的独特优势，从而有助于揭示多重因果关联以及各疾病间共同的致病机制。然而，PheWAS目前也面临诸多挑战。该方法本身存在一定的局限性，包括工具变量的选择是否具有代表性以及繁重的多重校正负担。此外，如何应用生物学知识阐释研究结果是PheWAS的另一重点问题。本文将围绕PheWAS方法学进行概述，以期为后续更好地开展PheWAS提供思路和建议。 |
| English Abstract: |
| Phenome-wide association study (PheWAS) is a reverse genetic analysis method to identify the potential phenotypes associated with genetic variations. With the increasing availability of biomedical databases and electronic medical records (EMR), PheWAS has gradually become an effective tool to unveil the relationships between exposure and a broad range of health phenotypes. The unique advantage of this method is that it can simultaneously explore the associations of a specific exposure with a variety of disease outcomes, thus helping to reveal multiple causal relationships and the shared pathogenic mechanisms among diseases. However, PheWAS has limitations, including selecting instrumental variables and the heavy burden of various corrections. In addition, how to interpret the biological mechanisms underlying significant findings is another crucial issue of PheWAS. This review will focus on the methodology and application of PheWAS to provide meaningful suggestions and insights for future studies. |
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