35 results on '"Skene, N."'
Search Results
2. EpiCompare: R package for the comparison and quality control of epigenomic peak files
- Author
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Choi, S, Schilder, B, Abbasova, L, Murphy, A, and Skene, N
- Abstract
Summary EpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner. Availability and Implementation EpiCompare is available on Bioconductor (≥ v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html All source code is publically available via GitHub: https://github.com/neurogenomics/EpiCompare Documentation website https://neurogenomics.github.io/EpiCompare EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare Competing Interest Statement The authors have declared no competing interest.
- Published
- 2022
3. CUT&Tag recovers up to half of ENCODE ChIP-seq peaks
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Hu, D, Abbasova, L, Schilder, B, Nott, A, Skene, N, and Marzi, S
- Abstract
Techniques for genome-wide epigenetic profiling have been undergoing rapid development toward recovery of high quality data from bulk and single cell samples. DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), which has become the gold standard for studying histone modifications or transcription factor binding. Cleavage Under Targets & Tagmentation (CUT&Tag) is a promising new technique, which enables profiling of such interactions in situ at high sensitivity and is adaptable to single cell applications. However thorough evaluation and benchmarking against established ChIP-seq datasets are still lacking. Here we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Across a total of 30 new and 6 published CUT&Tag datasets we found that no experiment recovers more than 50% of known ENCODE peaks, regardless of the histone mark. We tested peak callers MACS2 and SEACR, identifying optimal peak calling parameters. Balancing both precision and recall of known ENCODE peaks, SEACR without retention of duplicates showed the best performance. We found that reducing PCR cycles during library preparation lowered duplication rates at the expense of ENCODE peak recovery. Despite the moderate ENCODE peak recovery, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.
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- 2022
4. Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders
- Author
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Byrne, E., Zhu, Z., Qi, T., Skene, N., Bryois, J., Pardinas, A., Stahl, E., Smoller, J., Rietschel, N., Bipolar Working Group, of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the, Psychiatric Genomics Consortium, Owen, M., Walters, J., O’Donovan, M., McGrath, J., Hjerling-Leffler, J., Sullivan, P., Goddard, M., Visscher, P., Yang, J., Wray, N., Gordon-Smith, Katherine, Jones, Lisa, and Perry, Amy
- Subjects
0301 basic medicine ,medicine.medical_specialty ,Bipolar Disorder ,LOCI ,BF ,Single-nucleotide polymorphism ,Genome-wide association study ,Polymorphism, Single Nucleotide ,Article ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,0302 clinical medicine ,Pleiotropy ,medicine ,SNP ,Humans ,Genetic Predisposition to Disease ,Bipolar disorder ,GENOME-WIDE ASSOCIATION ,Psychiatry ,Molecular Biology ,Depression (differential diagnoses) ,RISK ,business.industry ,BIPOLAR DISORDER ,RETROMER COMPLEX ,medicine.disease ,STATISTICS ,Psychiatry and Mental health ,030104 developmental biology ,Schizophrenia ,Attention Deficit Disorder with Hyperactivity ,RC0321 ,Autism ,business ,030217 neurology & neurosurgery ,Genome-Wide Association Study ,NEUROTROPHIC FACTOR - Abstract
Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium-schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.
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- 2021
- Full Text
- View/download PDF
5. Publisher Correction : Altered perivascular fibroblast activity precedes ALS disease onset (Nature Medicine, (2021), 27, 4, (640-646), 10.1038/s41591-021-01295-9)
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Månberg, Anna, Skene, N., Sanders, F., Trusohamn, M., Remnestål, Julia, Szczepińska, A., Aksoylu, I. S., Lönnerberg, P., Ebarasi, L., Wouters, S., Lehmann, M., Olofsson, Jennie, von Gohren Antequera, I., Domaniku, A., De Schaepdryver, M., De Vocht, J., Poesen, K., Uhlén, Mathias, Anink, J., Mijnsbergen, C., Vergunst-Bosch, H., Hübers, A., Kläppe, U., Rodriguez-Vieitez, E., Gilthorpe, J. D., Hedlund, E., Harris, R. A., Aronica, E., Van Damme, P., Ludolph, A., Veldink, J., Ingre, C., Nilsson, Peter, Lewandowski, Sebastian, Månberg, Anna, Skene, N., Sanders, F., Trusohamn, M., Remnestål, Julia, Szczepińska, A., Aksoylu, I. S., Lönnerberg, P., Ebarasi, L., Wouters, S., Lehmann, M., Olofsson, Jennie, von Gohren Antequera, I., Domaniku, A., De Schaepdryver, M., De Vocht, J., Poesen, K., Uhlén, Mathias, Anink, J., Mijnsbergen, C., Vergunst-Bosch, H., Hübers, A., Kläppe, U., Rodriguez-Vieitez, E., Gilthorpe, J. D., Hedlund, E., Harris, R. A., Aronica, E., Van Damme, P., Ludolph, A., Veldink, J., Ingre, C., Nilsson, Peter, and Lewandowski, Sebastian
- Abstract
In the version of this article initially published, the label along the right margin of the top row in Fig. 2d (SO1DG93A) was incorrect. The correct label is ‘SOD1G93A’. The error has been corrected in the HTML and PDF versions of the article., QC 20220318
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- 2021
- Full Text
- View/download PDF
6. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease
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Bryois, J. (Julien), Skene, N. G. (Nathan G.), Hansen, T. F. (Thomas Folkmann), Kogelman, L. J. (Lisette J. A.), Watson, H. J. (Hunna J.), Liu, Z. (Zijing), E. D. (Eating Disorders Working Group of the Psychiatric Genomics Consortium), I. H. (International Headache Genetics Consortium), 2. R. (23andMe Research Team), Brueggeman, L. (Leo), Breen, G. (Gerome), Bulik, C. M. (Cynthia M.), Arenas, E. (Ernest), Hjerling-Leffler, J. (Jens), Sullivan, P. F. (Patrick F.), Bryois, J. (Julien), Skene, N. G. (Nathan G.), Hansen, T. F. (Thomas Folkmann), Kogelman, L. J. (Lisette J. A.), Watson, H. J. (Hunna J.), Liu, Z. (Zijing), E. D. (Eating Disorders Working Group of the Psychiatric Genomics Consortium), I. H. (International Headache Genetics Consortium), 2. R. (23andMe Research Team), Brueggeman, L. (Leo), Breen, G. (Gerome), Bulik, C. M. (Cynthia M.), Arenas, E. (Ernest), Hjerling-Leffler, J. (Jens), and Sullivan, P. F. (Patrick F.)
- Abstract
Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease., Eating Disorders Working Group of the Psychiatric Genomics Consortium Roger Adan17,18,19, Lars Alfredsson20, Tetsuya Ando21, Ole Andreassen22, Jessica Baker9, Andrew Bergen23,24, Wade Berrettini25, Andreas Birgegård26,27, Joseph Boden28, Ilka Boehm29, Claudette Boni30, Vesna Boraska Perica31,32, Harry Brandt33, Gerome Breen13,14, Julien Bryois1, Katharina Buehren34, Cynthia Bulik1,9,15, Roland Burghardt35, Matteo Cassina36, Sven Cichon37, Maurizio Clementi36, Jonathan Coleman13,14, Roger Cone38, Philippe Courtet39, Steven Crawford33, Scott Crow40, James Crowley16,26, unna Danner18, Oliver Davis41,42, Martina de Zwaan43, George Dedoussis44, Daniela Degortes45, Janiece DeSocio46, Danielle Dick47, Dimitris Dikeos48, Christian Dina49,50, Monika Dmitrzak-Weglarz51, Elisa Docampo Martinez52,53,54, Laramie Duncan55, Karin Egberts56, Stefan Ehrlich29, Geòrgia Escaramís52,53,54, Tõnu Esko57,58, Xavier Estivill52,53,54,59, Anne Farmer13, Angela Favaro45, Fernando Fernández-Aranda60,61, Manfred Fichter62,63, Krista Fischer57, Manuel Föcker64, Lenka Foretova65, Andreas Forstner37,66,67,68,69, Monica Forzan36, Christopher Franklin31, Steven Gallinger70, Héléna Gaspar13,14, Ina Giegling71, Johanna Giuranna64, Paola Giusti-Rodríquez16, Fragiskos Gonidakis72, Scott Gordon73, Philip Gorwood30,74, Monica Gratacos Mayora52,53,54, Jakob Grove75,76,77,78, Sébastien Guillaume39, Yiran Guo79, Hakon Hakonarson79,80, Katherine Halmi81, Ken Hanscombe82, Konstantinos Hatzikotoulas31, Joanna Hauser83, Johannes Hebebrand64, Sietske Helder13,84, Anjali Henders85, Stefan Herms37,69, Beate Herpertz-Dahlmann34, Wolfgang Herzog86, Anke Hinney64, L. John Horwood28, Christopher Hübel1,13, Laura Huckins31,87, James Hudson88, Hartmut Imgart89, Hidetoshi Inoko90, Vladimir Janout91, Susana Jiménez-Murcia60,61, Craig Johnson92, Jennifer Jordan93,94, Antonio Julià95, Anders Juréus1, Gursharan Kalsi13, Deborah Kaminská96, Allan Kaplan97, Jaakko Kaprio98,99, Leila Karhunen100, Andreas Karwautz101, Martien Kas, International Headache Genetics Consortium Verneri Anttila177, Ville Artto178, Andrea Carmine Belin179, Irene de Boer180, Dorret I. Boomsma181, Sigrid Børte182, Daniel I. Chasman183, Lynn Cherkas184, Anne Francke Christensen185, Bru Cormand186, Ester Cuenca-Leon177, George Davey-Smith187, Martin Dichgans188, Cornelia van Duijn189, Tonu Esko57, Ann Louise Esserlind190, Michel Ferrari180, Rune R. Frants180, Tobias Freilinger191, Nick Furlotte192, Padhraig Gormley177, Lyn Griffiths193, Eija Hamalainen194, Thomas Folkmann Hansen6, Marjo Hiekkala195, M. Arfan Ikram189, Andres Ingason196, Marjo-Riitta Järvelin197, Risto Kajanne194, Mikko Kallela178, Jaakko Kaprio98,99, Mari Kaunisto195, Lisette J. A. Kogelman6, Christian Kubisch198, Mitja Kurki177, Tobias Kurth199, Lenore Launer200, Terho Lehtimaki201, Davor Lessel198, Lannie Ligthart181, Nadia Litterman192, Arn van den Maagdenberg180, Alfons Macaya202, Rainer Malik188, Massimo Mangino184, George McMahon187, Bertram Muller-Myhsok203, Benjamin M. Neale177, Carrie Northover192, Dale R. Nyholt193, Jes Olesen190, Aarno Palotie58,99,137, Priit Palta194, Linda Pedersen182, Nancy Pedersen1, Danielle Posthuma181, Patricia Pozo-Rosich204, Alice Pressman205, Olli Raitakari206, Markus Schürks199, Celia Sintas186, Kari Stefansson196, Hreinn Stefansson196, Stacy Steinberg196, David Strachan207, Gisela Terwindt180, Marta Vila-Pueyo202, Maija Wessman195, Bendik S. Winsvold182, Huiying Zhao193 and John Anker Zwart182 177Broad Institute of MIT and Harvard, Cambridge, MA, USA. 178Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland. 179Karolinska Institutet, Stockholm, Sweden. 