PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger
PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger
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DOI：
10.1016/j.ajhg.2018.02.017
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发表时间：
2018-04-05
影响因子：
9.8
通讯作者：
Pendergrass, Sarah A.
中科院分区：
文献类型：
--
作者：
Verma, Anurag;Lucas, Anastasia;Pendergrass, Sarah A.
迄今为止，大多数全表型关联研究（PheWAS）使用了少量到中等数量的SNP与表型数据关联。我们进行了一项大规模的单队列PheWAS，使用电子健康记录（EHR）衍生的病例对照状态，使用国际疾病分类第9版（ICD-9）代码和25个中位数临床实验室指标进行541例诊断。我们计算了这些诊断与性状之间的关联，其中38，662个个体具有类似于630，000个常见频率SNP，次要等位基因频率>0.01。在这个景观PheWAS中，我们探索了疾病和性状的结果，将结果与以前在全基因组关联研究（GWAS）中报道的结果以及以前发表的PheWAS进行了比较。我们进一步利用了从蛋白质编码到调控区域的功能影响的背景，对这些关联提供了更深入的解释。该PheWAS的综合性质允许新的假设生成，用于未来表型算法开发的进一步研究的表型鉴定，以及交叉表型关联的鉴定。
Most phenome-wide association studies (PheWASs) to date have used a small to moderate number of SNPs for association with phenotypic data. We performed a large-scale single-cohort PheWAS, using electronic health record (EHR)-derived case-control status for 541 diagnoses using International Classification of Disease version 9 (ICD-9) codes and 25 median clinical laboratory measures. We calculated associations between these diagnoses and traits with similar to 630,000 common frequency SNPs with minor allele frequency >0.01 for 38,662 individuals. In this landscape PheWAS, we explored results within diseases and traits, comparing results to those previously reported in genome-wide association studies (GWASs), as well as previously published PheWASs. We further leveraged the context of functional impact from protein-coding to regulatory regions, providing a deeper interpretation of these associations. The comprehensive nature of this PheWAS allows for novel hypothesis generation, the identification of phenotypes for further study for future phenotypic algorithm development, and identification of cross-phenotype associations.