Genome-wide TWAS of brain and blood tissues identifies novel risk genes for Alzheimer’s disease dementia Open Access
Guo, Shuyi (Spring 2023)
Abstract
Background: Transcriptome-wide association studies (TWAS) are a powerful tool for identifying novel genes associated with complex diseases, including Alzheimer's disease (AD) dementia. TWAS integrate reference genetic and transcriptomic data to identify expression quantitative trait loci (eQTLs) of target genes and estimate the genetically regulated gene expression (GReX) levels for each gene. GReX data are then integrated with the genome-wide association study (GWAS) summary statistics to assess the association between gene expression and the phenotype of interest. However, existing TWAS methods only consider cis-eQTL (eQTLs located within the 1 MB region of the gene) effects and miss effects of trans-eQTLs (outside of the 1 MB region). To overcome this limitation, we applied a Bayesian Genome-wide TWAS (BGW-TWAS) method to leverage both cis- and trans-eQTL information to improve the mapping of risk genes for AD dementia.
Methods and Materials: We applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) dataset on three tissues – the prefrontal cortex, cortex, and whole blood. Then we integrated estimated eQTL effect sizes by BGW-TWAS with a summary-level GWAS dataset of AD dementia by S-PrediXcan to identify genes associated with AD dementia. We also use aggregated Cauchy association test-omnibus (ACAT-O) method to combine the TWAS p-values across the three tissues for each gene to obtain the combined p-values.
Results: Our analysis identified 37 genes significantly associated with AD dementia in the prefrontal cortex, 55 in the cortex, and 51 in the whole blood. After combining TWAS p-values across the three tissues by ACAT-O, we obtained 93 genes with significant combined p-values, including 50 novel genes not reported in previous studies, and 29 genes significant primarily due to trans-eQTLs. We detected 5 functional clusters comprised of both known AD risk genes and novel genes in the protein-protein association networks, and 7 enriched phenotypes in the phenotype enrichment analysis.
Conclusion: In this study, we conducted BGW-TWAS on three tissues and identified known and novel genes associated with AD dementia. Our study is the first genome-wide TWAS utilizing both cis- and trans-eQTLs for AD risk gene identification and provides new insights into the genetic basis of AD dementia.
Table of Contents
1. Introduction ....................... 1
2. Material and Methods ....................... 3
2.1 Bayesian Genome-wide TWAS (BGW-TWAS) ....................... 3
2.1.1 Bayesian Variable Selection Regression Model (BVSR) ....................... 3
2.1.2 EM-MCMC Algorithm ....................... 5
2.1.3 TWAS with summary-level GWAS data ....................... 6
2.2 Aggregated Cauchy association test-omnibus (ACAT-O) ....................... 7
2.3 Genotype-Tissue Expression (GTEx) dataset and GWAS summary statistics ....................... 8
2.4 Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) ....................... 8
3. Results ....................... 9
3.1 Overview of the workflow ....................... 9
3.2 BGW-TWAS results of AD dementia in three tissues ....................... 10
3.3 ACAT-O results of AD dementia across three tissues ....................... 11
3.4 Known risk genes for AD dementia ....................... 14
3.5 Novel findings of risk genes for AD dementia ....................... 15
3.6 Protein-protein association networks and phenotype enrichment analysis by STRING ....................... 16
3.6.1 Protein-protein association networks ....................... 17
3.6.2 Phenotype enrichment analysis ....................... 19
3.7 eQTLs of the significant genes ....................... 20
4. Discussion ....................... 22
References ....................... 25
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