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  • Schizophrenia
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  • 1
    In: Ripke, Stephan; Neale, Benjamin M. Corvin, Aiden; Walters, James T. R.; Farh, Kai-How; Holmans, Peter A.; Lee, Phil; Bulik-Sullivan, Brendan; Collier, David A.; Huang, Hailiang; Pers, Tune H.; Agartz, Ingrid; Agerbo, Esben; Albus, Margot; Alexander, Madeline; Amin, Farooq; Bacanu, Silviu A.; Begemann, Martin; Belliveau Jr, Richard A.; Bene, Judit; Bergen, Sarah E.; Bevilacqua, Elizabeth; Bigdeli, Tim B.; Black, Donald W.; Bruggeman, Richard; Buccola, Nancy G.; Buckner, Randy L.; Byerley, William; Cahn, Wiepke; Cai, Guiqing; Campion, Dominique; Cantor, Rita M.; Carr, Vaughan J.; Carrera, Noa; Catts, Stanley V.; Chambert, Kimberly D.; Chan, Raymond C. K.; Chen, Ronald Y. L.; Chen, Eric Y. H.; Cheng, Wei; Cheung, Eric F. C.; Ann Chong, Siow; Robert Cloninger, C.; Cohen, David; Cohen, Nadine; Cormican, Paul; Craddock, Nick; Crowley, James J.; Curtis, David; Davidson, Michael; Davis, Kenneth L.; Degenhardt, Franziska; Del Favero, Jurgen; Demontis, Ditte; Dikeos, Dimitris; Dinan, Timothy; Djurovic, Srdjan; Donohoe, Gary; Drapeau, Elodie; Duan, Jubao; Dudbridge, Frank; Durmishi, Naser; Eichhammer, Peter; Eriksson, Johan; Escott-Price, Valentina; Essioux, Laurent; Fanous, Ayman H.; Farrell, Martilias S.; Frank, Josef; Franke, Lude; Freedman, Robert; Freimer, Nelson B.; Friedl, Marion; Friedman, Joseph I.; Fromer, Menachem; Genovese, Giulio; Georgieva, Lyudmila; Giegling, Ina; Giusti-Rodríguez, Paola; Godard, Stephanie; Goldstein, Jacqueline I.; Golimbet, Vera; Gopal, Srihari; Gratten, Jacob; de Haan, Lieuwe; Hammer, Christian; Hamshere, Marian L.; Hansen, Mark; Hansen, Thomas; Haroutunian, Vahram; Hartmann, Annette M.; Henskens, Frans A.; Herms, Stefan; Hirschhorn, Joel N.; Hoffmann, Per; Hofman, Andrea; Hollegaard, Mads V.; Hougaard, David M.; Ikeda, Masashi; Joa, Inge; Julià, Antonio; Kahn, René S.; Kalaydjieva, Luba; Karachanak-Yankova, Sena; Karjalainen, Juha; Kavanagh, David; Keller, Matthew C.; Kennedy, James L.; Khrunin, Andrey; Kim, Yunjung; Klovins, Janis; Knowles, James A.; Konte, Bettina; Kucinskas, Vaidutis; Ausrele Kucinskiene, Zita; Kuzelova-Ptackova, Hana; Kähler, Anna K.; Laurent, Claudine; Lee Chee Keong, Jimmy; Hong Lee, S.; Legge, Sophie E.; Lerer, Bernard; Li, Miaoxin; Li, Tao; Liang, Kung-Yee; Lieberman, Jeffrey; Limborska, Svetlana; Loughland, Carmel M.; Lubinski, Jan; Lönnqvist, Jouko; Macek Jr, Milan; Magnusson, Patrik K. E.; Maher, Brion S.; Maier, Wolfgang; Mallet, Jacques; Marsal, Sara; Mattheisen, Manuel; Mattingsdal, Morten; McCarley, Robert W.; McDonald, Colm; McIntosh, Andrew M.; Meier, Sandra; Meijer, Carin J.; Melegh, Bela; Melle, Ingrid; Mesholam-Gately, Raquelle I.; Metspalu, Andres; Michie, Patricia T.; Milani, Lili; Milanova, Vihra; Mokrab, Younes; Morris, Derek W.; Mors, Ole; Murphy, Kieran C.; Murray, Robin