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神经退行性疾病中全基因组关联研究与转录组全关联研究的整合:机遇、挑战及当前的方法创新

The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.

作者信息

Gu Si Chun, Welton Thomas, Sun QiaoYang, Wu Yun-Cheng, Tan Eng King, Zhou Zhi Dong

机构信息

Department of Neurology, National Neuroscience Institute, 11 Jalan Tan Tock Seng, Central Region, Singapore 308433, Singapore.

Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, 725 South Wanping Road, Xuhui District, Shanghai Municipality, Shanghai, 200032, China.

出版信息

Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf350.

DOI:10.1093/bib/bbaf350
PMID:40694035
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12282128/
Abstract

Neurodegenerative diseases (NDs) such as Alzheimer's and Parkinson's disease are characterized by complex genetic and regulatory landscapes. Genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) have become two essential and complementary methods for investigating the genetic basis of these disorders. GWAS systematically identifies genetic variants associated with disease risk, while TWAS provides functional insight by integrating expression quantitative trait loci to infer the effects of genetically regulated gene expression on complex traits. The aim of this review was to provide a comprehensive overview of methodological developments and integrative applications of GWAS and TWAS in the context of NDs research. We first conducted a bibliometric analysis that delineates evolving research trends and identifies emerging focal areas in the field. We then compared the underlying assumptions, strengths, and analytical frameworks of GWAS and TWAS. Subsequently, we highlighted recent advances in TWAS methodology, including fine-mapping strategies, multi-tissue and single-cell modeling, integration of multi-omic data layers, and applications of machine learning and artificial intelligence. Finally, current challenges related to ancestry representation, reference panel diversity, and translational generalizability were also presented. By synthesizing these perspectives, this review clarified the methodological landscape, guided future integrative analyses, and supported the broader application of transcriptome-informed genetic approaches in understanding and treating NDs.

摘要

神经退行性疾病(NDs),如阿尔茨海默病和帕金森病,具有复杂的遗传和调控格局。全基因组关联研究(GWAS)和全转录组关联研究(TWAS)已成为研究这些疾病遗传基础的两种重要且互补的方法。GWAS系统地识别与疾病风险相关的遗传变异,而TWAS通过整合表达数量性状位点来推断基因调控的基因表达对复杂性状的影响,从而提供功能方面的见解。本综述的目的是全面概述GWAS和TWAS在神经退行性疾病研究背景下的方法学发展和综合应用。我们首先进行了文献计量分析,描绘了不断演变的研究趋势,并确定了该领域新出现的重点领域。然后,我们比较了GWAS和TWAS的基本假设、优势和分析框架。随后,我们强调了TWAS方法学的最新进展,包括精细定位策略、多组织和单细胞建模、多组学数据层的整合以及机器学习和人工智能的应用。最后,还介绍了与祖先代表性、参考面板多样性和转化通用性相关的当前挑战。通过综合这些观点,本综述阐明了方法学格局,指导了未来的综合分析,并支持了转录组信息遗传方法在理解和治疗神经退行性疾病方面的更广泛应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dd3/12282128/4214733f52ca/bbaf350f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dd3/12282128/5deac2a06fb4/bbaf350f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dd3/12282128/4214733f52ca/bbaf350f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dd3/12282128/5deac2a06fb4/bbaf350f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0dd3/12282128/4214733f52ca/bbaf350f2.jpg

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本文引用的文献

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TWAS-GKF: a novel method for causal gene identification in transcriptome-wide association studies with knockoff inference.TWAS-GKF:一种用于转录组关联研究中基于置换检验的因果基因识别的新方法。
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