Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, 7491 Trondheim, Sør-Trøndelag, Norway.
USC Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA.
Am J Hum Genet. 2018 Jun 7;102(6):1048-1061. doi: 10.1016/j.ajhg.2018.04.001. Epub 2018 May 17.
Health systems are stewards of patient electronic health record (EHR) data with extraordinarily rich depth and breadth, reflecting thousands of diagnoses and exposures. Measures of genomic variation integrated with EHRs offer a potential strategy to accurately stratify patients for risk profiling and discover new relationships between diagnoses and genomes. The objective of this study was to evaluate whether polygenic risk scores (PRS) for common cancers are associated with multiple phenotypes in a phenome-wide association study (PheWAS) conducted in 28,260 unrelated, genotyped patients of recent European ancestry who consented to participate in the Michigan Genomics Initiative, a longitudinal biorepository effort within Michigan Medicine. PRS for 12 cancer traits were calculated using summary statistics from the NHGRI-EBI catalog. A total of 1,711 synthetic case-control studies was used for PheWAS analyses. There were 13,490 (47.7%) patients with at least one cancer diagnosis in this study sample. PRS exhibited strong association for several cancer traits they were designed for, including female breast cancer, prostate cancer, melanoma, basal cell carcinoma, squamous cell carcinoma, and thyroid cancer. Phenome-wide significant associations were observed between PRS and many non-cancer diagnoses. To differentiate PRS associations driven by the primary trait from associations arising through shared genetic risk profiles, the idea of "exclusion PRS PheWAS" was introduced. Further analysis of temporal order of the diagnoses improved our understanding of these secondary associations. This comprehensive PheWAS used PRS instead of a single variant.
健康系统是患者电子健康记录 (EHR) 数据的管理者，这些数据具有非常丰富的深度和广度，反映了数千种诊断和暴露情况。将基因组变异与 EHR 相结合的措施提供了一种潜在的策略，可以准确地对患者进行风险分层，并发现诊断和基因组之间的新关系。本研究的目的是评估常见癌症的多基因风险评分 (PRS) 是否与 28260 名无亲缘关系、经基因分型的、最近有欧洲血统的、同意参与密歇根医学纵向生物库努力的密歇根基因组倡议的患者的全表型关联研究 (PheWAS) 中的多个表型相关联。使用 NHGRI-EBI 目录的汇总统计数据计算了 12 种癌症特征的 PRS。总共使用了 1711 项合成病例对照研究进行 PheWAS 分析。在本研究样本中，有 13490 名（47.7%）患者至少有一种癌症诊断。PRS 与他们设计的几种癌症特征表现出强烈的关联，包括女性乳腺癌、前列腺癌、黑色素瘤、基底细胞癌、鳞状细胞癌和甲状腺癌。在 PRS 和许多非癌症诊断之间观察到全表型显著关联。为了区分由主要特征驱动的 PRS 关联和由于共同遗传风险谱引起的关联，引入了“排除 PRS PheWAS”的概念。对诊断的时间顺序的进一步分析提高了我们对这些次要关联的理解。这项全面的 PheWAS 使用 PRS 而不是单一变体。
