Genetic variability in the regulation of gene expression in ten regions of the human brain

By A. Ramasamy, D. Trabzuni, S. Guelfi, Vibin Varghese, Colin Smith, R. Walker, Tisham De, L. Coin, Rohan de Silva, M. Cookson and 4 more. Nature Neuroscience, 2014

686 citations recorded by the source corpus.

Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found that cis-eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis-eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/. Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/10da29ff11b5fa023f5b9f611a3f4685e47c7483

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Citations in the corpus

Functional mapping and annotation of genetic associations with FUMA (2017, background): “…mapping of deleterious coding SNPs and eQTL mapping prioritized 84 unique genes of which 36 genes were implicated by deleterious coding SNPs and 65 were implicated by eQTLs in ﬂ uencing expression of these genes (six genes had both deleterious coding SNPs and eQTLs; Supplementary Data 18).”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, methodology): “Development and ageing data are from BrainCloud 17 , regional data are from Braineac 16 and cell type expression data are from fluorescent-activated cell sorted transcriptomes from mouse cortex 162”
Common genetic variants influence human subcortical brain structures (2015, neutral): “ADNI2GO, (4) BETULA, (5) BFS, (6) BIG, (7) BIG-Rep, (8) BrainSCALE, (9) BRCDECC, (10) CHARGE, (11) EPIGEN, (12) GIG, (13) GSP, (14) HUBIN, (15) IMAGEN, (16) IMpACT, (17) LBC1936, (18) Lieber, (19) MAS, (20) MCIC, (21) MooDS, (22) MPIP, (23) NCNG, (24) NESDA, (25) neuroIMAGE, (26) neuroIMAGE-Rep, (27”
variancePartition: interpreting drivers of variation in complex gene expression studies (2016, positive): “Recent studies have simultaneously considered multiple dimensions of variation to understand the impact of cell type [1], tissue type [2], brain region [3], experimental stimuli [4], time duration following stimulus [5] or ancestry [1, 4, 6] on the genetic regulation of gene expression.”
Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia (2016, methodology): “Table 1: Overlaps and differences between CMC and other publicly available eQTL resources * Best eQTL per probeset reported ** Best eQTL per gene reported FDR < 5% used to define eQTL in all cohorts. eQTL for Brain Cloud, HBCC, HBTRC, NIH and UKBEC were computed as described in the supplement. eQTL ”
Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences (2016, background): “The Genotype-Tissue Expression portal is a catalogue of human gene expression, eQTL, sQTL (splicing quantitative trait loci) and ASE data that can be used interactively to verify gene expression and gene expression regulation patterns in a variety of different tissues [59], while Braineac is a simil”