M.; Myin-Germeys, Inez; Müller-Myhsok, Bertram; Nelis, Mari; Nenadic, Igor; Nertney, Deborah A.; Nestadt, Gerald; Nicodemus, Kristin K.; Nikitina-Zake, Liene; Nisenbaum, Laura; Nordin, Annelie; O’Callaghan, Eadbhard; O’Dushlaine, Colm; O’Neill, F. Anthony; Oh, Sang-Yun; Olincy, Ann; Olsen, Line; Van Os, Jim; Endophenotypes International Consortium, Psychosis; Pantelis, Christos; Papadimitriou, George N.; Papiol, Sergi; Parkhomenko, Elena; Pato, Michele T.; Paunio, Tiina; Pejovic-Milovancevic, Milica; Perkins, Diana O.; Pietiläinen, Olli; Pimm, Jonathan; Pocklington, Andrew J.; Powell, John; Price, Alkes; Pulver, Ann E.; Purcell, Shaun M.; Quested, Digby; Rasmussen, Henrik B.; Reichenberg, Abraham; Reimers, Mark A.; Richards, Alexander L.; Roffman, Joshua L.; Roussos, Panos; Ruderfer, Douglas M.; Salomaa, Veikko; Sanders, Alan R.; Schall, Ulrich; Schubert, Christian R.; Schulze, Thomas G.; Schwab, Sibylle G.; Scolnick, Edward M.; Scott, Rodney J.; Seidman, Larry J.; Shi, Jianxin; Sigurdsson, Engilbert; Silagadze, Teimuraz; Silverman, Jeremy M.; Sim, Kang; Slominsky, Petr; Smoller, Jordan W.; So, Hon-Cheong; Spencer, ChrisC. A.; Stahl, Eli A.; Stefansson, Hreinn; Steinberg, Stacy; Stogmann, Elisabeth; Straub, Richard E.; Strengman, Eric; Strohmaier, Jana; Scott Stroup, T.; Subramaniam, Mythily; Suvisaari, Jaana; Svrakic, Dragan M.; Szatkiewicz, Jin P.; Söderman, Erik; Thirumalai, Srinivas; Toncheva, Draga; Tosato, Sarah; Veijola, Juha; Waddington, John; Walsh, Dermot; Wang, Dai; Wang, Qiang; Webb, Bradley T.; Weiser, Mark; Wildenauer, Dieter B.; Williams, Nigel M.; Williams, Stephanie; Witt, Stephanie H.; Wolen, Aaron R.; Wong, Emily H. M.; Wormley, Brandon K.; Simon Xi, Hualin; Zai, Clement C.; Zheng, Xuebin; Zimprich, Fritz; Wray, Naomi R.; Stefansson, Kari; Visscher, Peter M.; Trust Case-Control Consortium, Wellcome; Adolfsson, Rolf; Andreassen, Ole A.; Blackwood, Douglas H. R.; Bramon, Elvira; Buxbaum, Joseph D.; Børglum, Anders D.; Cichon, Sven; Darvasi, Ariel; Domenici, Enrico; Ehrenreich, Hannelore; Esko, Tõnu; Gejman, Pablo V.; Gill, Michael; Gurling, Hugh; Hultman, Christina M.; Iwata, Nakao; Jablensky, Assen V.; Jönsson, Erik G.; Kendler, Kenneth S.; Kirov, George; Knight, Jo; Lencz, Todd; Levinson, Douglas F.; Li, Qingqin S.; Liu, Jianjun; Malhotra, Anil K.; McCarroll, Steven A.; McQuillin, Andrew; Moran, Jennifer L.; Mortensen, Preben B.; Mowry, Bryan J.; Nöthen, Markus M.; Ophoff, Roel A.; Owen, Michael J.; Palotie, Aarno; Pato, Carlos N.; Petryshen, Tracey L.; Posthuma, Danielle; Rietschel, Marcella; Riley, Brien P.; Rujescu, Dan; Sham, Pak C.; Sklar, Pamela; St Clair, David; Weinberger, Daniel R.; Wendland, Jens R.; Werge, Thomas; Daly, Mark J.; Sullivan, Patrick F.; O’Donovan, Michael C. (2014). Biological insights from 108 schizophrenia-associated genetic loci. Nature 511 (7510),
    Description: Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.
