Beginning in the early 2000s, the accumulation of biospecimens linked to electronic health records (EHRs) made possible genome-phenome studies (i.e., comparative analyses of genetic variants and phenotypes) using only data collected as a by-product of typical health care. In addition to disease and trait genetics, EHRs proved a valuable resource for analyzing pharmacogenetic traits and developing reverse genetics approaches such as phenome-wide association studies (PheWASs). PheWASs are designed to survey which of many phenotypes may be associated with a given genetic variant. PheWAS methods have been validated through replication of hundreds of known genotype-phenotype associations, and their use has differentiated between true pleiotropy and clinical comorbidity, added context to genetic discoveries, and helped define disease subtypes, and may also help repurpose medications. PheWAS methods have also proven to be useful with research-collected data. Future efforts that integrate broad, robust collection of phenotype data (e.g., EHR data) with purpose-collected research data in combination with a greater understanding of EHR data will create a rich resource for increasingly more efficient and detailed genome-phenome analysis to usher in new discoveries in precision medicine.

中文翻译：

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广泛的关联研究，作为促进精密医学的工具。

从2000年代初开始，与电子健康记录（EHR）相关的生物标本的积累使得仅使用作为典型卫生保健副产品收集的数据进行基因组表型研究（即，遗传变异和表型的比较分析）成为可能。除疾病和性状遗传学外，EHR还被证明是用于分析药理遗传学特征和开发反向遗传学方法（例如全基因组关联研究（PheWAS））的宝贵资源。PheWAS被设计用来调查许多表型中的哪些可能与给定的遗传变异有关。PheWAS方法已经通过复制数百种已知的基因型-表型关联得到了验证，并且它们的使用在真正的多效性和临床合并症之间进行了区分，为遗传发现增加了背景，并有助于定义疾病亚型，并可能有助于重新调整药物用途。PheWAS方法也已被证明对研究收集的数据有用。未来将表型数据（例如EHR数据）的广泛，强大的收集与目标收集的研究数据结合在一起的进一步努力，以及对EHR数据的更深入的了解，将为日益高效和详细的基因组表型分析提供丰富的资源，以用于精密医学的新发现。