Methodology in phenome-wide association studies: a systematic review

现象 计算机科学 梅德林 工作流程 生物信息学 数据挖掘 数据科学 遗传学 生物 数据库 表型 生物化学 基因
作者
Lijuan Wang,Xiaomeng Zhang,Xiangrui Meng,Fotios Koskeridis,Andrea Georgiou,Lili Yu,Harry Campbell,Evropi Τheodoratou,Xue Li
出处
期刊:Journal of Medical Genetics [BMJ]
卷期号:58 (11): 720-728 被引量:12
标识
DOI:10.1136/jmedgenet-2021-107696
摘要

Phenome-wide association study (PheWAS) has been increasingly used to identify novel genetic associations across a wide spectrum of phenotypes. This systematic review aims to summarise the PheWAS methodology, discuss the advantages and challenges of PheWAS, and provide potential implications for future PheWAS studies. Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases were searched to identify all published PheWAS studies up until 24 April 2021. The PheWAS methodology incorporating how to perform PheWAS analysis and which software/tool could be used, were summarised based on the extracted information. A total of 1035 studies were identified and 195 eligible articles were finally included. Among them, 137 (77.0%) contained 10 000 or more study participants, 164 (92.1%) defined the phenome based on electronic medical records data, 140 (78.7%) used genetic variants as predictors, and 73 (41.0%) conducted replication analysis to validate PheWAS findings and almost all of them (94.5%) received consistent results. The methodology applied in these PheWAS studies was dissected into several critical steps, including quality control of the phenome, selecting predictors, phenotyping, statistical analysis, interpretation and visualisation of PheWAS results, and the workflow for performing a PheWAS was established with detailed instructions on each step. This study provides a comprehensive overview of PheWAS methodology to help practitioners achieve a better understanding of the PheWAS design, to detect understudied or overstudied outcomes, and to direct their research by applying the most appropriate software and online tools for their study data structure.
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