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

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

Yong Heng Wang #  1   2 , Pan Pan Luo #  1 , Ao Yi Geng  1 , Xinwei Li  3 , Tai-Hang Liu  1   2 , Yi Jie He  1 , Lin Huang  1 , Ya Qin Tang  1
Affiliations

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

Yong Heng Wang et al. Front Aging Neurosci. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The work flow chart of the experiment.
FIGURE 2
FIGURE 2
Manhattan plots illustrating MR-JTI results in different brain regions and blood. The vertical axis is the corresponding “-log (P-value)” of each gene in the JTI result; the higher the corresponding value of the gene point, the higher the association between the gene and AD. Brain amygdala (A); brain cortex (B), brain nucleus accumbens basal ganglia (C), and blood (D).
FIGURE 3
FIGURE 3
Enrichment analysis of AD-associated genes. (A) The GO enrichment analysis of 415 AD-associated genes, including molecular biological process (BP), cellular components (CC), and function (MF). (B) The KEGG enrichment analysis of 415 AD-associated genes.
FIGURE 4
FIGURE 4
Overlapping differentially expressed genes (DEGs) with causal genes in brain regions (A) and blood (B).
FIGURE 5
FIGURE 5
Correspondence between highly reliable genes and enriched pathways. From the outer circle to inner circle, the first circle represents an index containing 20 genes and 95 pathways (enriched by the 415 potential AD-associated genes with adjusted P < 0.05); the second circle denotes the gene or pathway type; the third circle is whether the gene/pathway has been reported to be related to AD; the fourth circle indicates whether the gene/pathway is immune-related. Gray lines indicate the correspondence between genes and pathways.
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