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

Ramasamy, Adaikalavan; Trabzuni, Daniah; Guelfi, Sebastian; Varghese, Vibin; Smith, Colin; Walker, Robert; De, Tisham; Coin, Lachlan; Silva, Rohan de; Cookson, Mark; Singleton, Andrew B.; Hardy, John; Ryten, Mina; Weale, Michael E.

doi:10.1038/nn.3801

Cited by 640 publications

(710 citation statements)

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“…First, we obtained additional annotation information. We obtained histone marks and DNase I hypersensitive sites data from the Roadmap Epigenomics Consortium14; we obtained eQTLs derived from brain regions from the UK Brain Expression Consortium10 and the GTEx Consortium9; and we obtained promoter capture HiC array express data in CD34 (a marker of immature hematopoietic cells) cells from GM12878 (reference: E‐MTAB‐2323) 35. We also considered two gene sets.…”

Section: Methodsmentioning

confidence: 99%

“…We grouped eQTLs across all brain regions, but treated the eQTLs from the UK Brain Expression Consortium10 and the GTEx Consortium9 separately (resulting in two annotations). Both the GTEx and UKBEC analyses included brain regions highly relevant to MS, AD, and PD, namely white matter, hippocampus, temporal cortex, and substantia nigra.…”

Section: Methodsmentioning

confidence: 99%

“…Given the many ways in which complex diseases arise, and for human brain diseases, the well‐recognized cellular heterogeneity of the brain, pinpointing cell types of interest, is important to further understand pathogenicity. Efforts to obtain brain samples (the most obviously relevant tissue for neurodegenerative diseases) for eQTL analyses are ongoing 8, 9, 10, 11, 12, 13. There has been a recent proliferation in the availability of cell‐type and tissue‐specific annotations, including brain tissue, for example, through the Roadmap Epigenomics Project14 and the PsychEncode Project 11.…”

Section: Introductionmentioning

confidence: 99%

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Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases

Gagliano

Pouget

Hardy

et al. 2016

Ann Clin Transl Neurol

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ObjectivesWe assessed the current genetic evidence for the involvement of various cell types and tissue types in the etiology of neurodegenerative diseases, especially in relation to the neuroinflammatory hypothesis of neurodegenerative diseases.MethodsWe obtained large‐scale genome‐wide association study (GWAS) summary statistics from Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). We used multiple sclerosis (MS), an autoimmune disease of the central nervous system, as a positive control. We applied stratified LD score regression to determine if functional marks for cell type and tissue activity, and gene‐set lists were enriched for genetic heritability. We compared our results to those from two gene‐set enrichment methods (Ingenuity Pathway Analysis and enrichr).ResultsThere were no significant heritability enrichments for annotations marking genes active within brain regions, but there were significant heritability enrichments for annotations marking genes active within cell types that form part of both the innate and adaptive immune systems. We found this for MS (as expected) and also for AD and PD. The strongest signals were from the adaptive immune system (e.g., T cells) for PD, and from both the adaptive (e.g., T cells) and innate (e.g., CD14: a marker for monocytes, and CD15: a marker for neutrophils) immune systems for AD. Annotations from the liver were also significant for AD. Pathway analysis provided complementary results.InterpretationFor AD and PD, we found significant enrichment of heritability in annotations marking gene activity in immune cells.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases

Gagliano

Pouget

Hardy

et al. 2016

Ann Clin Transl Neurol

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“…1). As cis ‐effects on gene expression can be specific to developmental stage [Hill and Bray, 2012; Tao et al, 2014] and brain region [Buonocore et al, 2010; Gibbs et al, 2010; Ramasamy et al, 2014], we examined effects in the human fetal brain as well as in three adult brain regions implicated in the pathophysiology of schizophrenia: dorsolateral prefrontal cortex (DLPFC), hippocampus, and caudate.…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide significant schizophrenia risk variation on chromosome 10q24 is associated with altered cis‐regulation of BORCS7, AS3MT, and NT5C2 in the human brain

