Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits.
Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits.
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DOI：
10.1159/000381805
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发表时间：
2015
期刊：
影响因子：
1.8
通讯作者：
Bush WS
中科院分区：
文献类型：
--
作者：
Crawford DC;Goodloe R;Farber-Eger E;Boston J;Pendergrass SA;Haines JL;Ritchie MD;Bush WS
目前有限的资源需要DNA和表型替代传统的前瞻性基于人群的流行病学收集。为了加速基因组的发现，重点是不同的人群，我们作为基因与环境相关的流行病学架构（EAGLE）研究的一部分，访问了范德比尔特大学生物资料库BioVU中可用的所有非欧洲美国样本（n=15,863），这些样本与去识别的电子医疗记录相关，作为使用基因组学和流行病学（PAGE） I研究的更大人群架构的一部分进行基因组研究。鉴于先前的研究已告诫不要将临床收集的数据与流行病学收集的数据进行二次使用，我们在此介绍EAGLE BioVU的特征，包括成人和儿科患者的账单和诊断（ICD-9）代码分布，以及对选定的健康指标(体重指数、血糖、糖化血红蛋白、HDL-C、LDL-C、和甘油三酯)，并与DNA样本（NHANES III; n=7,159和NHANES 1999-2002； n=7,839）相关联的基于人群的国家健康和营养检查调查（NHANES）。总的来说，账单和诊断代码的分布表明，这个临床样本是健康和患病患者的混合体，就像当代美国人口所期望的那样。在健康指标中几乎没有观察到偏倚，这表明该临床收集适合与传统流行病学队列一起进行基因组研究。
Present day limited resources demand DNA and phenotyping alternatives to the traditional prospective population-based epidemiologic collections. To accelerate genomic discovery with an emphasis on diverse populations, we as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study accessed all non-European American samples (n=15,863) available in BioVU, the Vanderbilt University biorepository linked to de-identified electronic medical records, for genomic studies as part of the larger Population Architecture using Genomics and Epidemiology (PAGE) I Study. Given previous studies have cautioned against the secondary use of clinically collected data compared with epidemiologically-collected data, we present here a characterization of EAGLE BioVU, including the billing and diagnostic (ICD-9) code distributions for adult and pediatric patients as well as comparisons made for select health metrics (body mass index, glucose, HbA1c, HDL-C, LDL-C, and triglycerides) with the population-based National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples (NHANES III; n=7,159 and NHANES 1999–2002; n=7,839). Overall, the distributions of billing and diagnostic codes suggest this clinical sample is mixture of healthy and sick patients like that expected for a contemporary American population. Little bias is observed among health metrics suggesting this clinical collection is suitable for genomic studies along with traditional epidemiologic cohorts.