180Leiden University Medical Centre, Leiden, the Netherlands. 181VU University, Amsterdam, the Netherlands. 182Oslo University Hospital and University of Oslo, Oslo, Norway. 183Harvard Medical School, Cambridge, MA, USA. 184Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK. 185Danish Headache Center, C, 23andMe Research Team Michelle Agee208, Babak Alipanahi208, Adam Auton208, Robert Bell208, Katarzyna Bryc208, Sarah Elson208, Pierre Fontanillas208, Nicholas Furlotte208, Karl Heilbron208, David Hinds208, Karen Huber208, Aaron Kleinman208, Nadia Litterman208, Jennifer McCreight208, Matthew McIntyre208, Joanna Mountain208, Elizabeth Noblin208, Carrie Northover208, Steven Pitts208, J. Sathirapongsasuti208, Olga Sazonova208, Janie Shelton208, Suyash Shringarpure208, Chao Tian208, Joyce Tung208, Vladimir Vacic208 and Catherine Wilson208 20823andMe, Inc., Mountain View, CA, US
- Published
- 2020
7. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson's disease
- Author
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Bryois J., Skene N. G., Hansen T. F., Kogelman L. J. A., Watson H. J., Liu Z., Adan R., Alfredsson L., Ando T., Andreassen O., Baker J., Bergen A., Berrettini W., Birgegard A., Boden J., Boehm I., Boni C., Boraska Perica V., Brandt H., Breen G., Buehren K., Bulik C., Burghardt R., Cassina M., Cichon S., Clementi M., Coleman J., Cone R., Courtet P., Crawford S., Crow S., Crowley J., Danner U., Davis O., de Zwaan M., Dedoussis G., Degortes D., DeSocio J., Dick D., Dikeos D., Dina C., Dmitrzak-Weglarz M., Docampo Martinez E., Duncan L., Egberts K., Ehrlich S., Escaramis G., Esko T., Estivill X., Farmer A., Favaro A., Fernandez-Aranda F., Fichter M., Fischer K., Focker M., Foretova L., Forstner A., Forzan M., Franklin C., Gallinger S., Gaspar H., Giegling I., Giuranna J., Giusti-Rodriquez P., Gonidakis F., Gordon S., Gorwood P., Gratacos Mayora M., Grove J., Guillaume S., Guo Y., Hakonarson H., Halmi K., Hanscombe K., Hatzikotoulas K., Hauser J., Hebebrand J., Helder S., Henders A., Herms S., Herpertz-Dahlmann B., Herzog W., Hinney A., Horwood L. J., Hubel C., Huckins L., Hudson J., Imgart H., Inoko H., Janout V., Jimenez-Murcia S., Johnson C., Jordan J., Julia A., Jureus A., Kalsi G., Kaminska D., Kaplan A., Kaprio J., Karhunen L., Karwautz A., Kas M., Kaye W., Kennedy J., Kennedy M., Keski-Rahkonen A., Kiezebrink K., Kim Y. -R., Kirk K., Klareskog L., Klump K., Knudsen G. P., La Via M., Landen M., Larsen J., Le Hellard S., Leppa V., Levitan R., Li D., Lichtenstein P., Lilenfeld L., Lin B. D., Lissowska J., Luykx J., Magistretti P., Maj M., Mannik K., Marsal S., Marshall C., Martin N., Mattheisen M., Mattingsdal M., McDevitt S., McGuffin P., Medland S., Metspalu A., Meulenbelt I., Micali N., Mitchell J., Mitchell K., Monteleone P., Monteleone A. M., Montgomery G., Mortensen P. B., Munn-Chernoff M., Nacmias B., Navratilova M., Norring C., Ntalla I., Olsen C., Ophoff R., O'Toole J., Padyukov L., Palotie A., Pantel J., Papezova H., Parker R., Pearson J., Pedersen N., Petersen L., Pinto D., Purves K., Rabionet R., Raevuori A., Ramoz N., Reichborn-Kjennerud T., Ricca V., Ripatti S., Ripke S., Ritschel F., Roberts M., Rotondo A., Rujescu D., Rybakowski F., Santonastaso P., Scherag A., Scherer S., Schmidt U., Schork N., Schosser A., Seitz J., Slachtova L., Slagboom P. E., Slof-Op 't Landt M., Slopien A., Sorbi S., Strober M., Stuber G., Sullivan P., Swiatkowska B., Szatkiewicz J., Tachmazidou I., Tenconi E., Thornton L., Tortorella A., Tozzi F., Treasure J., Tsitsika A., Tyszkiewicz-Nwafor M., Tziouvas K., van Elburg A., van Furth E., Wade T., Wagner G., Walton E., Watson H., Werge T., Whiteman D., Widen E., Woodside D. B., Yao S., Yilmaz Z., Zeggini E., Zerwas S., Zipfel S., Anttila V., Artto V., Belin A. C., de Boer I., Boomsma D. I., Borte S., Chasman D. I., Cherkas L., Christensen A. F., Cormand B., Cuenca-Leon E., Davey-Smith G., Dichgans M., van Duijn C., Esserlind A. L., Ferrari M., Frants R. R., Freilinger T., Furlotte N., Gormley P., Griffiths L., Hamalainen E., Hiekkala M., Ikram M. A., Ingason A., Jarvelin M. -R., Kajanne R., Kallela M., Kaunisto M., Kubisch C., Kurki M., Kurth T., Launer L., Lehtimaki T., Lessel D., Ligthart L., Litterman N., Maagdenberg A., Macaya A., Malik R., Mangino M., McMahon G., Muller-Myhsok B., Neale B. M., Northover C., Nyholt D. R., Olesen J., Palta P., Pedersen L., Posthuma D., Pozo-Rosich P., Pressman A., Raitakari O., Schurks M., Sintas C., Stefansson K., Stefansson H., Steinberg S., Strachan D., Terwindt G., Vila-Pueyo M., Wessman M., Winsvold B. S., Zhao H., Zwart J. A., Agee M., Alipanahi B., Auton A., Bell R., Bryc K., Elson S., Fontanillas P., Heilbron K., Hinds D., Huber K., Kleinman A., McCreight J., McIntyre M., Mountain J., Noblin E., Pitts S., Sathirapongsasuti J., Sazonova O., Shelton J., Shringarpure S., Tian C., Tung J., Vacic V., Wilson C., Brueggeman L., Bulik C. M., Arenas E., Hjerling-Leffler J., Sullivan P. F., Functional Genomics, APH - Methodology, APH - Mental Health, Biological Psychology, APH - Personalized Medicine, Amsterdam Neuroscience - Complex Trait Genetics, Complex Trait Genetics, Bryois, Julien, Hansen, Thomas Folkmann, Kogelman, Lisette J A, Watson, Hunna J, Breen, Gerome, Bulik, Cynthia M, Micali, Nadia, van Duijn, C, Kas lab, Bryois, J., Skene, N. G., Hansen, T. F., Kogelman, L. J. A., Watson, H. J., Liu, Z., Adan, R., Alfredsson, L., Ando, T., Andreassen, O., Baker, J., Bergen, A., Berrettini, W., Birgegard, A., Boden, J., Boehm, I., Boni, C., Boraska Perica, V., Brandt, H., Breen, G., Buehren, K., Bulik, C., Burghardt, R., Cassina, M., Cichon, S., Clementi, M., Coleman, J., Cone, R., Courtet, P., Crawford, S., Crow, S., Crowley, J., Danner, U., Davis, O., de Zwaan, M., Dedoussis, G., Degortes, D., Desocio, J., Dick, D., Dikeos, D., Dina, C., Dmitrzak-Weglarz, M., Docampo Martinez, E., Duncan, L., Egberts, K., Ehrlich, S., Escaramis, G., Esko, T., Estivill, X., Farmer, A., Favaro, A., Fernandez-Aranda, F., Fichter, M., Fischer, K., Focker, M., Foretova, L., Forstner, A., Forzan, M., Franklin, C., Gallinger, S., Gaspar, H., Giegling, I., Giuranna, J., Giusti-Rodriquez, P., Gonidakis, F., Gordon, S., Gorwood, P., Gratacos Mayora, M., Grove, J., Guillaume, S., Guo, Y., Hakonarson, H., Halmi, K., Hanscombe, K., Hatzikotoulas, K., Hauser, J., Hebebrand, J., Helder, S., Henders, A., Herms, S., Herpertz-Dahlmann, B., Herzog, W., Hinney, A., Horwood, L. J., Hubel, C., Huckins, L., Hudson, J., Imgart, H., Inoko, H., Janout, V., Jimenez-Murcia, S., Johnson, C., Jordan, J., Julia, A., Jureus, A., Kalsi, G., Kaminska, D., Kaplan, A., Kaprio, J., Karhunen, L., Karwautz, A., Kas, M., Kaye, W., Kennedy, J., Kennedy, M., Keski-Rahkonen, A., Kiezebrink, K., Kim, Y. -R., Kirk, K., Klareskog, L., Klump, K., Knudsen, G. P., La Via, M., Landen, M., Larsen, J., Le Hellard, S., Leppa, V., Levitan, R., Li, D., Lichtenstein, P., Lilenfeld, L., Lin, B. D., Lissowska, J., Luykx, J., Magistretti, P., Maj, M., Mannik, K., Marsal, S., Marshall, C., Martin, N., Mattheisen, M., Mattingsdal, M., Mcdevitt, S., Mcguffin, P., Medland, S., Metspalu, A., Meulenbelt, I., Micali, N., Mitchell, J., Mitchell, K., Monteleone, P., Monteleone, A. M., Montgomery, G., Mortensen, P. B., Munn-Chernoff, M., Nacmias, B., Navratilova, M., Norring, C., Ntalla, I., Olsen, C., Ophoff, R., O'Toole, J., Padyukov, L., Palotie, A., Pantel, J., Papezova, H., Parker, R., Pearson, J., Pedersen, N., Petersen, L., Pinto, D., Purves, K., Rabionet, R., Raevuori, A., Ramoz, N., Reichborn-Kjennerud, T., Ricca, V., Ripatti, S., Ripke, S., Ritschel, F., Roberts, M., Rotondo, A., Rujescu, D., Rybakowski, F., Santonastaso, P., Scherag, A., Scherer, S., Schmidt, U., Schork, N., Schosser, A., Seitz, J., Slachtova, L., Slagboom, P. E., Slof-Op 