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首页> 外文会议>Pacific Symposium on Biocomputing >INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES
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INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES

机译：将临床实验室措施和ICD-9代码诊断整合到苯胺 - 宽协会研究中

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Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCode~(TM) biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.
机译：电子健康记录（EHR）为发现提供了综合资源，允许前所未有的探索遗传建筑对健康和疾病的影响。 EHR的数据还允许探索健康和疾病之间健康措施之间的复杂相互作用。基于EHR的研究产生的发现提供了识别临床决策遗传变异的重要信息。由于EHR内收集的信息广度，使用基于EHR的数据发现的挑战是从遗传变异到表型的遗传变异开发高通量工具的开发。菲尼 - 范围的协会研究（PPEPAS）提供了一种探讨遗传变异和综合表型测量之间的关联的方法，产生新的假设，并且还暴露遗传建筑和结果之间的复杂关系，包括肺炎。基于EHR的PPEWAS主要评估了来自国际疾病的国际分类的案例/控制状态，第九版（ICD-9）代码。虽然这些研究突出显示通过PHEPAS发现，但在EHR内收集的临床实验室措施的丰富资源可以更好地用于高通量PPEMAS分析和发现。为了更好地利用这些资源并丰富Phewas关联结果，我们开发了一种用于从EHR数据中提取各种临床实验室措施的声音方法。我们从景观景观的参与者的去鉴定EHR中提取了第一组21个临床实验室措施，并计算了这些实验室措施中位数的12,039个科目。接下来，我们评估了这21个临床实验室中值和635,5525个遗传变异之间的关联，为21例临床实验室措施进行了全基因组关联研究（GWAS）。然后，我们计算了这些GWA之间的SNP之间的关联，通过我们的Bonferroni定义的P值截止和165个ICD-9代码。通过GWAS，我们发现了一系列结果复制了已知的关联，以及一些具有较少研究临床实验室措施的可能性新的关联。我们发现大多数PPEMAS ICD-9诊断与与相同SNP相关的临床实验室措施高度相关。向前发展，我们将评估进一步的表型并扩大成功提取方法的方法，用于研究和Phewas使用的临床实验室测量。这些开发对于扩展基于EHR的发现的PPEPAS方法很重要。

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