. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat Neurosci. 2014 Oct;17(10):1418-28. Epub 2014 Aug 31 PubMed.


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  1. This is an interesting study that significantly extends the repertoire of genetic variations that influence gene expression. As has been shown in several recent studies, many genetic variations associated with disease susceptibility influence gene expression in cell- and context-specific manners. This study significantly extends the range of brain regions that have been profiled and examined to identify expression-quantitative trait loci (eQTLs); it will serve as an excellent resource for future investigations, particularly those involving neuropsychiatric disease that have regionally specific effects.

    In addition, having profiled 10 brain regions in a substantial number of subjects, the authors enable robust comparisons across the regions to address how gene expression correlates with functional specificity, and they uncover interesting observations such as the co-variation of certain cis-eQTL signals in specific brain regions. These patterns suggest higher-order structure in gene expression that will refine our understanding of the brain transcriptome. Extending these types of studies to understand which cell types are driving the association signal is an important goal for future studies.

  2. Genome-wide associations studies (GWAS) have been extremely successful in identifying novel loci for complex traits, including Alzheimer’s disease (AD). The latest and largest GWAS for Alzheimer disease identified 19 genomic regions associated with risk (Lambert et al., 2013). Despite this success, we are still far from understanding the biological role of these associations. In some cases, it is not clear what gene or genetic variant is responsible for the association, and in other cases we do not know what is the functional mechanism driving the association.

    Because some of those signals are not in the coding region but close to the gene, it has been speculated that those SNPs may affect overall gene-expression or splicing. Several studies have previously analyzed the association of gene expression with different genetic variants (Allen et al., 2012; Karch et al.,  2012), and found that some of the GWAS signals also are associated with gene expression (eQTL).

    In this study Ramasamy et al. moved a step forward and generated gene-expression data at the exon level in 10 different brain regions from 134 brains samples without known neurological disorders. They also generated GWAS data for all the individuals. They found a large number of eQTLs. Some of them were tissue- or region-specific, while others were not. Additional analyses identified pathways that may help us understand some of the biology of Alzheimer’s disease. Some of the eQTLs also overlap with genomic regions associated with complex traits such as Parkinson’s disease, ALS, lung cancer, or smoking and nicotine addiction

    These are some examples of the results derived from this study. Much more results are presented in the paper with broad repercussion in complex traits. This project has generated so much data that additional results will be found with further analyses. More importantly all of this data is currently available on dbGAP or the GEO dataset, so any investigator can have access to the processed data or the raw data.


    . Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet. 2013 Dec;45(12):1452-8. Epub 2013 Oct 27 PubMed.

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