    Keywords: Genome-Wide Association ; Common Variants ; Bipolar Disorder ; Conferring Risk ; Disease ; Metaanalysis ; Mutations ; Drugs
    ISSN: 00280836
    E-ISSN: 14764687
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  • 2
    Language: English
    In: American Journal of Psychiatry, March, 2011, Vol.168(3), p.302(15)
    Description: Objective: To evaluate previously reported associations of copy number variants (CNVs) with schizophrenia and to identify additional associations, the authors analyzed CNVs in the Molecular Genetics of Schizophrenia study (MGS) and additional available data. Method: After quality control, MGS data for 3,945 subjects with schizophrenia or schizoaffective disorder and 3,611 screened comparison subjects were available for analysis of rare CNVs (〈1% frequency). CNV detection thresholds were chosen that maximized concordance in 151 duplicate assays. Pointwise and genewise analyses were carried out, as well as analyses of previously reported regions. Selected regions were visually inspected and confirmed with quantitative polymerase chain reaction. Results: In analyses of MGS data combined with other available data sets, odds ratios of 7.5 or greater were observed for previously reported deletions in chromosomes 1q21.1, 15q13.3, and 22q11.21, duplications in 16p11.2, and exon-disrupting deletions in NRXN1. The most consistently supported candidate associations across data sets included a 1.6-Mb deletion in chromosome 3q29 (21 genes, TFRC to BDHI) that was previously described in a mild-moderate mental retardation syndrome, exonic duplications in the gene for vasoactive intestinal peptide receptor 2 (V1PR2), and exonic duplications in CI 6orf72. The case subjects had a modestly higher genome-wide number of gene-containing deletions (〉100 kb and 〉1 Mb) but not duplications. Conclusions: The data strongly confirm the association of schizophrenia with 1q21.1, 15q13.3, and 22q11.21 deletions, 16p11.2 duplications, and exonic NRXN1 deletions. These CNVs, as well as 3q29 deletions, are also associated with mental retardation, autism spectrum disorders, and epilepsy. Additional candidate genes and regions, including VIPR2, were identified. Study of the mechanisms underlying these associations should shed light on the pathophysiology of schizophrenia.
    Keywords: Schizophrenia -- Development And Progression ; Schizophrenia -- Research ; Schizophrenia -- Genetic Aspects
    ISSN: 0002-953X
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  • 3
    Language: English
    In: Nature, 2016, Vol.530(7589), pp.177-+
    Description: Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia's strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia
    Keywords: Article;
    ISSN: 0028-0836
    ISSN: 1476-4687
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  • 4
    In: Nature, 2009, Vol.460(7256), p.753
    Description: Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission. 1 Recent studies implicate rare, large, high-penetrance copy number variants (CNVs) in some cases 2 , but it is not known what genes or biological mechanisms underlie susceptibility. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended Major Histocompatibility Complex (MHC) region on chromosome 6. We carried out a genome-wide association study (GWAS) of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium (ISC) and SGENE datasets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 ( P = 9.54 × 10 −9 ). This region includes a histone gene cluster and several immunity-related genes, possibly implicating etiologic mechanisms involving chromatin modification, transcriptional regulation, auto-immunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.
    Keywords: Article;
    ISSN: 0028-0836
    E-ISSN: 14764687
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  • 5
    In: International Journal of Epidemiology, 2015, Vol. 44(5), pp.1706-1721
    Description: Background: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. Methods: We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. Results: We estimate a small but significant negative SNP-genetic correlation between SZ and RA (−0.046, s.e. 0.026, P  = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (−0.174, s.e. 0.071, P  = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P  = 0.00090). Conclusions: Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.
    Keywords: Schizophrenia ; Rheumatoid Arthritis ; Genetic Relationship ; Pleiotropy
    ISSN: 0300-5771
    E-ISSN: 1464-3685
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  • 6
    Language: English
    In: The American Journal of Human Genetics, 07 June 2018, Vol.102(6), pp.1185-1194
    Description: Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.
    Keywords: Linkage Disequilibrium Score Regression ; Genomic Restricted Maximum Likelihood ; Genetic Correlation ; Schizophrenia ; Body Mass Index ; Height ; Snp Heritability ; Accuracy ; Biasedness ; Genome-Wide Snps ; Biology
    ISSN: 0002-9297
    E-ISSN: 1537-6605
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  • 7
    Language: English
    In: The American Journal of Human Genetics, 01 October 2015, Vol.97(4), pp.576-592
    Description: Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.