't Landt, M., Slopien, A., Sorbi, S., Strober, M., Stuber, G., Sullivan, P., Swiatkowska, B., Szatkiewicz, J., Tachmazidou, I., Tenconi, E., Thornton, L., Tortorella, A., Tozzi, F., Treasure, J., Tsitsika, A., Tyszkiewicz-Nwafor, M., Tziouvas, K., van Elburg, A., van Furth, E., Wade, T., Wagner, G., Walton, E., Watson, H., Werge, T., Whiteman, D., Widen, E., Woodside, D. B., Yao, S., Yilmaz, Z., Zeggini, E., Zerwas, S., Zipfel, S., Anttila, V., Artto, V., Belin, A. C., de Boer, I., Boomsma, D. I., Borte, S., Chasman, D. I., Cherkas, L., Christensen, A. F., Cormand, B., Cuenca-Leon, E., Davey-Smith, G., Dichgans, M., van Duijn, C., Esserlind, A. L., Ferrari, M., Frants, R. R., Freilinger, T., Furlotte, N., Gormley, P., Griffiths, L., Hamalainen, E., Hiekkala, M., Ikram, M. A., Ingason, A., Jarvelin, M. -R., Kajanne, R., Kallela, M., Kaunisto, M., Kubisch, C., Kurki, M., Kurth, T., Launer, L., Lehtimaki, T., Lessel, D., Ligthart, L., Litterman, N., Maagdenberg, A., Macaya, A., Malik, R., Mangino, M., Mcmahon, G., Muller-Myhsok, B., Neale, B. M., Northover, C., Nyholt, D. R., Olesen, J., Palta, P., Pedersen, L., Posthuma, D., Pozo-Rosich, P., Pressman, A., Raitakari, O., Schurks, M., Sintas, C., Stefansson, K., Stefansson, H., Steinberg, S., Strachan, D., Terwindt, G., Vila-Pueyo, M., Wessman, M., Winsvold, B. S., Zhao, H., Zwart, J. A., Agee, M., Alipanahi, B., Auton, A., Bell, R., Bryc, K., Elson, S., Fontanillas, P., Heilbron, K., Hinds, D., Huber, K., Kleinman, A., Mccreight, J., Mcintyre, M., Mountain, J., Noblin, E., Pitts, S., Sathirapongsasuti, J., Sazonova, O., Shelton, J., Shringarpure, S., Tian, C., Tung, J., Vacic, V., Wilson, C., Brueggeman, L., Bulik, C. M., Arenas, E., Hjerling-Leffler, J., and Sullivan, P. F.
- Subjects
Nervous system ,Netherlands Twin Register (NTR) ,Aging ,Parkinson's disease ,Medizin ,Genome-wide association study ,Disease ,Neurodegenerative ,Medical and Health Sciences ,ddc:616.89 ,Mice ,0302 clinical medicine ,Malaltia de Parkinson ,Monoaminergic ,Eating Disorders Working Group of the Psychiatric Genomics Consortium ,2.1 Biological and endogenous factors ,Aetiology ,Cervell ,ALZHEIMERS ,NEURONS ,Animals ,Brain ,Genome-Wide Association Study ,Humans ,Neurons ,Parkinson Disease ,Transcriptome ,11 Medical and Health Sciences ,Genetics & Heredity ,0303 health sciences ,Parkinson Disease/etiology/genetics/pathology ,HERITABILITY ,International Headache Genetics Consortium ,Biological Sciences ,Transcriptome/genetics ,medicine.anatomical_structure ,Neurological ,Genome-Wide Association Study/methods ,Alzheimer's disease ,Life Sciences & Biomedicine ,Gens ,Cell type ,TISSUES ,1.1 Normal biological development and functioning ,Biology ,IMMUNITY ,23andMe Research Team ,Article ,03 medical and health sciences ,ENTERIC NERVOUS-SYSTEM ,SDG 3 - Good Health and Well-being ,Underpinning research ,medicine ,Genetics ,Brain/pathology ,GENOME-WIDE ASSOCIATION ,NUCLEUS ,METAANALYSIS ,030304 developmental biology ,Science & Technology ,Neurons/pathology ,Human Genome ,Neurosciences ,06 Biological Sciences ,medicine.disease ,RISK LOCI ,Brain Disorders ,Genes ,Enteric nervous system ,Neuroscience ,030217 neurology & neurosurgery ,Developmental Biology - Abstract
Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease. Eating Disorders Working Group of the Psychiatric Genomics Consortium Roger Adan17,18,19, Lars Alfredsson20, Tetsuya Ando21, Ole Andreassen22, Jessica Baker9, Andrew Bergen23,24, Wade Berrettini25, Andreas Birgegård26,27, Joseph Boden28, Ilka Boehm29, Claudette Boni30, Vesna Boraska Perica31,32, Harry Brandt33, Gerome Breen13,14, Julien Bryois1, Katharina Buehren34, Cynthia Bulik1,9,15, Roland Burghardt35, Matteo Cassina36, Sven Cichon37, Maurizio Clementi36, Jonathan Coleman13,14, Roger Cone38, Philippe Courtet39, Steven Crawford33, Scott Crow40, James Crowley16,26, unna Danner18, Oliver Davis41,42, Martina de Zwaan43, George Dedoussis44, Daniela Degortes45, Janiece DeSocio46, Danielle Dick47, Dimitris Dikeos48, Christian Dina49,50, Monika Dmitrzak-Weglarz51, Elisa Docampo Martinez52,53,54, Laramie Duncan55, Karin Egberts56, Stefan Ehrlich29, Geòrgia Escaramís52,53,54, Tõnu Esko57,58, Xavier Estivill52,53,54,59, Anne Farmer13, Angela Favaro45, Fernando Fernández-Aranda60,61, Manfred Fichter62,63, Krista Fischer57, Manuel Föcker64, Lenka Foretova65, Andreas Forstner37,66,67,68,69, Monica Forzan36, Christopher Franklin31, Steven Gallinger70, Héléna Gaspar13,14, Ina Giegling71, Johanna Giuranna64, Paola Giusti-Rodríquez16, Fragiskos Gonidakis72, Scott Gordon73, Philip Gorwood30,74, Monica Gratacos Mayora52,53,54, Jakob Grove75,76,77,78, Sébastien Guillaume39, Yiran Guo79, Hakon Hakonarson79,80, Katherine Halmi81, Ken Hanscombe82, Konstantinos Hatzikotoulas31, Joanna Hauser83, Johannes Hebebrand64, Sietske Helder13,84, Anjali Henders85, Stefan Herms37,69, Beate Herpertz-Dahlmann34, Wolfgang Herzog86, Anke Hinney64, L. John Horwood28, Christopher Hübel1,13, Laura Huckins31,87, James Hudson88, Hartmut Imgart89, Hidetoshi Inoko90, Vladimir Janout91, Susana Jiménez-Murcia60,61, Craig Johnson92, Jennifer Jordan93,94, Antonio Julià95, Anders Juréus1, Gursharan Kalsi13, Deborah Kaminská96, Allan Kaplan97, Jaakko Kaprio98,99, Leila Karhunen100, Andreas Karwautz101, Martien Kas17,102, Walter Kaye103, James Kennedy97, Martin Kennedy104, Anna Keski-Rahkonen98, Kirsty Kiezebrink105, Youl-Ri Kim106, Katherine Kirk73, Lars Klareskog107, Kelly Klump108, Gun Peggy Knudsen109, Maria La Via9, Mikael Landén1,19, Janne Larsen76,110,111, Stephanie Le Hellard112,113,114, Virpi Leppä1, Robert Levitan115, Dong Li79, Paul Lichtenstein1, Lisa Lilenfeld116, Bochao Danae Lin17, Jolanta Lissowska117, Jurjen Luykx17, Pierre Magistretti118,119, Mario Maj120, Katrin Mannik57,121, Sara Marsal95, Christian Marshall122, Nicholas Martin73, Manuel Mattheisen26,27,75,123, Morten Mattingsdal22, Sara McDevitt124,125, Peter McGuffin13, Sarah Medland73, Andres Metspalu57,126, Ingrid Meulenbelt127, Nadia Micali128,129, James Mitchell130, Karen Mitchell131, Palmiero Monteleone132, Alessio Maria Monteleone120, Grant Montgomery73,85,133, Preben Bo Mortensen76,110,111, Melissa Munn-Chernoff9, Benedetta Nacmias134, Marie Navratilova65, Claes Norring26,27, Ioanna Ntalla44, Catherine Olsen73, Roel Ophoff17,135, Julie O’Toole136, Leonid Padyukov107, Aarno Palotie58,99,137, Jacques Pantel30, Hana Papezova96, Richard Parker73, John Pearson138, Nancy Pedersen1, Liselotte Petersen76,110,111, Dalila Pinto87, Kirstin Purves13, Raquel Rabionet139,140,141, Anu Raevuori98, Nicolas Ramoz30, Ted Reichborn-Kjennerud109,142, Valdo Ricca134,143, Samuli Ripatti144, Stephan Ripke145,146,147, Franziska Ritschel29,148, Marion Roberts13, Alessandro Rotondo149, Dan Rujescu62,71, Filip Rybakowski150, Paolo Santonastaso151, André Scherag152, Stephen Scherer153, ulrike Schmidt13, Nicholas Schork154, Alexandra Schosser155, Jochen Seitz34, Lenka Slachtova156, P. Eline Slagboom127, Margarita Slof-Op ‘t Landt157,158, Agnieszka Slopien159, Sandro Sorbi134,160, Michael Strober161,162, Garret Stuber9,163, Patrick Sullivan1,16, Beata Świątkowska164, Jin Szatkiewicz16, Ioanna Tachmazidou31, Elena Tenconi45, Laura Thornton9, Alfonso Tortorella165,166, Federica Tozzi167, Janet Treasure13, Artemis Tsitsika168, Marta Tyszkiewicz-Nwafor150, Konstantinos Tziouvas169, Annemarie van Elburg18,170, Eric van Furth157,158, Tracey Wade171, Gudrun Wagner101, Esther Walton29, Hunna Watson9,10,11, Thomas Werge172, David Whiteman73, Elisabeth Widen99, D. Blake Woodside173,174, Shuyang Yao1, Zeynep Yilmaz9,16, Eleftheria Zeggini31,175, Stephanie Zerwas9 and Stephan Zipfel176 17Brain Center Rudolf Magnus, Department of Translational Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands. 18Center for Eating Disorders Rintveld, Altrecht Mental Health Institute, Zeist, the Netherlands. 19Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 20Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. 21Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan. 22NORMENT KG Jebsen Centre, Division of Mental Health and Addiction, University of Oslo, Oslo University Hospital, Oslo, Norway. 23BioRealm, LLC, Walnut, CA, USA. 24Oregon Research Institute, Eugene, OR, USA. 25Department of Psychiatry, Center for Neurobiology and Behavior, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. 26Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. 27Center for Psychiatry Research, Stockholm Health Care Services, Stockholm City Council, Stockholm, Sweden. 28Christchurch Health and Development Study, University of Otago, Christchurch, New Zealand. 29Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany. 30INSERM U894, Centre of Psychiatry and Neuroscience, Paris, France. 31Wellcome Sanger Institute, Hinxton, Cambridge, UK. 32Department of Medical Biology, School of Medicine, University of Split, Split, Croatia. 