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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.01021

Integrate GWAS, eQTL and mQTL data to identify Alzheimer’s Disease-related genes

 tianyi zhao1,  Yang Hu1, tianyi zang1* and yadong wang1*
  • 1Harbin Institute of Technology, China

It is estimated that the impact of related genes on the risk of Alzheimer’s Disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make Genome-wide association study (GWAS) become an important means to find disease-related muta-tion sites. Because of Linkage disequilibrium (LD), nei-ther the gene regulated by SNP nor the specific SNP can be determined. Because GWAS is affected by sample size and interaction, we introduced Empirical Bayes (EB) to make a meta-analysis of GWAS to greatly eliminate the bias caused by sample and the interaction of SNP. In ad-dition, most SNPs are in the non-coding region, so it is not clear how they relate to phenotype. In this paper, ex-pression quantitative trait locus (eQTL) studies and methylation quantitative trait locus (mQTL) studies are combined with GWAS to find the genes associated with Alzheimer disease in expression levels by pleiotropy. Summary data based Mendelian randomization (SMR) is introduced to integrate GWAS and eQTL/mQTL data. Finally, we prioritized 274 significant SNPs which belong to 20 genes by eQTL analysis and 379 significant SNPs which belong to 7 known genes by mQTL. Among them, 93 SNPs and 2 genes are overlapped. Finally, we did 10 case studies to prove the effectiveness of our method.

Keywords: Alzheimer’s disease, Mendelian randomization, GWAS, eQTL, mQTL

Received: 22 Apr 2019; Accepted: 24 Sep 2019.

Copyright: © 2019 zhao, Hu, zang and wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Mx. tianyi zang, Harbin Institute of Technology, Harbin, China,
Mx. yadong wang, Harbin Institute of Technology, Harbin, China,