Department of Biomedical Informatics, Columbia University, New York, NY, USA.
Department of Biomedical Informatics, Columbia University, New York, NY, USA; Institute for Genomic Medicine, Columbia University, New York, NY, USA.
Cell. 2018 Jun 14;173(7):1692-1704.e11. doi: 10.1016/j.cell.2018.04.032. Epub 2018 May 17.
Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.
遗传力对于理解疾病的生物学原因至关重要，但需要费力地招募患者并确定表型。电子健康记录 (EHR) 被动地捕获广泛的临床相关数据，并为研究通常无法获得的特征的遗传力提供了资源。EHR 包含通过患者紧急联系表收集的近亲信息，但直到现在，这些数据在研究中都未被使用。我们在三个学术医疗中心挖掘了紧急联系数据，在保护患者隐私的同时确定了 740 万种家族关系。确定的关系与遗传相关性一致。我们使用 EHR 数据计算了 500 种疾病表型的遗传力估计值。总体而言，这些估计值与文献和各站点的结果一致。不一致表明 EHR 研究具有独特的局限性和机会。这些分析验证了 EHR 用于遗传学和疾病研究的用途。