33The Center for Eating Disorders at Sheppard Pratt, Baltimore, MD, USA. 34Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany. 35Klinikum Frankfurt/Oder, Frankfurt, Germany. 36Clinical Genetics Unit, Department of Woman and Child Health, University of Padova, Padua, Italy. 37Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland. 38Life Sciences Institute and Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA. 39Department of Emergency Psychiatry and Post-Acute Care, CHRU Montpellier, University of Montpellier, Montpellier, France. 40Department of Psychiatry, University of Minnesota, Minneapolis, MN, USA. 41MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK. 42School of Social and Community Medicine, University of Bristol, Bristol, UK. 43Department of Psychosomatic Medicine and Psychotherapy, Hannover Medical School, Hannover, Germany. 44Department of Nutrition and Dietetics, Harokopio University, Athens, Greece. 45Department of Neurosciences, University of Padova, Padua, Italy. 46College of Nursing, Seattle University, Seattle, WA, USA. 47Department of Psychology, Virginia Commonwealth University, Richmond, VA, USA. 48Department of Psychiatry, Athens University Medical School, Athens University, Athens, Greece. 49L’institut du thorax, INSERM, CNRS, UNIV Nantes, Nantes, France. 50L’institut du thorax, CHU Nantes, Nantes, France. 51Department of Psychiatric Genetics, Poznań University of Medical Sciences, Poznań, Poland. 52Barcelona Institute of Science and Technology, Barcelona, Spain. 53Universitat Pompeu Fabra, Barcelona, Spain. 54Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. 55Department of Psychiatry and Behavioral Sciences, Stanford University Stanford, CA, USA. 56Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Würzburg, Centre for Mental Health, Würzburg, Germany. 57Estonian Genome Center, University of Tartu, Tartu, Estonia. 58Program in Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 59Genomics and Disease, Bioinformatics and Genomics Programme, Centre for Genomic Regulation, Barcelona, Spain. 60Department of Psychiatry, University Hospital of Bellvitge –IDIBELL and CIBERobn, Barcelona, Spain. 61Department of Clinical Sciences, School of Medicine, University of Barcelona, Barcelona, Spain. 62Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University (LMU), Munich, Germany. 63Schön Klinik Roseneck affiliated with the Medical Faculty of the University of Munich (LMU), Munich, Germany. 64Department of Child and Adolescent Psychiatry, University Hospital Essen, University of Duisburg-Essen, Essen, Germany. 65Department of Cancer, Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic. 66Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany. 67Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. 68Department of Psychiatry (UPK), University of Basel, Basel, Switzerland. 69Department of Biomedicine, University of Basel, Basel, Switzerland. 70Department of Surgery, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 71Department of Psychiatry, Psychotherapy and Psychosomatics, Martin Luther University of Halle-Wittenberg, Halle, Germany. 721st Psychiatric Department, National and Kapodistrian University of Athens, Medical School, Eginition Hospital, Athens, Greece. 73QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. 74CMME (Groupe Hospitalier Sainte-Anne), Paris Descartes University, Paris, France. 75Department of Biomedicine, Aarhus University, Aarhus, Denmark. 76The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSyCH), Aarhus, Denmark. 77Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark. 78Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark. 79Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 80Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 81Department of Psychiatry, Weill Cornell Medical College, New york, Ny, USA. 82Department of Medical and Molecular Genetics, King’s College London, Guy’s Hospital, London, UK. 83Department of Adult Psychiatry, Poznań University of Medical Sciences, Poznań, Poland. 84Zorg op Orde, Leidschendam, the Netherlands. 85Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. 86Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany. 87Department of Psychiatry, and Genetics and Genomics Sciences, Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New york, Ny, USA. 88Biological Psychiatry Laboratory, McLean Hospital/Harvard Medical School, Boston, MA, USA. 89Eating Disorders Unit, Parklandklinik, Bad Wildungen, Germany. 90Department of Molecular Life Science, Division of Basic Medical Science and Molecular Medicine, School of Medicine, Tokai University, Isehara, Japan. 91Faculty of Health Sciences, Palacky University, Olomouc, Czech Republic. 92Eating Recovery Center, Denver, CO, USA. 93Department of Psychological Medicine, University of Otago, Christchurch, New Zealand. 94Canterbury District Health Board, Christchurch, New Zealand. 95Rheumatology Research Group, Vall d’Hebron Research Institute, Barcelona, Spain. 96Department of Psychiatry, First Faculty of Medicine, Charles University, Prague, Czech Republic. 97Center for Addiction and Mental Health, Department of Psychiatry, Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. 98Department of Public Health, University of Helsinki, Helsinki, Finland. 99Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland. 100Institute of Public Health and Clinical Nutrition, Department of Clinical Nutrition, University of Eastern Finland, Kuopio, Finland. 101Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria. 102Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, the Netherlands. 103Department of Psychiatry, University of California San Diego, San Diego, CA, USA. 104Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand. 105Health Services Research Unit, University of Aberdeen, Aberdeen, UK. 106Department of Psychiatry, Seoul Paik Hospital, Inje University, Seoul, Korea. 107Rheumatology Unit, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden. 108Department of Psychology, Michigan State University, East Lansing, MI, USA. 109Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway. 110National Centre for Register-Based Research, Aarhus BSS, Aarhus University, Aarhus, Denmark. 111Centre for Integrated Register-based Research (CIRRAU), Aarhus University, Aarhus, Denmark. 112Department of Clinical Science, K.G. Jebsen Centre for Psychosis Research, Norwegian Centre for Mental Disorders Research (NORMENT), University of Bergen, Bergen, Norway. 113Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway. 114Department of Clinical Medicine, Laboratory Building, Haukeland University Hospital, Bergen, Norway. 115Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada. 116American School of Professional Psychology, Argosy University, Northern Virginia, Arlington, VA, USA. 117Department of Cancer Epidemiology and Prevention, M Skłodowska-Curie Cancer Center - Oncology Center, Warsaw, Poland. 118BESE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia. 119Department of Psychiatry, University of Lausanne-University Hospital of Lausanne (UNIL-CHUV), Lausanne, Switzerland. 120Department of Psychiatry, University of Campania ‘Luigi Vanvitelli’, Naples, Italy. 121Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland. 122Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada. 123Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany. 124Department of Psychiatry, University College Cork, Cork, Ireland. 125Eist Linn Adolescent Unit, Bessborough, Health Service Executive South, Cork, Ireland. 126Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. 127Molecular Epidemiology Section (Department of Medical Statistics), Leiden University Medical Centre, Leiden, the Netherlands. 128Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland. 129Division of Child and Adolescent Psychiatry, Geneva University Hospital, Geneva, Switzerland. 130Department of Psychiatry and Behavioral Science, University of North Dakota School of Medicine and Health Sciences, Fargo, ND, USA. 131National Center for PTSD, VA Boston Healthcare System, Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA. 132Department of Medicine, Surgery and Dentistry ‘Scuola Medica Salernitana’, University of Salerno, Salerno, Italy. 133Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. 134Department of Neuroscience, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy. 135Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA. 136Kartini Clinic, Portland, OR, USA. 137Center for Human Genome Research at the Massachusetts General Hospital, Boston, MA, USA. 138Biostatistics and Computational Biology Unit, University of Otago, Christchurch, New Zealand. 139Saint Joan de Déu Research Institute, Saint Joan de Déu Barcelona Children’s Hospital, Barcelona, Spain. 140Institute of Biomedicine (IBUB), University of Barcelona, Barcelona, Spain. 141Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain. 142Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 143Department of Health Science, University of Florence, Florence, Italy. 144Department of Biometry, University of Helsinki, Helsinki, Finland. 145Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. 146Stanley Center for Psychiatric Research, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 147Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin, Germany. 148Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany. 149Department of Psychiatry, Neurobiology, Pharmacology, and Biotechnologies, University of Pisa, Pisa, Italy. 150Department of Psychiatry, Poznań University of Medical Sciences, Poznań, Poland. 151Department of Neurosciences, Padua Neuroscience Center, University of Padova, Padua, Italy. 152Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany. 153Department of Genetics and Genomic Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. 154J. Craig Venter Institute (JCVI), La Jolla, CA, USA. 155Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria. 156Department of Pediatrics and Center of Applied Genomics, First Faculty of Medicine, Charles University, Prague, Czech Republic. 157Center for Eating Disorders Ursula, Rivierduinen, Leiden, the Netherlands. 158Department of Psychiatry, Leiden University Medical Centre, Leiden, the Netherlands. 159Department of Child and Adolescent Psychiatry, Poznań University of Medical Sciences, Poznań, Poland. 160IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy. 161Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA. 162David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. 163Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 164Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Lodz, Poland. 165Department of Psychiatry, University of Naples SUN, Naples, Italy. 166Department of Psychiatry, University of Perugia, Perugia, Italy. 167Brain Sciences Department, Stremble Ventures, Limassol, Cyprus. 168Adolescent Health Unit, Second Department of Pediatrics, ‘P. & A. Kyriakou’ Children’s Hospital, University of Athens, Athens, Greece. 169Pediatric Intensive Care Unit, ‘P. & A. Kyriakou’ Children’s Hospital, University of Athens, Athens, Greece. 170Faculty of Social and Behavioral Sciences, Utrecht University, Utrecht, the Netherlands. 171School of Psychology, Flinders University, Adelaide, South Australia, Australia. 172Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. 173Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 174Toronto General Hospital, Toronto, Ontario, Canada. 175Institute of Translational Genomics, Helmholtz Zentrum München, Neuherberg, Germany. 176Department of Internal Medicine VI, Psychosomatic Medicine and Psychotherapy, University Medical Hospital Tübingen, Tübingen, Germany International Headache Genetics Consortium Verneri Anttila177, Ville Artto178, Andrea Carmine Belin179, Irene de Boer180, Dorret I. Boomsma181, Sigrid Børte182, Daniel I. Chasman183, Lynn Cherkas184, Anne Francke Christensen185, Bru Cormand186, Ester Cuenca-Leon177, George Davey-Smith187, Martin Dichgans188, Cornelia van Duijn189, Tonu Esko57, Ann Louise Esserlind190, Michel Ferrari180, Rune R. Frants180, Tobias Freilinger191, Nick Furlotte192, Padhraig Gormley177, Lyn Griffiths193, Eija Hamalainen194, Thomas Folkmann Hansen6, Marjo Hiekkala195, M. Arfan Ikram189, Andres Ingason196, Marjo-Riitta Järvelin197, Risto Kajanne194, Mikko Kallela178, Jaakko Kaprio98,99, Mari Kaunisto195, Lisette J. A. Kogelman6, Christian Kubisch198, Mitja Kurki177, Tobias Kurth199, Lenore Launer200, Terho Lehtimaki201, Davor Lessel198, Lannie Ligthart181, Nadia Litterman192, Arn van den Maagdenberg180, Alfons Macaya202, Rainer Malik188, Massimo Mangino184, George McMahon187, Bertram Muller-Myhsok203, Benjamin M. Neale177, Carrie Northover192, Dale R. Nyholt193, Jes Olesen190, Aarno Palotie58,99,137, Priit Palta194, Linda Pedersen182, Nancy Pedersen1, Danielle Posthuma181, Patricia Pozo-Rosich204, Alice Pressman205, Olli Raitakari206, Markus Schürks199, Celia Sintas186, Kari Stefansson196, Hreinn Stefansson196, Stacy Steinberg196, David Strachan207, Gisela Terwindt180, Marta Vila-Pueyo202, Maija Wessman195, Bendik S. Winsvold182, Huiying Zhao193 and John Anker Zwart182 177Broad Institute of MIT and Harvard, Cambridge, MA, USA. 178Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland. 179Karolinska Institutet, Stockholm, Sweden. 180Leiden University Medical Centre, Leiden, the Netherlands. 181VU University, Amsterdam, the Netherlands. 182Oslo University Hospital and University of Oslo, Oslo, Norway. 183Harvard Medical School, Cambridge, MA, USA. 184Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK. 185Danish Headache Center, Copenhagen University Hospital, Copenhagen, Denmark. 186University of Barcelona, Barcelona, Spain. 187Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK. 188Institute for Stroke and Dementia Research, Munich, Germany. 189Erasmus University Medical Centre, Rotterdam, the Netherlands. 190Danish Headache Center, Department of Neurology, Rigshospitalet, Glostrup, Denmark. 191University of Tübingen, Tübingen, Germany. 19223&Me Inc., Mountain View, CA, USA. 193Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. 194Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. 195Folkhälsan Institute of Genetics, Helsinki, Finland. 196Decode genetics Inc., Reykjavik, Iceland. 197University of Oulu, Biocenter Oulu, Finland. 198University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 199Harvard Medical School, Boston, MA, USA. 200National Institute on Aging, Bethesda, MD, USA. 201School of Medicine, University of Tampere, Tampere, Finland. 202Vall d’Hebron Research Institute, Barcelona, Spain. 203Max Planck Institute of Psychiatry, Munich, Germany. 204Headache Research Group, Universitat Autònoma de Barcelona, Barcelona, Spain. 205Sutter Health, Sacramento, CA, USA. 206Department of Medicine, University of Turku, Turku, Finland. 207Population Health Research Institute, St George’s University of London, London, UK. 23andMe Research Team Michelle Agee208, Babak Alipanahi208, Adam Auton208, Robert Bell208, Katarzyna Bryc208, Sarah Elson208, Pierre Fontanillas208, Nicholas Furlotte208, Karl Heilbron208, David Hinds208, Karen Huber208, Aaron Kleinman208, Nadia Litterman208, Jennifer McCreight208, Matthew McIntyre208, Joanna Mountain208, Elizabeth Noblin208, Carrie Northover208, Steven Pitts208, J. Sathirapongsasuti208, Olga Sazonova208, Janie Shelton208, Suyash Shringarpure208, Chao Tian208, Joyce Tung208, Vladimir Vacic208 and Catherine Wilson208 20823andMe, Inc., Mountain View, CA, US
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- 2020
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8. Genetic analysis identifies molecular systems and biological pathways associated with household income
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David Hill, W, Davies, N, Ritchie, S, Skene, N, Bryois, J, Bell, S, Angelantonio, ED, Roberts, D, Xueyi, S, Davies, G, Liewald, DCM, Porteous, D, Hayward, C, Butterworth, A, McIntosh, A, Gale, C, and Deary, I
- Abstract
Socio-economic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. Previous genome-wide association studies (GWAS) using household income as a marker of SEP have shown that common genetic variants account for 11% of its variation. Here, in a sample of 286,301 participants from UK Biobank, we identified 30 independent genome-wide significant loci, 29 novel, that are associated with household income. Using a recently-developed method to meta-analyze data that leverages power from genetically-correlated traits, we identified an additional 120 income-associated loci. These loci showed clear evidence of functional enrichment, with transcriptional differences identified across multiple cortical tissues, in addition to links with GABAergic and serotonergic neurotransmission. We identified neurogenesis and the components of the synapse as candidate biological systems that are linked with income. By combining our GWAS on income with data from eQTL studies and chromatin interactions, 24 genes were prioritized for follow up, 18 of which were previously associated with cognitive ability. Using Mendelian Randomization, we identified cognitive ability as one of the causal, partly-heritable phenotypes that bridges the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. Significant differences between genetic correlations indicated that, the genetic variants associated with income are related to better mental health than those linked to educational attainment (another commonly-used marker of SEP). Finally, we were able to predict 2.5% of income differences using genetic data alone in an independent sample. These results are important for understanding the observed socioeconomic inequalities in Great Britain today.
