Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis

Yong Heng Wang; Pan Pan Luo; Ao Yi Geng; Xinwei Li; Tai-Hang Liu; Yi Jie He; Lin Huang; Ya Qin Tang

doi:10.3389/fnagi.2023.1183119

Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis

Front Aging Neurosci. 2023 Jun 21:15:1183119. doi: 10.3389/fnagi.2023.1183119. eCollection 2023.

Authors

Yong Heng Wang^#^{1

2}, Pan Pan Luo^#¹, Ao Yi Geng¹, Xinwei Li³, Tai-Hang Liu^{1

2}, Yi Jie He¹, Lin Huang¹, Ya Qin Tang¹

Affiliations

¹ Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China.
² Joint International Research Laboratory of Reproduction and Development, Chongqing Medical University, Chongqing, China.
³ School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China.

^# Contributed equally.

Abstract

Numerous genetic variants associated with Alzheimer's disease (AD) have been identified through genome-wide association studies (GWAS), but their interpretation is hindered by the strong linkage disequilibrium (LD) among the variants, making it difficult to identify the causal variants directly. To address this issue, the transcriptome-wide association study (TWAS) was employed to infer the association between gene expression and a trait at the genetic level using expression quantitative trait locus (eQTL) cohorts. In this study, we applied the TWAS theory and utilized the improved Joint-Tissue Imputation (JTI) approach and Mendelian Randomization (MR) framework (MR-JTI) to identify potential AD-associated genes. By integrating LD score, GTEx eQTL data, and GWAS summary statistic data from a large cohort using MR-JTI, a total of 415 AD-associated genes were identified. Then, 2873 differentially expressed genes from 11 AD-related datasets were used for the Fisher test of these AD-associated genes. We finally obtained 36 highly reliable AD-associated genes, including APOC1, CR1, ERBB2, and RIN3. Moreover, the GO and KEGG enrichment analysis revealed that these genes are primarily involved in antigen processing and presentation, amyloid-beta formation, tau protein binding, and response to oxidative stress. The identification of these potential AD-associated genes not only provides insights into the pathogenesis of AD but also offers biomarkers for early diagnosis of the disease.

Keywords: Alzheimer’s disease; GWAS; Mendelian Randomization; TWAS; eQTL.