Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 93 risk genes for Alzheimer's disease dementia.

BACKGROUND

Transcriptome-wide association study (TWAS) is an influential tool for identifying novel genes associated with complex diseases, where their genetic effects may be mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate genetic effect sizes on expression quantitative traits of target genes (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are then employed as variant weights in burden gene-based association test statistics, facilitating the mapping of risk genes for complex diseases with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia have primarily focused on -eQTL, disregarding potential -eQTL. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method which incorporated both - and -eQTL of brain and blood tissues to enhance mapping risk genes for AD dementia.

METHODS

We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate - and -eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Subsequently, estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per tissue type. Finally, we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.

RESULTS

We identified 37 genes in prefrontal cortex, 55 in cortex, and 51 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 93 significant risk genes including 29 genes primarily due to -eQTL and 50 novel genes. Utilizing protein-protein interaction network and phenotype enrichment analyses with these 93 significant risk genes, we detected 5 functional clusters comprised of both known and novel AD risk genes and 7 enriched phenotypes.

CONCLUSION

We applied BGW-TWAS and aggregated Cauchy test methods to integrate both - and -eQTL data of brain and blood tissues with GWAS summary data to identify risk genes of AD dementia. The risk genes we identified provide novel insights into the underlying biological pathways implicated in AD dementia.