Abstract
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 mutation sites. Because of linkage disequilibrium (LD), neither 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 addition, most SNPs are in the noncoding region, so it is not clear how they relate to phenotype. In this paper, expression 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 seven 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.
Highlights
It is estimated that the impact of related genes on the risk of Alzheimer’s disease (AD) is nearly 70%
Since Zhu et al proposed “Summary data-based Mendelian randomization (SMR)” in 2016, it has become a common way to identify the genes whose expression levels are associated with a complex trait because of pleiotropy
There are 149,326 SNPs occur in both genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) and 408,896 SNPs occur in both GWAS and methylation quantitative trait locus (mQTL)
Summary
It is estimated that the impact of related genes on the risk of AD is nearly 70%. Identifying candidate genes and loci can help us understand the pathogenesis of AD and develop drugs. Due to the increase in the number of samples, they found nine AD risk loci more than in previous studies. Jansen et al found that most of the AD-related DNA mutations were located in the noncoding part of the genome in regions that affected gene transcription. It means that combining GWAS data with transcriptional expression data will greatly advance AD research (Cheng et al, 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
