Shao Mengting, Chen Kaiyang, Zhang Shuting, Tian Min, Shen Yan, Cao Chen, Gu Ning
Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China.
Nanjing Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Institute of Clinical Medicine, Nanjing Drum Tower Hospital, Medical School, Nanjing University, Nanjing 210093, China.
Genomics Proteomics Bioinformatics. 2024 Dec 3;22(5). doi: 10.1093/gpbjnl/qzae077.
The rapid development of multiome (transcriptome, proteome, cistrome, imaging, and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases. Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective. This review provides a detailed categorization and summary of the statistical models, use cases, and advantages of recent multiome-wide association studies. In addition, to illustrate gene-disease association studies based on transcriptome-wide association study (TWAS), we collected 478 disease entries across 22 categories from 235 manually reviewed publications. Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS, indicating its potential to deepen our understanding of the genetic architecture of complex diseases. In summary, this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.
多组学（转录组、蛋白质组、顺式作用组、成像组和调控组）全基因组关联研究方法的迅速发展为生物学家理解复杂疾病背后的易感基因开辟了新途径。对这些方法进行全面比较对于为特定研究目标选择最合适的工具至关重要。本综述详细分类并总结了近期多组学全基因组关联研究的统计模型、应用案例及优势。此外，为了阐述基于转录组全基因组关联研究（TWAS）的基因与疾病关联研究，我们从235篇经过人工审核的出版物中收集了22个类别的478种疾病条目。我们的分析表明，精神障碍是TWAS研究最频繁的疾病，这表明其有潜力加深我们对复杂疾病遗传结构的理解。总之，本综述强调了多组学全基因组关联研究在阐明复杂疾病方面 的重要性，并突出了为每项研究选择合适方法的意义。
