GWAS advancements to investigate disease associations and biological mechanisms

Oluwaferanmi Omidiran, Aashna Patel, Sarah Usman, Ishani Mhatre, Habiba Abdelhalim, William DeGroat, Rishabh Narayanan, Kritika Singh, Dinesh Mendhe, Zeeshan Ahmed
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Abstract

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 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 randomisation (MR), the Pangenome's high-quality Telomere-to-Telomere (T2T)-CHM13 panel and the Human BioMolecular Atlas Program (HuBMAP), as potential enablers of current and future GWAS research. The state of the literature demonstrates the capabilities of these techniques to enhance 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 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. MR has advanced causative inference, improving clinical diagnostics and facilitating definitive conclusions. Furthermore, spatial biology techniques such as HuBMAP enable 3D molecular mapping of tissues at single-cell resolution, offering insights into pathology of complex traits. This study aimed to elucidate and advocate for the increased application of these technologies, highlighting their potential to shape the future of GWAS research.

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研究疾病关联和生物机制的 GWAS 进展
全基因组关联研究(GWAS)有助于阐明各种性状和疾病的遗传结构。尽管全基因组关联研究取得了巨大成功,但其固有的局限性,如识别罕见和超罕见变异、可能存在虚假关联以及精确定位致病因子等,都会削弱诊断能力。本综述概述了 GWAS,并重点介绍了遗传学的最新进展,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)、Pangenome 的高质量端粒到端粒 (T2T)-CHM13 面板和人类生物分子图谱计划 (HuBMAP),这些都是当前和未来 GWAS 研究的潜在推动因素。文献研究表明,这些技术有能力提高 GWAS 的统计能力。WGS 以其全面的方法捕获了整个基因组,超越了传统 GWAS 技术侧重于预定义单核苷酸多态性位点的能力。Pangenome 的 T2T-CHM13 面板采用整体方法,有助于分析具有高序列同一性的区域,如节段重复。磁共振技术推进了病因推断,改善了临床诊断,有助于得出明确结论。此外,空间生物学技术(如 HuBMAP)能够以单细胞分辨率绘制组织的三维分子图谱,有助于深入了解复杂性状的病理学。本研究旨在阐明并倡导更多地应用这些技术,强调它们塑造未来 GWAS 研究的潜力。
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