Duarte

Troakes

Nolan

et al. 2016

American J of Med Genetics Pt B

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Chromosome 10q24.32‐q24.33 is one of the most robustly supported risk loci to emerge from genome‐wide association studies (GWAS) of schizophrenia. However, extensive linkage disequilibrium makes it difficult to distinguish the actual susceptibility gene(s) at the locus, limiting its value for improving biological understanding of the condition. In the absence of coding changes that can account for the association, risk is likely conferred by altered regulation of one or more genes in the region. We, therefore, used highly sensitive measures of allele‐specific expression to assess cis‐regulatory effects associated with the two best‐supported schizophrenia risk variants (SNP rs11191419 and indel ch10_104957618_I/rs202213518) on the primary positional candidates BORCS7, AS3MT, CNNM2, and NT5C2 in the human brain. Heterozygosity at rs11191419 was associated with increased allelic expression of BORCS7 and AS3MT in the fetal and adult brain, and with reduced allelic expression of NT5C2 in the adult brain. Heterozygosity at ch10_104957618_I was associated with reduced allelic expression of NT5C2 in both the fetal and adult brain. Comparisons between cDNA ratios in heterozygotes and homozygotes for the risk alleles indicated that cis‐effects on NT5C2 expression in the adult dorsolateral prefrontal cortex could be largely accounted for by genotype at these two risk variants. While not excluding effects on other genes in the region, this study implicates altered neural expression of BORCS7, AS3MT, and NT5C2 in susceptibility to schizophrenia arising from genetic variation at the chromosome 10q24 locus. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

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“…To identify eQTL effects, eQTL databases were analyzed (Borel et al., 2011; Dimas et al., 2009; Dixon et al., 2007; Fehrmann et al., 2011; Greenawalt et al., 2011; Grundberg et al., 2009; GTEx Consortium, 2015; Kim, Cho, Lee, & Webster, 2012; Kirsten et al., 2015; Mehta et al., 2013; Myers et al., 2007; Ramasamy et al., 2014; Schadt et al., 2008; Schröder et al., 2011; Veyrieras et al., 2008; Westra et al., 2013; Xia et al., 2012; Zeller et al., 2010). We only considered SNPs identified in brain or blood tissue and eQTLs had to be replicated in at least one study.…”

Section: Methodsmentioning

confidence: 99%

ATP2C2andDYX1C1are putative modulators of dyslexia‐related MMR

et al. 2017

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BackgroundDyslexia is a specific learning disorder affecting reading and spelling abilities. Its prevalence is ~5% in German‐speaking individuals. Although the etiology of dyslexia largely remains to be determined, comprehensive evidence supports deficient phonological processing as a major contributing factor. An important prerequisite for phonological processing is auditory discrimination and, thus, essential for acquiring reading and spelling skills. The event‐related potential Mismatch Response (MMR) is an indicator for auditory discrimination capabilities with dyslexics showing an altered late component of MMR in response to auditory input.MethodsIn this study, we comprehensively analyzed associations of dyslexia‐specific late MMRs with genetic variants previously reported to be associated with dyslexia‐related phenotypes in multiple studies comprising 25 independent single‐nucleotide polymorphisms (SNPs) within 10 genes.ResultsFirst, we demonstrated validity of these SNPs for dyslexia in our sample by showing that additional inclusion of a polygenic risk score improved prediction of impaired writing compared with a model that used MMR alone. Secondly, a multifactorial regression analysis was conducted to uncover the subset of the 25 SNPs that is associated with the dyslexia‐specific late component of MMR. In total, four independent SNPs within DYX1C1 and ATP2C2 were found to be associated with MMR stronger than expected from multiple testing. To explore potential pathomechanisms, we annotated these variants with functional data including tissue‐specific expression analysis and eQTLs.ConclusionOur findings corroborate the late component of MMR as a potential endophenotype for dyslexia and support tripartite relationships between dyslexia‐related SNPs, the late component of MMR and dyslexia.

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

Cited by 640 publications

References 60 publications

Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases

Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases

Genome‐wide significant schizophrenia risk variation on chromosome 10q24 is associated with altered cis‐regulation of BORCS7, AS3MT, and NT5C2 in the human brain

ATP2C2andDYX1C1are putative modulators of dyslexia‐related MMR

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