Despite the enrichment of our samples with tissue from AD subjects and our use of both cerebellar and temporal cortex tissue, we did not identify strong transcript associations for some of the top genes recently implicated in AD risk in large LOAD GWAS studies [28], [39], [40], [47], [48]. This could be because the AD risk variants in these genes exert their effects via mechanisms other than influencing transcript levels, namely changes in protein conformation. If so, even the negative results from an eGWAS could be informative in guiding the future deep-sequencing efforts which should focus on coding rather than non-coding, functional regions. Alternative explanations include technical shortcomings, such as inability to measure all transcript species, measurements of global rather than cell-specific gene expression, not including all tested disease-associated variants in our genotyping platform. We also need to consider that the top genes nearest the strongest variants from the LOAD GWAS may not be actual disease genes. These loci require further investigations to account for this possibility. Additionally, our criteria for selection of the top cisSNPs, requiring significance in both
