Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method.

Yang Hu,Tianyi Zhao,Liang Cheng,Ying Zhang,Tianyi Zang

doi:10.3389/fgene.2018.00703

Abstract

Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered.

Highlights

Alzheimer’s disease (AD) is a neurodegenerative disease and has become the fourth major cause of death after cardiovascular disease, malignant tumor and stroke in the elderly (Senova et al, 2018)
EQTL refers to regions on chromosomes that regulate the expression of the mRNAs and proteins
Cis-expression quantitative trait locus (eQTL) are the eQTLs of a gene that are located in the genomic region of the gene, indicating changes in mRNA levels that may be caused by differences in the gene itself; transeQTLs are the eQTLs of a gene that are located in other genomic regions, indicating other genes

Summary

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative disease and has become the fourth major cause of death after cardiovascular disease, malignant tumor and stroke in the elderly (Senova et al, 2018). An important role of the large-scale eQTL research is to be able to prioritize the screening of possible pathogenic sites among SNP loci in the GWAS susceptible regions (Cheng et al, 2018c; Hu et al, 2018), and to speculate the possible biological mechanism through the impact of DNA polymorphism on biological traits. SMR is first proposed in the paper by Zhu et al (2016) as the “Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.”. They used this method to identify several genes which are related to five complex traits. Looking at these studies and the evidence, we concluded that the SMR is an effective tool

METHODS

RESULTS

CONCLUSION