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- 2019
9. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways.
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Jansen, P.R., Watanabe, K., Stringer, S., Skene, N., Bryois, J., Hammerschlag, A.R., De Leeuw, C.A., Benjamins, Jeroen S, Munoz-Manchado, A.B., Nagel, M., Savage, J.E., Tiemeier, H., White, T., The 23andMe Research Team, Tung, J.Y., Hinds, D.A., Vacic, V., Wang, X., Sullivan, P.F., Van der Sluis, S., Polderman, T.J.C., Smit, A.B., Hjerling-Leffler, J., van Someren, E.J.W., Posthuma, Danielle, Jansen, P.R., Watanabe, K., Stringer, S., Skene, N., Bryois, J., Hammerschlag, A.R., De Leeuw, C.A., Benjamins, Jeroen S, Munoz-Manchado, A.B., Nagel, M., Savage, J.E., Tiemeier, H., White, T., The 23andMe Research Team, Tung, J.Y., Hinds, D.A., Vacic, V., Wang, X., Sullivan, P.F., Van der Sluis, S., Polderman, T.J.C., Smit, A.B., Hjerling-Leffler, J., van Someren, E.J.W., and Posthuma, Danielle
- Abstract
Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.
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- 2019
10. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk
- Author
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Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, Ida K., Hägg, S., Athanasiu, L., Voyle, N., Proitsi, P., Witoelar, A., Stringer, S., Aarsland, D., Almdahl, I. S., Andersen, F., Bergh, S., Bettella, F., Bjornsson, S., Brækhus, A., Bråthen, G., de Leeuw, C., Desikan, R. S., Djurovic, S., Dumitrescu, L., Fladby, T., Hohman, T. J., Jonsson, P. V., Kiddle, S. J., Rongve, A., Saltvedt, I., Sando, S. B., Selbæk, G., Shoai, M., Skene, N. G., Snaedal, J., Stordal, E., Ulstein, I. D., Wang, Y., White, L. R., Hardy, J., Hjerling-Leffler, J., Sullivan, P. F., van der Flier, W. M., Dobson, R., Davis, L. K., Stefansson, H., Stefansson, K., Pedersen, N. L., Ripke, S., Andreassen, O. A., Posthuma, D., Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, Ida K., Hägg, S., Athanasiu, L., Voyle, N., Proitsi, P., Witoelar, A., Stringer, S., Aarsland, D., Almdahl, I. S., Andersen, F., Bergh, S., Bettella, F., Bjornsson, S., Brækhus, A., Bråthen, G., de Leeuw, C., Desikan, R. S., Djurovic, S., Dumitrescu, L., Fladby, T., Hohman, T. J., Jonsson, P. V., Kiddle, S. J., Rongve, A., Saltvedt, I., Sando, S. B., Selbæk, G., Shoai, M., Skene, N. G., Snaedal, J., Stordal, E., Ulstein, I. D., Wang, Y., White, L. R., Hardy, J., Hjerling-Leffler, J., Sullivan, P. F., van der Flier, W. M., Dobson, R., Davis, L. K., Stefansson, H., Stefansson, K., Pedersen, N. L., Ripke, S., Andreassen, O. A., and Posthuma, D.
- Abstract
Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
- Published
- 2019
- Full Text
- View/download PDF
11. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence
- Author
-
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., De Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., Young, H., Zabaneh, D., Hägg, S., Hansell, N. K., Karlsson, Ida K., Linnarsson, S., Montgomery, G. W., Muñoz-Manchado, A. B., Quinlan, E. B., Schumann, G., Skene, N. G., Webb, B. T., White, T., Arking, D. E., Avramopoulos, D., Bilder, R. M., Bitsios, P., Burdick, K. E., Cannon, T. D., Chiba-Falek, O., Christoforou, A., Cirulli, E. T., Congdon, E., Corvin, A., Davies, G., Deary, I. J., Derosse, P., Dickinson, D., Djurovic, S., Donohoe, G., Conley, E. D., Eriksson, J. G., Espeseth, T., Freimer, N. A., Giakoumaki, S., Giegling, I., Gill, M., Glahn, D. C., Hariri, A. R., Hatzimanolis, A., Keller, M. C., Knowles, E., Koltai, D., Konte, B., Lahti, J., Le Hellard, S., Lencz, T., Liewald, D. C., London, E., Lundervold, A. J., Malhotra, A. K., Melle, I., Morris, D., Need, A. C., Ollier, W., Palotie, A., Payton, A., Pendleton, N., Poldrack, R. A., Räikkönen, K., Reinvang, I., Roussos, P., Rujescu, D., Sabb, F. W., Scult, M. A., Smeland, O. B., Smyrnis, N., Starr, J. M., Steen, V. M., Stefanis, N. C., Straub, R. E., Sundet, K., Tiemeier, H., Voineskos, A. N., Weinberger, D. R., Widen, E., Yu, J., Abecasis, G., Andreassen, O. A., Breen, G., Christiansen, L., Debrabant, B., Dick, D. M., Heinz, A., Hjerling-Leffler, J., Ikram, M. A., Kendler, K. S., Martin, N. G., Medland, S. E., Pedersen, N. L., Plomin, R., Polderman, T. J. C., Ripke, S., Van Der Sluis, S., Sullivan, P. F., Vrieze, S. I., Wright, M. J., Posthuma, D., Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., De Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., Young, H., Zabaneh, D., Hägg, S., Hansell, N. K., Karlsson, Ida K., Linnarsson, S., Montgomery, G. W., Muñoz-Manchado, A. B., Quinlan, E. B., Schumann, G., Skene, N. G., Webb, B. T., White, T., Arking, D. E., Avramopoulos, D., Bilder, R. M., Bitsios, P., Burdick, K. E., Cannon, T. D., Chiba-Falek, O., Christoforou, A., Cirulli, E. T., Congdon, E., Corvin, A., Davies, G., Deary, I. J., Derosse, P., Dickinson, D., Djurovic, S., Donohoe, G., Conley, E. D., Eriksson, J. G., Espeseth, T., Freimer, N. A., Giakoumaki, S., Giegling, I., Gill, M., Glahn, D. C., Hariri, A. R., Hatzimanolis, A., Keller, M. C., Knowles, E., Koltai, D., Konte, B., Lahti, J., Le Hellard, S., Lencz, T., Liewald, D. C., London, E., Lundervold, A. J., Malhotra, A. K., Melle, I., Morris, D., Need, A. C., Ollier, W., Palotie, A., Payton, A., Pendleton, N., Poldrack, R. A., Räikkönen, K., Reinvang, I., Roussos, P., Rujescu, D., Sabb, F. W., Scult, M. A., Smeland, O. B., Smyrnis, N., Starr, J. M., Steen, V. M., Stefanis, N. C., Straub, R. E., Sundet, K., Tiemeier, H., Voineskos, A. N., Weinberger, D. R., Widen, E., Yu, J., Abecasis, G., Andreassen, O. A., Breen, G., Christiansen, L., Debrabant, B., Dick, D. M., Heinz, A., Hjerling-Leffler, J., Ikram, M. A., Kendler, K. S., Martin, N. G., Medland, S. E., Pedersen, N. L., Plomin, R., Polderman, T. J. C., Ripke, S., Van Der Sluis, S., Sullivan, P. F., Vrieze, S. I., Wright, M. J., and Posthuma, D.
- Abstract
Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.
- Published
- 2018
- Full Text
- View/download PDF
12. Checks and balances, Adams and Wilson.
- Author
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Skene, N.
- Subjects
- *
POLITICAL science ,UNITED States politics & government - Abstract
Opinion. Notes that our government was set up with a system of checks and balances to disperse power and prevent the rise of tyranny, and suggests that the people are the ultimate `check and balance' of this country. The re-emergence of a strong central figure in the president; Woodrow Wilson's `Congressional Government,' a book lamenting the diffusion of power and responsibility to the point that no individual could be held responsible; Details.
- Published
- 1992
13. Conservative cycles on the court.
- Author
-
Skene, N.
- Subjects
- UNITED States. Supreme Court
- Abstract
Opinion. Presents a history of the Supreme Court which shows how the conservative nature of the court's decisions travels back to Federalist Chief Justice John Marshall's Court in 1836. The court's conservative tendencies; An aberration of the court's political history during the liberal period under Chief Justice Earl Warren (1953-69); The Warren Court's sympathy for the underdog and instigation of social change; The enduring life of judicial philosophy; More.
- Published
- 1992
14. `Advice and consent': Atrophying power.
- Author
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Skene, N.
- Subjects
- UNITED States. Congress. Senate
- Abstract
Opinion. Discusses the Senate's power to reject presidential nominees as stated in the Constitution, and the lack of political influence the present Senate has in the Bush court of power. Lack of cohesive bonding; George Bush's flawless veto record and 31,369 nominations; Pending nominations of Robert M. Gates for director of central intelligence, Robert L. Clarke for comptroller of currency, and Clarence Thomas to the Supreme Court.
- Published
- 1991
15. Legislative choices will guide nation.
- Author
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Skene, N.
- Subjects
- *
NINETEEN nineties , *POLITICAL questions & judicial power , *FORECASTING ,UNITED States politics & government, 1989- - Abstract
Opinion. Argues that United States society's course in the 1990s will depend far more on the choices made in Congress and the states than on the conservative evolution of the Supreme Court. Justice Potter Stewart's view in 1965; Court should interpret the law, not make it; Nomination of Clarence Thomas to succeed Justice Thurgood Marshall; Debate about judicial activism; Justice C. McReynolds in 1923; Example of abortion issue.
- Published
- 1991
16. The Miranda ruling: Equality of justice.
- Author
-
Skene, N.
