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基于多组学和孟德尔随机化分析探索阿尔茨海默病的生物标志物。

Exploring biomarkers of Alzheimer's disease based on multi-omics and Mendelian randomisation analysis.

机构信息

Department of Radiology, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Hubei, China.

出版信息

Ann Hum Biol. 2024 Feb;51(1):2415035. doi: 10.1080/03014460.2024.2415035. Epub 2024 Nov 7.

DOI:10.1080/03014460.2024.2415035
PMID:39508514
Abstract

BACKGROUND

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with no fully curative treatment.

AIM

This study aims to identify effective biomarkers for AD diagnosis and treatment by combining multi-omics and Mendelian randomisation (MR) analyses.

SUBJECTS AND METHODS

Positron emission tomography (PET), single nucleotide polymorphism (SNP), and gene expression data of AD patients using advanced correlation analysis methods (AdaSMCCA, rAdaSMCCA, and unAdaSMCCA algorithms) are integrated.

RESULTS

Several regions of interest, risk SNP sites, and risk genes associated with AD are identified. Expression quantitative trait loci (eQTL) for the top 100 risk genes are retrieved from public datasets. A two-sample MR analysis using genome-wide association study (GWAS) data reveals two genes (FAM117A and ACSL1) causally related to AD. Additionally, single-cell transcriptome (scRNA-seq) data from AD samples are analysed to identify high-scoring cell clusters and their interactions.

CONCLUSIONS

The identified multi-omics biomarkers and genes causally related to AD could inform clinical diagnosis and treatment.

摘要

背景

阿尔茨海默病(AD)是一种不可逆转的神经退行性疾病,目前尚无完全治愈的方法。

目的

本研究旨在通过整合多组学和孟德尔随机化(MR)分析,鉴定 AD 诊断和治疗的有效生物标志物。

受试者和方法

使用先进的相关分析方法(AdaSMCCA、rAdaSMCCA 和 unAdaSMCCA 算法)整合 AD 患者的正电子发射断层扫描(PET)、单核苷酸多态性(SNP)和基因表达数据。

结果

确定了与 AD 相关的几个感兴趣区域、风险 SNP 位点和风险基因。从公共数据集检索到前 100 个风险基因的表达数量性状基因座(eQTL)。使用全基因组关联研究(GWAS)数据的两样本 MR 分析揭示了两个与 AD 因果相关的基因(FAM117A 和 ACSL1)。此外,对 AD 样本的单细胞转录组(scRNA-seq)数据进行分析,以鉴定高分细胞簇及其相互作用。

结论

鉴定出的与 AD 相关的多组学生物标志物和基因可为临床诊断和治疗提供信息。

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