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Gene expression in the human brain

$284,896ZIAFY2011AGNIH

National Institute On Aging

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Abstract

We have explored the associations between DNA variants in the genome, particularly single nucleotide polymorphisms (SNPs) and expression of nearby mRNA. At a genome wide level, there are many such associations found as expression quantitative trait loci (eQTLs). We have generated, and made publically available, one of the largest eQTL mapping sets in the human brain, with nearly 400 individuals with age range covering most of the human lifespan where genotype and expression data form microarrays is available. Primarily this is focused on two brain regions, the cerebellum and the frontal cerebral cortex, with ancillary datasets in other brain regions and on isolated cell types. Our aim is to provide a comprehensive view of the genomic control of gene expression in this complex organ that can be examined in a number of ways. In our own laboratory, we have begun to examine the relationship of age to epigenetic and gene expression markers. We have found that there are distinct and reproducible gene DNA methylation and expression signatures in the human brain and are following this up by analysis of additional datasets. If this can be confirmed, we will have found novel interesting candidates for neuronal responses to aging. This dataset has been used by several groups to identify the effects of SNPs nominated by genome wide association studies (GWAS) for their effect on lifetime risk of age-related neurodegenerative disorders such as Parkinsons disease or progressive supranuclear palsy. The data has also been used to examine genetic influences on expression of biomarkers related to other diseases. In the future, we will augment these datasets with RNA sequencing level analyses, which is allowing us to examine gene expression with greater precision and better dynamic range than microarrays. This methodology also allows examination of individual exons of a transcript even to the single base level. Current work is aimed at defining bioinformatic tools to query this data and exploit it in the same way that we did for array datasets.

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