Paper
- Alzforum Recommends
Ramasamy A, Trabzuni D, Guelfi S, Varghese V, Smith C, Walker R, De T, UK Brain Expression Consortium, North American Brain Expression Consortium, Coin L, de Silva R, Cookson MR, Singleton AB, Hardy J, Ryten M, Weale ME, UK Brain Expression Consortium, North American Brain Expression Consortium. 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.
Recommends
Please login to recommend the paper.
Comments
Columbia University Irving Medical Scool
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.
Washington University School of Medicine
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.
References:
Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bellenguez C, DeStafano AL, Bis JC, Beecham GW, Grenier-Boley B, Russo G, Thorton-Wells TA, Jones N, Smith AV, Chouraki V, Thomas C, Ikram MA, Zelenika D, Vardarajan BN, Kamatani Y, Lin CF, Gerrish A, Schmidt H, Kunkle B, Dunstan ML, Ruiz A, Bihoreau MT, Choi SH, Reitz C, Pasquier F, Cruchaga C, Craig D, Amin N, Berr C, Lopez OL, De Jager PL, Deramecourt V, Johnston JA, Evans D, Lovestone S, Letenneur L, Morón FJ, Rubinsztein DC, Eiriksdottir G, Sleegers K, Goate AM, Fiévet N, Huentelman MW, Gill M, Brown K, Kamboh MI, Keller L, Barberger-Gateau P, McGuiness B, Larson EB, Green R, Myers AJ, Dufouil C, Todd S, Wallon D, Love S, Rogaeva E, Gallacher J, St George-Hyslop P, Clarimon J, Lleo A, Bayer A, Tsuang DW, Yu L, Tsolaki M, Bossù P, Spalletta G, Proitsi P, Collinge J, Sorbi S, Sanchez-Garcia F, Fox NC, Hardy J, Deniz Naranjo MC, Bosco P, Clarke R, Brayne C, Galimberti D, Mancuso M, Matthews F, European Alzheimer's Disease Initiative (EADI), Genetic and Environmental Risk in Alzheimer's Disease, Alzheimer's Disease Genetic Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology, Moebus S, Mecocci P, Del Zompo M, Maier W, Hampel H, Pilotto A, Bullido M, Panza F, Caffarra P, Nacmias B, Gilbert JR, Mayhaus M, Lannefelt L, Hakonarson H, Pichler S, Carrasquillo MM, Ingelsson M, Beekly D, Alvarez V, Zou F, Valladares O, Younkin SG, Coto E, Hamilton-Nelson KL, Gu W, Razquin C, Pastor P, Mateo I, Owen MJ, Faber KM, Jonsson PV, Combarros O, O'Donovan MC, Cantwell LB, Soininen H, Blacker D, Mead S, Mosley TH Jr, Bennett DA, Harris TB, Fratiglioni L, Holmes C, de Bruijn RF, Passmore P, Montine TJ, Bettens K, Rotter JI, Brice A, Morgan K, Foroud TM, Kukull WA, Hannequin D, Powell JF, Nalls MA, Ritchie K, Lunetta KL, Kauwe JS, Boerwinkle E, Riemenschneider M, Boada M, Hiltuenen M, Martin ER, Schmidt R, Rujescu D, Wang LS, Dartigues JF, Mayeux R, Tzourio C, Hofman A, Nöthen MM, Graff C, Psaty BM, Jones L, Haines JL, Holmans PA, Lathrop M, Pericak-Vance MA, Launer LJ, Farrer LA, van Duijn CM, Van Broeckhoven C, Moskvina V, Seshadri S, Williams J, Schellenberg GD, Amouyel P, Wang J, Uitterlinden AG, Rivadeneira F, Koudstgaal PJ, Longstreth WT Jr, Becker JT, Kuller LH, Lumley T, Rice K, Garcia M, Aspelund T, Marksteiner JJ, Dal-Bianco P, Töglhofer AM, Freudenberger P, Ransmayr G, Benke T, Toeglhofer AM, Bressler J, Breteler MM, Fornage M, Hernández I, Rosende Roca M, Ana Mauleón M, Alegrat M, Ramírez-Lorca R, González-Perez A, Chapman J, Stretton A, Morgan A, Kehoe PG, Medway C, Lord J, Turton J, Hooper NM, Vardy E, Warren JD, Schott JM, Uphill J, Ryan N, Rossor M, Ben-Shlomo Y, Makrina D, Gkatzima O, Lupton M, Koutroumani M, Avramidou D, Germanou A, Jessen F, Riedel-Heller S, Dichgans M, Heun R, Kölsch H, Schürmann B, Herold C, Lacour A, Drichel D, Hoffman P, Kornhuber J, Gu W, Feulner T, van den Bussche H, Lawlor B, Lynch A, Mann D, Smith AD, Warden D, Wilcock G, Heuser I, Wiltgang J, Frölich L, Hüll M, Mayo K, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Singleton AB, Guerreiro R, Jöckel KH, Klopp N, Wichmann HE, Dickson DW, Graff-Radford NR, Ma L, Bisceglio G, Fisher E, Warner N, Pickering-Brown S. 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.
Make a Comment
To make a comment you must login or register.