Omidiran Oluwaferanmi, Patel Aashna, Usman Sarah, Mhatre Ishani, Abdelhalim Habiba, DeGroat William, Narayanan Rishabh, Singh Kritika, Mendhe Dinesh, Ahmed Zeeshan
Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA.
Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
Clin Transl Discov. 2024 Jul;4(3). doi: 10.1002/ctd2.296. Epub 2024 May 1.
Genome-wide association studies (GWAS) have been instrumental in elucidating the genetic architecture of various traits and diseases. Despite the success of GWAS, inherent limitations such as identifying rare and ultra-rare variants, the potential for spurious associations, and in pinpointing causative agents can undermine diagnostic capabilities. This review provides an overview of GWAS and highlights recent advances in genetics that employ a range of methodologies, including Whole Genome Sequencing (WGS), Mendelian Randomization (MR), the Pangenome's high-quality T2T-CHM13 panel, and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. State of the literature demonstrate the capabilities of these techniques in enhancing the statistical power of GWAS. WGS, with its comprehensive approach, captures the entire genome, surpassing the capabilities of the traditional GWAS technique focused on predefined Single Nucleotide Polymorphism (SNP) sites. The Pangenome's T2T-CHM13 panel, with its holistic approach, aids in the analysis of regions with high sequence identity, such as segmental duplications (SDs). Mendelian Randomization has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques like HuBMAP, enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aims to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.
全基因组关联研究(GWAS)在阐明各种性状和疾病的遗传结构方面发挥了重要作用。尽管GWAS取得了成功,但其固有局限性,如识别罕见和超罕见变异、虚假关联的可能性以及确定致病因素等,可能会削弱诊断能力。本综述概述了GWAS,并强调了遗传学领域的最新进展,这些进展采用了一系列方法,包括全基因组测序(WGS)、孟德尔随机化(MR)、泛基因组的高质量T2T-CHM13基因组、人类生物分子图谱计划(HuBMAP),它们是当前和未来GWAS研究的潜在推动因素。文献现状展示了这些技术在增强GWAS统计效力方面的能力。WGS以其全面的方法捕获了整个基因组,超越了专注于预定义单核苷酸多态性(SNP)位点的传统GWAS技术的能力。泛基因组的T2T-CHM13基因组以其整体方法,有助于分析具有高序列同一性的区域,如片段重复(SDs)。孟德尔随机化推进了因果推断,改善了临床诊断并有助于得出明确结论。此外,像HuBMAP这样的空间生物学技术能够以单细胞分辨率对组织进行三维分子图谱绘制,为复杂性状的病理学提供见解。本研究旨在阐明并倡导增加这些技术的应用,强调它们对塑造GWAS研究未来的潜力。