Prediction of Alzheimer's disease-associated genes by integration of GWAS summary data and expression data

TL;DR

This study integrates GWAS and expression data using TWAS to identify 44 potential Alzheimer's disease causal genes, including 25 newly discovered, revealing links to autoimmune disorders and advancing understanding of disease genetics.

Contribution

The paper introduces a TWAS-based approach combining GWAS and expression data to discover novel Alzheimer's disease-associated genes, enhancing genetic understanding of the disease.

Findings

Identified 15 disease causal genes, 4 of which are new.

Discovered 29 additional potential causal genes, 21 new.

Many identified genes are linked to autoimmune disorders.

Abstract

Alzheimer's disease is the most common cause of dementia. It is the fifth-leading cause of death among elderly people. With high genetic heritability (79%), finding disease causal genes is a crucial step in find treatment for AD. Following the International Genomics of Alzheimer's Project (IGAP), many disease-associated genes have been identified; however, we don't have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression level data by using TWAS method and identified 15 disease causal genes under strict multiple testing (alpha<0.05), 4 genes are newly identified; identified additional 29 potential disease causal genes under false discovery rate(alpha < 0.05), 21 of them are newly identified. Many genes we identified are also associated with some autoimmune disorder.