- Subjects
- *
CIVIL rights - Abstract
Opinion. Considers the Miranda ruling, created on June 13, 1966, under which a suspect in custody must be warned that he has the right to remain silent, among other rights. Staple of daily police work; Chief Justice Earl Warren's ruling; A case about rich and poor, not a matter of protecting criminals; Brought about by the arrest of Ernesto Miranda, a poor ninth-grade dropout; The Constitution's Fifth Amendment; `Miranda v. Arizona'; Oral argument in Miranda's case; Details.
- Published
- 1991
17. The Supreme Court's passive policy role.
- Author
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Skene, N.
- Subjects
- UNITED States. Supreme Court
- Abstract
Opinion. Discusses the new, more passive role that the Supreme Court has established for itself, and for the Constitution, in national policy. Supreme Court ultimately declares what the law is; Applied most forcefully during three areas in American history; Under leadership of Justice Antonin Scalia, entered a new era of deference and restraint; Policy-making left to states, Congress, and the president; Examples of the court's deference.
- Published
- 1991
18. Court conservatives bow to precedent.
- Author
-
Skene, N.
- Subjects
- KENNEDY, Anthony M., 1936-
- Abstract
Examines Anthony M. Kennedy's opinion to expand the ruling of `Miranda v. Arizona,' which includes the right to remain silent and the right to an attorney. Kennedy and `Minnick v. Mississippi'; Sandra Day O'Conner's involvement; Conservatives' deferential attitude toward Congress.
- Published
- 1990
19. The politics of silence of court nominees.
- Author
-
Skene, N.
- Subjects
- *
JUDGES - Abstract
Opinion. Refutes the myth that those seeking judgeships should not declare their views on issues likely to come before them on the court. Asserts that a candidate who keeps his views secret has done nothing to neutralize the role those views will play in his judicial decisions. Agrees that candidates should not make commitments to the people who select them. Examples of David H. Souter and Sandra Day O'Conner.
- Published
- 1990
20. Will high court retreat on reapportionment?
- Author
-
Skene, N.
- Subjects
- *
APPORTIONMENT (Election law) - Abstract
Opinion. Discusses reapportionment and the effect that `Baker v. Carr' had on it. Example of William LeRoy Collins, elected governor of Florida in 1954; Earl Warren's opinion of `Baker v. Carr'; Reviewing apportionment of state legislatures under the 14th Amendment's `equal protection' standard; Today's Supreme Court reflecting judicial conservatism; More.
- Published
- 1990
21. Fame often not the rule for court nominees.
- Author
-
Skene, N.
- Subjects
- UNITED States. Supreme Court
- Abstract
Opinion. Asserts that through 200 years of US history, each new vacancy on the Supreme Court has presented a unique combination of political considerations for a president and the Senate. Presidents have selected nominees with philosophies quite different from their own; Senate has rejected nominees solely because of their views on a single political issue; Felt necessities of politics play a big role in Supreme Court nominations; About past nominees.
- Published
- 1990
22. Court not in lock step with Reagan march.
- Author
-
Skene, N.
- Subjects
- UNITED States. Supreme Court
- Abstract
Opinion. Asserts that it would be a mistake to think that the Supreme Court's last term was boring, when considered against the backdrop of the court of the 1790s. Enduring embodiment of the Reagan Revolution; `Webster v. Reproductive Health Services; Conservative revolution; Sky falling on the liberals; Principles of separation of powers; One school of conservative legal thought; Constitutional `right to die'; Liberal advance on social issues; More.
- Published
- 1990
23. Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset.
- Author
-
Murphy AE, Fancy N, and Skene N
- Subjects
- Humans, Single-Cell Gene Expression Analysis, Quality Control, RNA, Small Nuclear, RNA-Seq, Alzheimer Disease genetics
- Abstract
Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer's disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes., Competing Interests: AM, NF, NS No competing interests declared, (© 2023, Murphy et al.)
- Published
- 2023
- Full Text
- View/download PDF
24. Artificial intelligence for dementia genetics and omics.
- Author
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Bettencourt C, Skene N, Bandres-Ciga S, Anderson E, Winchester LM, Foote IF, Schwartzentruber J, Botia JA, Nalls M, Singleton A, Schilder BM, Humphrey J, Marzi SJ, Toomey CE, Kleifat AA, Harshfield EL, Garfield V, Sandor C, Keat S, Tamburin S, Frigerio CS, Lourida I, Ranson JM, and Llewellyn DJ
- Subjects
- Humans, Machine Learning, Phenotype, Precision Medicine, Artificial Intelligence, Alzheimer Disease genetics
- Abstract
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
- Published
- 2023
- Full Text
- View/download PDF
25. miR-483-5p offsets functional and behavioural effects of stress in male mice through synapse-targeted repression of Pgap2 in the basolateral amygdala.
- Author
-
Mucha M, Skrzypiec AE, Kolenchery JB, Brambilla V, Patel S, Labrador-Ramos A, Kudla L, Murrall K, Skene N, Dymicka-Piekarska V, Klejman A, Przewlocki R, Mosienko V, and Pawlak R
- Subjects
- Animals, Male, Mice, Amygdala metabolism, Neurons metabolism, Synapses metabolism, Basolateral Nuclear Complex metabolism, MicroRNAs genetics, MicroRNAs metabolism
- Abstract
Severe psychological trauma triggers genetic, biochemical and morphological changes in amygdala neurons, which underpin the development of stress-induced behavioural abnormalities, such as high levels of anxiety. miRNAs are small, non-coding RNA fragments that orchestrate complex neuronal responses by simultaneous transcriptional/translational repression of multiple target genes. Here we show that miR-483-5p in the amygdala of male mice counterbalances the structural, functional and behavioural consequences of stress to promote a reduction in anxiety-like behaviour. Upon stress, miR-483-5p is upregulated in the synaptic compartment of amygdala neurons and directly represses three stress-associated genes: Pgap2, Gpx3 and Macf1. Upregulation of miR-483-5p leads to selective contraction of distal parts of the dendritic arbour and conversion of immature filopodia into mature, mushroom-like dendritic spines. Consistent with its role in reducing the stress response, upregulation of miR-483-5p in the basolateral amygdala produces a reduction in anxiety-like behaviour. Stress-induced neuromorphological and behavioural effects of miR-483-5p can be recapitulated by shRNA mediated suppression of Pgap2 and prevented by simultaneous overexpression of miR-483-5p-resistant Pgap2. Our results demonstrate that miR-483-5p is sufficient to confer a reduction in anxiety-like behaviour and point to miR-483-5p-mediated repression of Pgap2 as a critical cellular event offsetting the functional and behavioural consequences of psychological stress., (© 2023. The Author(s).)
- Published
- 2023
- Full Text
- View/download PDF
26. Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes.
- Author
-
Wahedi A, Soondram C, Murphy AE, Skene N, and Rahman S
- Subjects
- Humans, Transcriptome, Mutation, Brain metabolism, Magnetic Resonance Imaging, Leigh Disease genetics
- Abstract
Leigh syndrome is a rare, inherited, complex neurometabolic disorder with genetic and clinical heterogeneity. Features present in affected patients range from classical stepwise developmental regression to ataxia, seizures, tremor, and occasionally psychiatric manifestations. Currently, more than 100 monogenic causes of Leigh syndrome have been identified, yet the pathophysiology remains unknown. Here, we sought to determine the cellular specificity within the brain of all genes currently associated with Leigh syndrome. Further, we aimed to investigate potential genetic commonalities between Leigh syndrome and other disorders with overlapping clinical features. Enrichment of our target genes within the brain was evaluated with co-expression (CoExp) network analyses constructed using existing UK Brain Expression Consortium data. To determine the cellular specificity of the Leigh associated genes, we employed expression weighted cell type enrichment (EWCE) analysis of single-cell RNA-Seq data. Finally, CoExp network modules demonstrating enrichment of Leigh syndrome associated genes were then utilised for synaptic gene ontology analysis and heritability analysis. CoExp network analyses revealed that Leigh syndrome associated genes exhibit the highest levels of expression in brain regions most affected on MRI in affected patients. EWCE revealed significant enrichment of target genes in hippocampal and somatosensory pyramidal neurons and interneurons of the brain. Analysis of CoExp modules enriched with our target genes revealed preferential association with pre-synaptic structures. Heritability studies suggested some common enrichment between Leigh syndrome and Parkinson disease and epilepsy. Our findings suggest a primary mitochondrial dysfunction as the underlying basis of Leigh syndrome, with associated genes primarily expressed in neuronal cells., (© 2022 The Authors. Journal of Inherited Metabolic Disease published by John Wiley & Sons Ltd on behalf of SSIEM.)
- Published
- 2023
- Full Text
- View/download PDF
27. Harnessing the potential of machine learning and artificial intelligence for dementia research.
- Author
-
Ranson JM, Bucholc M, Lyall D, Newby D, Winchester L, Oxtoby NP, Veldsman M, Rittman T, Marzi S, Skene N, Al Khleifat A, Foote IF, Orgeta V, Kormilitzin A, Lourida I, and Llewellyn DJ
- Abstract
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine., (© 2023. The Author(s).)
