Late-onset Alzheimer's disease (LOAD) has a substantial genetic component estimated to be as high as 80%, much of which remains unexplained. Genome-wide association studies (GWAS) are emerging as a powerful approach in deciphering the genetic risk factors for common-complex diseases. It has been proposed that the genetic basis of common-complex diseases is mainly due to regulatory factors. There have been several GWAS in humans utilizing messenger RNA (mRNA) expression levels from lymphoblasts and one using cortical tissue from human brains. These studies identified many cis-SNPs that show strong association with transcript levels. Many of these associations achieved genome wide significance when only hundreds of samples are analyzed. We have recently analyzed 313,504 single-nucleotide polymorphisms (SNPs) using the Illumina platform in 2,099 subjects from LOAD case-control series. Of these subjects, there are 200 pathologically confirmed AD cases and 197 non-AD subjects with cerebellar RNA samples. In our preliminary analysis of mRNA expression levels for 12 LOAD candidate genes from cerebella of <200 LOAD subjects, we identified a cis-SNP in a strong candidate LOAD gene, insulin degrading enzyme (IDE) that associated with IDE transcript levels at genome-wide significance (p=2.7 x 10-8). This SNP also associated with AD risk and was in complete linkage disequilibrium with a putative functional SNP that resided in an evolutionarily conserved region of IDE. Our results suggest that the use of brain mRNA levels as endophenotypes in LOAD GWAS may be a powerful way to identify LOAD susceptibility alleles. Our 200 AD cases and 197 non-AD subjects with whole genome SNP genotypes and rich neuropathologic characterization provide a highly valuable resource to pursue GWAS of gene expression levels using mRNA from their cerebellar tissue, the brain region least affected by AD pathology. Assessment of cis-SNP associations with whole-transcriptome cerebellar expression levels in our nearly 400 samples will generate a valuable resource that can be utilized for mapping complex diseases. Our data will also enable simultaneous assessment of cis-variants for their effects on gene expression and AD risk. SNPs that associate with both AD risk and mRNA levels will be candidate susceptibility variants for AD with plausible regulatory effects on gene expression. Finally, such candidate variants can then be tested in functional expression studies to establish their biological effects and to validate the results from the expression GWAS.
Our specific aims are: 1. To obtain whole transcriptome expression levels from the cerebellar mRNA of 200 LOAD and 197 non-AD subjects with whole genome SNP genotypes;2. To perform GWAS of whole transcriptome expression levels to identify significant cis-SNP/transcript associations;3. To identify and validate cis-SNPs that associate with both LOAD and gene expression levels.
Alzheimer's disease (AD) is an epidemic that accounts for 60% of all dementias affecting an estimated 13.5 million individuals worldwide. Understanding its genetics will help understand its formation, may provide progress in its prevention and potential drug targets for its cure. Our proposal is aimed at the discovery of AD risk variants that work through regulation of gene expression using a genome-wide association study design.
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