最多等待 10 秒，若超时请稍后重试。
Tingyang Hu, Randy L Parrish, Qile Dai, Aron S Buchman, Shinya Tasaki, David A Bennett, Nicholas T Seyfried, Michael P Epstein, Jingjing Yang
Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA.
Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA; Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA 30322, USA.
Am J Hum Genet. 2024 Sep 5;111(9):1848-1863. doi: 10.1016/j.ajhg.2024.07.001. Epub 2024 Jul 29.
Transcriptome-wide association study (TWAS) tools have been applied to conduct proteome-wide association studies (PWASs) by integrating proteomics data with genome-wide association study (GWAS) summary data. The genetic effects of PWAS-identified significant genes are potentially mediated through genetically regulated protein abundance, thus informing the underlying disease mechanisms better than GWAS loci. However, existing TWAS/PWAS tools are limited by considering only one statistical model. We propose an omnibus PWAS pipeline to account for multiple statistical models and demonstrate improved performance by simulation and application studies of Alzheimer disease (AD) dementia. We employ the Aggregated Cauchy Association Test to derive omnibus PWAS (PWAS-O) p values from PWAS p values obtained by three existing tools assuming complementary statistical models-TIGAR, PrediXcan, and FUSION. Our simulation studies demonstrated improved power, with well-calibrated type I error, for PWAS-O over all three individual tools. We applied PWAS-O to studying AD dementia with reference proteomic data profiled from dorsolateral prefrontal cortex of postmortem brains from individuals of European ancestry. We identified 43 risk genes, including 5 not identified by previous studies, which are interconnected through a protein-protein interaction network that includes the well-known AD risk genes TOMM40, APOC1, and APOC2. We also validated causal genetic effects mediated through the proteome for 27 (63%) PWAS-O risk genes, providing insights into the underlying biological mechanisms of AD dementia and highlighting promising targets for therapeutic development. PWAS-O can be easily applied to studying other complex diseases.
转录组关联研究（TWAS）工具已被应用于通过整合蛋白质组学数据与全基因组关联研究（GWAS）汇总数据来进行全蛋白质组关联研究（PWAS）。PWAS 鉴定的显著基因的遗传效应可能通过遗传调控的蛋白质丰度来介导，因此比 GWAS 基因座更好地反映潜在的疾病机制。然而，现有的 TWAS/PWAS 工具受到仅考虑一个统计模型的限制。我们提出了一个综合 PWAS 管道来考虑多个统计模型，并通过阿尔茨海默病（AD）痴呆的模拟和应用研究证明了改进的性能。我们采用聚集柯西关联检验（Aggregated Cauchy Association Test）从三个现有的假设互补统计模型的工具（TIGAR、PrediXcan 和 FUSION）获得的 PWAS 中推导出综合 PWAS（PWAS-O）的 p 值。我们的模拟研究表明，PWAS-O 相对于所有三个单独的工具，具有更高的功效和良好校准的 I 型错误率。我们将 PWAS-O 应用于研究 AD 痴呆症，参考了来自欧洲血统个体死后大脑背外侧前额叶皮层的蛋白质组学数据。我们鉴定了 43 个风险基因，包括 5 个以前研究未发现的基因，这些基因通过包括著名的 AD 风险基因 TOMM40、APOC1 和 APOC2 在内的蛋白质-蛋白质相互作用网络相互连接。我们还验证了 27 个（63%）PWAS-O 风险基因通过蛋白质组介导的因果遗传效应，为 AD 痴呆症的潜在生物学机制提供了深入了解，并突出了治疗开发的有前途的靶点。PWAS-O 可以很容易地应用于研究其他复杂疾病。
