The use of electronic health records for psychiatric phenotyping and genomics.
The use of electronic health records for psychiatric phenotyping and genomics.
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DOI：
10.1002/ajmg.b.32548
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发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Smoller JW
中科院分区：
文献类型：
--
作者：
Smoller JW
电子健康记录（HER）在医疗保健系统中的广泛采用创造了大量且不断增长的临床数据资源，并为基于人群的研究提供了新的机会。特别是，将EHR与生物样本库中的生物标本和基因组数据联系起来，可能有助于解决基因研究中的一个限速研究问题：需要大样本量。利用这些资源的主要障碍是需要建立从EHR中提取的表型的有效性。对于精神病遗传学研究来说，这是一个特殊的挑战，因为诊断是基于患者报告和临床医生的观察，而这些观察可能无法很好地反映在账单代码或叙述记录中。本文综述了基于EHR的表型分析在精神病遗传学研究中的应用。越来越多的研究表明，具有高阳性预测值的诊断算法可以从EHR中获得，特别是当结构化数据辅以文本挖掘方法时。这样的算法使得大规模病例对照研究的半自动表型分析成为可能。此外，EHR数据库的规模和范围已成功地用于识别表型亚组和推导出纵向风险预测的算法。基于EHR的基因组学特别适合于假定风险基因的快速查找复制，多效性研究（全表型关联研究或PheWAS），遗传网络和表型重叠的研究以及药物基因组学研究。EHR表型分析在精神病基因组研究中相对未得到充分利用，但可能成为推进精确精神病学的关键组成部分。
The widespread adoption of electronic health record (HERs) in healthcare systems has created a vast and continuously growing resource of clinical data and provides new opportunities for population-based research. In particular, the linking of EHRs to biospecimens and genomic data in biobanks may help address what has become a rate-limiting study for genetic research: the need for large sample sizes. The principal roadblock to capitalizing on these resources is the need to establish the validity of phenotypes extracted from the EHR. For psychiatric genetic research, this represents a particular challenge given that diagnosis is based on patient reports and clinician observations that may not be well-captured in billing codes or narrative records. This review addresses the opportunities and pitfalls in EHR-based phenotyping with a focus on their application to psychiatric genetic research. A growing number of studies have demonstrated that diagnostic algorithms with high positive predictive value can be derived from EHRs, especially when structured data are supplemented by text mining approaches. Such algorithms enable semi-automated phenotyping for large-scale case-control studies. In addition, the scale and scope of EHR databases have been used successfully to identify phenotypic subgroups and derive algorithms for longitudinal risk prediction. EHR-based genomics are particularly well-suited to rapid look-up replication of putative risk genes, studies of pleiotropy (phenomewide association studies or PheWAS), investigations of genetic networks and overlap across the phenome, and pharmacogenomic research. EHR phenotyping has been relatively under-utilized in psychiatric genomic research but may become a key component of efforts to advance precision psychiatry.
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影响因子：
30.8
作者：
Dahl A;Iotchkova V;Baud A;Johansson Å;Gyllensten U;Soranzo N;Mott R;Kranis A;Marchini J
通讯作者：
Marchini J
影响因子：
2.9
作者：
Castro VM;Gallagher PJ;Clements CC;Murphy SN;Gainer VS;Fava M;Weilburg JB;Churchill SE;Kohane IS;Iosifescu DV;Smoller JW;Perlis RH
通讯作者：
Perlis RH
DOI：
10.1146/annurev-genom-090314-024956
发表时间：
2016-08-31
影响因子：
8.7
作者：
Denny JC;Bastarache L;Roden DM
通讯作者：
Roden DM
DOI：
10.1136/bmj.f288
发表时间：
2013-01-29
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Castro VM;Clements CC;Murphy SN;Gainer VS;Fava M;Weilburg JB;Erb JL;Churchill SE;Kohane IS;Iosifescu DV;Smoller JW;Perlis RH
通讯作者：
Perlis RH
影响因子：
4.5
作者：
Ge T;Chen CY;Neale BM;Sabuncu MR;Smoller JW
通讯作者：
Smoller JW