- Published
- 2023
- Full Text
- View/download PDF
28. Publisher Correction: Altered perivascular fibroblast activity precedes ALS disease onset.
- Author
-
Månberg A, Skene N, Sanders F, Trusohamn M, Remnestål J, Szczepińska A, Aksoylu IS, Lönnerberg P, Ebarasi L, Wouters S, Lehmann M, Olofsson J, von Gohren Antequera I, Domaniku A, De Schaepdryver M, De Vocht J, Poesen K, Uhlén M, Anink J, Mijnsbergen C, Vergunst-Bosch H, Hübers A, Kläppe U, Rodriguez-Vieitez E, Gilthorpe JD, Hedlund E, Harris RA, Aronica E, Van Damme P, Ludolph A, Veldink J, Ingre C, Nilsson P, and Lewandowski SA
- Published
- 2021
- Full Text
- View/download PDF
29. Altered perivascular fibroblast activity precedes ALS disease onset.
- Author
-
Månberg A, Skene N, Sanders F, Trusohamn M, Remnestål J, Szczepińska A, Aksoylu IS, Lönnerberg P, Ebarasi L, Wouters S, Lehmann M, Olofsson J, von Gohren Antequera I, Domaniku A, De Schaepdryver M, De Vocht J, Poesen K, Uhlén M, Anink J, Mijnsbergen C, Vergunst-Bosch H, Hübers A, Kläppe U, Rodriguez-Vieitez E, Gilthorpe JD, Hedlund E, Harris RA, Aronica E, Van Damme P, Ludolph A, Veldink J, Ingre C, Nilsson P, and Lewandowski SA
- Subjects
- Amyotrophic Lateral Sclerosis blood, Amyotrophic Lateral Sclerosis genetics, Amyotrophic Lateral Sclerosis physiopathology, Animals, Biomarkers metabolism, Collagen Type VI genetics, Collagen Type VI metabolism, DNA-Binding Proteins metabolism, Disease Progression, Genetic Markers, Humans, Mice, Transgenic, Osteopontin blood, Prognosis, RNA, Messenger genetics, RNA, Messenger metabolism, Spinal Cord pathology, Spinal Cord ultrastructure, Superoxide Dismutase genetics, Transcription, Genetic, Vascular Remodeling, Amyotrophic Lateral Sclerosis pathology, Blood Vessels pathology, Fibroblasts pathology
- Abstract
Apart from well-defined factors in neuronal cells
1 , only a few reports consider that the variability of sporadic amyotrophic lateral sclerosis (ALS) progression can depend on less-defined contributions from glia2,3 and blood vessels4 . In this study we use an expression-weighted cell-type enrichment method to infer cell activity in spinal cord samples from patients with sporadic ALS and mouse models of this disease. Here we report that patients with sporadic ALS present cell activity patterns consistent with two mouse models in which enrichments of vascular cell genes preceded microglial response. Notably, during the presymptomatic stage, perivascular fibroblast cells showed the strongest gene enrichments, and their marker proteins SPP1 and COL6A1 accumulated in enlarged perivascular spaces in patients with sporadic ALS. Moreover, in plasma of 574 patients with ALS from four independent cohorts, increased levels of SPP1 at disease diagnosis repeatedly predicted shorter survival with stronger effect than the established risk factors of bulbar onset or neurofilament levels in cerebrospinal fluid. We propose that the activity of the recently discovered perivascular fibroblast can predict survival of patients with ALS and provide a new conceptual framework to re-evaluate definitions of ALS etiology.- Published
- 2021
- Full Text
- View/download PDF
30. Probabilistic cell typing enables fine mapping of closely related cell types in situ.
- Author
-
Qian X, Harris KD, Hauling T, Nicoloutsopoulos D, Muñoz-Manchado AB, Skene N, Hjerling-Leffler J, and Nilsson M
- Subjects
- Algorithms, Animals, CA1 Region, Hippocampal metabolism, Male, Mice, Models, Statistical, Neocortex metabolism, Neurons metabolism, Pyramidal Cells metabolism, CA1 Region, Hippocampal cytology, Gene Expression Profiling methods, Neocortex cytology, Neurons cytology, Pyramidal Cells cytology, Sequence Analysis, RNA methods, Single-Cell Analysis methods
- Abstract
Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.
- Published
- 2020
- Full Text
- View/download PDF
31. Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways.
- Author
-
Jansen PR, Watanabe K, Stringer S, Skene N, Bryois J, Hammerschlag AR, de Leeuw CA, Benjamins JS, Muñoz-Manchado AB, Nagel M, Savage JE, Tiemeier H, White T, Tung JY, Hinds DA, Vacic V, Wang X, Sullivan PF, van der Sluis S, Polderman TJC, Smit AB, Hjerling-Leffler J, Van Someren EJW, and Posthuma D
- Subjects
- Chromatin genetics, Female, Genome-Wide Association Study methods, Humans, Male, Middle Aged, Phenotype, Polymorphism, Single Nucleotide genetics, Sleep genetics, Genetic Predisposition to Disease genetics, Quantitative Trait Loci genetics, Sleep Initiation and Maintenance Disorders genetics
- Abstract
Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.
- Published
- 2019
- Full Text
- View/download PDF
32. Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals.
- Author
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Coleman JRI, Bryois J, Gaspar HA, Jansen PR, Savage JE, Skene N, Plomin R, Muñoz-Manchado AB, Linnarsson S, Crawford G, Hjerling-Leffler J, Sullivan PF, Posthuma D, and Breen G
- Subjects
- Brain metabolism, Cognition physiology, Cohort Studies, Data Analysis, Female, Frontal Lobe metabolism, Gene Expression genetics, Genetic Loci genetics, Genetic Predisposition to Disease genetics, Genome-Wide Association Study methods, Humans, Male, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide genetics, Pyramidal Cells physiology, Temporal Lobe metabolism, Intelligence genetics, Intelligence physiology
- Abstract
Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with a study comparing 1247 individuals with mean IQ ~170 to 8185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.
- Published
- 2019
- Full Text
- View/download PDF
33. Molecular Architecture of the Mouse Nervous System.
- Author
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Zeisel A, Hochgerner H, Lönnerberg P, Johnsson A, Memic F, van der Zwan J, Häring M, Braun E, Borm LE, La Manno G, Codeluppi S, Furlan A, Lee K, Skene N, Harris KD, Hjerling-Leffler J, Arenas E, Ernfors P, Marklund U, and Linnarsson S
- Subjects
- Animals, Female, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Male, Mice, Mice, Inbred C57BL, Nervous System growth & development, Gene Expression Regulation, Developmental, Gene Regulatory Networks, Nervous System metabolism, Single-Cell Analysis methods, Transcriptome
- Abstract
The mammalian nervous system executes complex behaviors controlled by specialized, precisely positioned, and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse and were grouped by developmental anatomical units and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission, and membrane conductance. We discovered seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system and enables genetic manipulation of specific cell types., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2018
- Full Text
- View/download PDF
34. Proteomic analysis of postsynaptic proteins in regions of the human neocortex.
- Author
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Roy M, Sorokina O, Skene N, Simonnet C, Mazzo F, Zwart R, Sher E, Smith C, Armstrong JD, and Grant SGN
- Subjects
- Animals, Computational Biology, Female, Humans, Image Processing, Computer-Assisted, Male, Membrane Potentials genetics, Microinjections, Neocortex diagnostic imaging, Nerve Tissue Proteins genetics, Oocytes, Oxygen blood, Patch-Clamp Techniques, Positron-Emission Tomography, Proteomics, Stroke pathology, Synapses ultrastructure, Xenopus laevis, gamma-Aminobutyric Acid pharmacology, Neocortex pathology, Nerve Tissue Proteins metabolism, Synapses metabolism, Synaptosomes metabolism
- Abstract
The postsynaptic proteome of excitatory synapses comprises ~1,000 highly conserved proteins that control the behavioral repertoire, and mutations disrupting their function cause >130 brain diseases. Here, we document the composition of postsynaptic proteomes in human neocortical regions and integrate it with genetic, functional and structural magnetic resonance imaging, positron emission tomography imaging, and behavioral data. Neocortical regions show signatures of expression of individual proteins, protein complexes, biochemical and metabolic pathways. We characterized the compositional signatures in brain regions involved with language, emotion and memory functions. Integrating large-scale GWAS with regional proteome data identifies the same cortical region for smoking behavior as found with fMRI data. The neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior, and to study the role of postsynaptic proteins in localization of brain functions.
- Published
- 2018
- Full Text
- View/download PDF
35. Stress-induced lipocalin-2 controls dendritic spine formation and neuronal activity in the amygdala.
- Author
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Skrzypiec AE, Shah RS, Schiavon E, Baker E, Skene N, Pawlak R, and Mucha M
- Subjects
- Action Potentials genetics, Alternative Splicing, Animals, Cluster Analysis, Gene Expression Profiling, Gene Expression Regulation, Lipocalins genetics, Male, Mice, Mice, Knockout, Transcription, Genetic, Amygdala cytology, Amygdala metabolism, Dendritic Spines, Lipocalins metabolism, Neurons metabolism, Stress, Psychological genetics
- Abstract
Behavioural adaptation to psychological stress is dependent on neuronal plasticity and dysfunction at this cellular level may underlie the pathogenesis of affective disorders such as depression and post-traumatic stress disorder. Taking advantage of genome-wide microarray assay, we performed detailed studies of stress-affected transcripts in the amygdala - an area which forms part of the innate fear circuit in mammals. Having previously demonstrated the role of lipocalin-2 (Lcn-2) in promoting stress-induced changes in dendritic spine morphology/function and neuronal excitability in the mouse hippocampus, we show here that the Lcn-2 gene is one of the most highly upregulated transcripts detected by microarray analysis in the amygdala after acute restraint-induced psychological stress. This is associated with increased Lcn-2 protein synthesis, which is found on immunohistochemistry to be predominantly localised to neurons. Stress-naïve Lcn-2(-/-) mice show a higher spine density in the basolateral amygdala and a 2-fold higher rate of neuronal firing rate compared to wild-type mice. Unlike their wild-type counterparts, Lcn-2(-/-) mice did not show an increase in dendritic spine density in response to stress but did show a distinct pattern of spine morphology. Thus, amygdala-specific neuronal responses to Lcn-2 may represent a mechanism for behavioural adaptation to psychological stress.
- Published
- 2013
- Full Text
- View/download PDF
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