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Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

机译:电子病历数据的全基因组关联研究与全基因组的关联研究数据的系统比较

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摘要

Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10−6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.
机译:候选基因和全基因组关联研究(GWAS)已确定可调节人类疾病风险的遗传变异。这些协会中有许多需要进一步研究以复制结果。在这里,我们报告电子病历(EMR)中的全现象组关联研究(PheWAS)范式的首次大规模应用,这是一种无偏见的复制和发现方法,可用于研究目标基因型和多种表型之间的关系。我们在欧洲祖先的13835个个体中扫描了3144个单核苷酸多态性(以前由GWAS牵涉到人类特征的介体)与1358个EMR衍生的表型之间的关联。该PheWAS复制了功率充足的先前GWAS关联的66%(51/77),并揭示了63个潜在的多效关联,P <4.6×10 -6 (错误发现率<0.1);这些新联想中最强的联想被复制到一个独立的队列中(n = 7,406)。这些发现验证了PheWAS作为一种工具,可以在基于EMR的队列中跨多种表型进行无偏倚的询问,并增强对人类疾病基因组基础的分析。

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