Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.
Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks.
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DOI：
10.1093/bioinformatics/btw282
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发表时间：
2016-06-15
期刊：
影响因子：
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通讯作者：
Dudley JT
中科院分区：
文献类型：
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作者：
Glicksberg BS;Li L;Badgeley MA;Shameer K;Kosoy R;Beckmann ND;Pho N;Hakenberg J;Ma M;Ayers KL;Hoffman GE;Dan Li S;Schadt EE;Patel CJ;Chen R;Dudley JT
动机：种族群体代表性不足是表型组学研究的一个重要挑战和主要差距。目前大多数人类表型组学研究主要基于欧洲人群；因此，将其扩大到考虑其他人口群体是一个重要的挑战。一种方法是利用 EMR 数据库中的数据，其中包含来自不同人口统计数据和血统的患者数据。这种数据的种族代表性不足对医疗保健服务和可操作性的影响可能是深远的。据我们所知，我们的工作是首次尝试对三个不同人群（即白种人（EA）、非裔美国人（AA）和西班牙裔/拉丁裔（HL））的疾病网络进行比较、人群规模分析。结果：我们比较了 1 025 573 名患者临床人群中 1988 种疾病和 37 282 种疾病对的易感性概况和时间连接模式。因此，我们揭示了 EA、AA 和 HL 人群之间在疾病易感性、时间模式、网络结构和潜在疾病联系方面存在显着差异。我们发现 EA 队列有 2158 种显着合并症，AA 组有 3265 种，HL 组有 672 种。我们进一步概述了每个人群特有的关键疾病对关联以及这些对的分类丰富。最后，我们确定了 51 种关键“中心”疾病，它们是以种族为中心的网络中的焦点，具有特殊的临床重要性。纳入特定种族的疾病合并症模式将产生更准确、更完整的疾病总体情况，并可以支持更准确地理解疾病关系和患者管理，以改善临床结果。联系方式： rong.chen@mssm.edu 或 joel.dudley@mssm.edu 补充信息： 补充数据可在生物信息学在线获取。
Motivation: Underrepresentation of racial groups represents an important challenge and major gap in phenomics research. Most of the current human phenomics research is based primarily on European populations; hence it is an important challenge to expand it to consider other population groups. One approach is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability. To the best of our knowledge, our work is the first attempt to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). Results: We compared susceptibility profiles and temporal connectivity patterns for 1988 diseases and 37 282 disease pairs represented in a clinical population of 1 025 573 patients. Accordingly, we revealed appreciable differences in disease susceptibility, temporal patterns, network structure and underlying disease connections between EA, AA and HL populations. We found 2158 significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. We further outlined key disease pair associations unique to each population as well as categorical enrichments of these pairs. Finally, we identified 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance. Incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes. Contacts: rong.chen@mssm.edu or joel.dudley@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online.