Phenotypes extracted from Electronic Health Records (EHRs) are increasingly prevalent in genetic studies. EHRs contain hundreds of distinct clinical laboratory test results, providing a trove of health data beyond diagnoses. Such lab data is complex and lacks a ubiquitous coding scheme, making it more challenging than diagnosis data. Here we describe the first large-scale cross-health system genome-wide association study (GWAS) of EHR-based quantitative laboratory-derived phenotypes. We meta-analyzed 70 lab traits matched between the BioVU cohort from the Vanderbilt University Health System and the Michigan Genomics Initiative (MGI) cohort from Michigan Medicine. We show high replication of known association for these traits, validating EHR-based measurements as high-quality phenotypes for genetic analysis. Notably, our analysis provides the first replication for 699 previous GWAS associations across 46 different traits. We discovered 31 novel associations at genome-wide significance for 22 distinct traits, including the first reported associations for two lab-based traits. We replicated 22 of these novel associations in an independent tranche of BioVU samples. The summary statistics for all association tests are freely available to benefit other researchers. Finally, we performed mirrored analyses in BioVU and MGI to assess competing analytic practices for EHR lab traits. We find that using the mean of all available lab measurements provides a robust summary value, but alternate summarizations can improve power in certain circumstances. This study provides a proof-of-principle for cross health system GWAS and is a framework for future studies of quantitative EHR lab traits.

从电子健康记录 (EHR) 中提取的表型在遗传研究中越来越普遍。EHR 包含数百个不同的临床实验室测试结果，提供诊断之外的大量健康数据。此类实验室数据复杂且缺乏通用的编码方案，使其比诊断数据更具挑战性。在这里，我们描述了基于 EHR 的定量实验室衍生表型的第一个大规模跨健康系统全基因组关联研究 (GWAS)。我们对范德比尔特大学卫生系统的 BioVU 队列和密歇根医学的密歇根基因组学计划 (MGI) 队列之间匹配的 70 个实验室特征进行了荟萃分析。我们展示了这些性状已知关联的高度复制，验证了基于 EHR 的测量作为用于遗传分析的高质量表型。尤其，我们的分析首次复制了 46 个不同性状的 699 个先前的 GWAS 关联。我们发现了 22 个不同性状的 31 个具有全基因组意义的新关联，包括首次报道的两个基于实验室的性状的关联。我们在独立的 BioVU 样本中复制了 22 个这些新关联。所有关联测试的汇总统计数据均可免费提供，以帮助其他研究人员。最后，我们在 BioVU 和 MGI 中进行了镜像分析，以评估 EHR 实验室特征的竞争分析实践。我们发现，使用所有可用实验室测量值的平均值可提供稳健的汇总值，但在某些情况下，交替汇总可以提高功效。