Preliminary Study Of Expression QTL Analysis For Candidate Genes Related To Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorders, and the fourth leading cause of death in adults. It is now understood that genetic factors play a crucial role in the risk of developing AD, however, the molecular mechanisms of AD is still under discussion. In this study, we collected the candidate genes related to AD based on bioinformatics analysis and performed an analysis of expression quantitative trait loci (eQTL) and the related gene network by using the tools of the GeneNetwork website (www.genenetwork.org). Affymetrix Mouse Expression 430 2.0 arrays (MOE430V2) were used to measure mRNA abundance and to identify gene expression differences across the hippocampi of 67 BXD recombinant inbred (BXD RI) strains. Combining the microarray data and eQTL analysis, we attempted to analyze the genetic regulatory relationship of the collected candidate genes. Currently, our focus is on cis-regulated gene. We found that 64 AD related genes had putative cis-eQTL with likelihood ratio statistic values (LRS) >15. Considering the important impact of sequence variants on the detection of cis-eQTL, we used SNPs from several important data sets to analyze the impact of SNPs on the significance of the expression variation and to distinguish genuine expression varation from variation due to overlapped SNPs. Take Rtn4 as an example, the candidate eQTL of this gene would be false positive owing to the overlapping SNP in Probe 812153. The allelic specific expression (ASE) analysis has been used to further validate genuine cis-eQTLs. The putative gene network has been constructed for those genes by using the Pearson correlation algorithm.This study provides the baseline data for the accuracy genetic networks associated with AD, that would be of great value for revealing the molecular mechanisms of AD. A number of studies have shown that amyloid precursor protein (App) plays a critical role in Alzheimer's disease, however, little is known about the genetic regulatory network. In this study, we combined microarray analysis and quantitative trait loci (QTL) mapping to characterize the genetic variation and genetic regulatory network for App using hippocampus of BXD recombinant inbred mice. The variation in expression level of App is conspicuous across the BXD RI strains. Moreover, the expression level of App is significantly higher in DBA/2J than the level in C57BL/6J (p<0.001). Quantitative RT-PCR (qRT-PCR) analysis has further confirmed the significant difference between the two parental strains C57BL/6J and DBA/2J. We performed an interval mapping for App gene expression and found that it is cis regulated with highly significant likelihood ratio statistic (LRS) score (LRS = 19.0; p<0.05). Four SNPs and two InDels (insertions or deletions) were identified in the promoter, and one of the SNPs is located in the pax2 motif. Cluster Map Tools were used for mapping the joint modulation of APP, the Spearman's rank correlation analysis was carried out to exclude the correlated transcripts due to linkage disequilibrium, six genes were statistically significantly correlated with APP (P < 0.05). Genetic regulatory network analysis showed that App co-regulated with many AD-related genes, including Gsk3b, Falz, Mef2a, Tlk2, Rtn, and Prkca. The genetical genomics approach demonstrates the importance and the potential power of the expression quantitative trait loci (eQTL) studies in identifying regulatory network that contribute to complex traits, such as AD.