    Keywords: Biology
    ISSN: 0002-9297
    E-ISSN: 1537-6605
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  • 8
    In: Marshall, Christian R; Howrigan, Daniel P; Merico, Daniele; Thiruvahindrapuram, Bhooma; Wu, Wenting; Greer, Douglas S; Antaki, Danny; Shetty, Aniket; Holmans, Peter A; Pinto, Dalila; Gujral, Madhusudan; Brandler, William M; Malhotra, Dheeraj; Wang, Zhouzhi; Fajarado, Karin V Fuentes; Maile, Michelle S; Ripke, Stephan; Agartz, Ingrid; Albus, Margot; Alexander, Madeline; Amin, Farooq; Atkins, Joshua; Bacanu, Silviu A; Belliveau, Richard A; Bergen, Sarah E; Bertalan, Marcelo; Bevilacqua, Elizabeth; Bigdeli, Tim B; Black, Donald W; Bruggeman, Richard; Buccola, Nancy G; Buckner, Randy L; Bulik-Sullivan, Brendan; Byerley, William; Cahn, Wiepke; Cai, Guiqing; Cairns, Murray J; Campion, Dominique; Cantor, Rita M; Carr, Vaughan J; Carrera, Noa; Catts, Stanley V; Chambert, Kimberley D; Cheng, Wei; Cloninger, C Robert; Cohen, David; Cormican, Paul; Craddock, Nick; Crespo-Facorro, Benedicto; Crowley, James J; Curtis, David; Davidson, Michael; Davis, Kenneth L; Degenhardt, Franziska; Del Favero, Jurgen; DeLisi, Lynn E; Dikeos, Dimitris; Dinan, Timothy; Djurovic, Srdjan; Donohoe, Gary; Drapeau, Elodie; Duan, Jubao; Dudbridge, Frank; Eichhammer, Peter; Eriksson, Johan; Escott-Price, Valentina; Essioux, Laurent; Fanous, Ayman H; Farh, Kai-How; Farrell, Martilias S; Frank, Josef; Franke, Lude; Freedman, Robert; Freimer, Nelson B; Friedman, Joseph I; Forstner, Andreas J; Fromer, Menachem; Genovese, Giulio; Georgieva, Lyudmila; Gershon, Elliot S; Giegling, Ina; Giusti-Rodríguez, Paola; Godard, Stephanie; Goldstein, Jacqueline I; Gratten, Jacob; de Haan, Lieuwe; Hamshere, Marian L; Hansen, Mark; Hansen, Thomas; Haroutunian, Vahram; Hartmann, Annette M; Henskens, Frans A; Herms, Stefan; Hirschhorn, Joel N; Hoffmann, Per; Hofman, Andrea; Huang, Hailiang; Ikeda, Masashi; Joa, Inge; Kähler, Anna K; Kahn, René S; Kalaydjieva, Luba; Karjalainen, Juha; Kavanagh, David; Keller, Matthew C; Kelly, Brian J; Kennedy, James L; Kim, Yunjung; Knowles, James A; Konte, Bettina; Laurent, Claudine; Lee, Phil; Lee, S Hong; Legge, Sophie E; Lerer, Bernard; Levy, Deborah L; Liang, Kung-Yee; Lieberman, Jeffrey; Lönnqvist, Jouko; Loughland, Carmel M; Magnusson, Patrik K E; Maher, Brion S; Maier, Wolfgang; Mallet, Jacques; Mattheisen, Manuel; Mattingsdal, Morten; McCarley, Robert W; McDonald, Colm; McIntosh, Andrew M; Meier, Sandra; Meijer, Carin J; Melle, Ingrid; Mesholam-Gately, Raquelle I; Metspalu, Andres; Michie, Patricia T; Milani, Lili; Milanova, Vihra; Mokrab, Younes; Morris, Derek W; Müller-Myhsok, Bertram; Murphy, Kieran C; Murray, Robin M; Myin-Germeys, Inez; Nenadic, Igor; Nertney, Deborah A; Nestadt, Gerald; Nicodemus, Kristin K; Nisenbaum, Laura; Nordin, Annelie; O'Callaghan, Eadbhard; O'Dushlaine, Colm; Oh, Sang-Yun; Olincy, Ann; Olsen, Line; O'Neill, F Anthony; Van Os, Jim; Pantelis, Christos; Papadimitriou, George N; Parkhomenko, Elena; Pato, Michele T; Paunio, Tiina; Perkins, Diana O; Pers, Tune H; Pietiläinen, Olli; Pimm, Jonathan; Pocklington, Andrew J; Powell, John; Price, Alkes; Pulver, Ann E; Purcell, Shaun M; Quested, Digby; Rasmussen, Henrik B; Reichenberg, Abraham; Reimers, Mark A; Richards, Alexander L; Roffman, Joshua L; Roussos, Panos; Ruderfer, Douglas M; Salomaa, Veikko; Sanders, Alan R; Savitz, Adam; Schall, Ulrich; Schulze, Thomas G; Schwab, Sibylle G; Scolnick, Edward M; Scott, Rodney J; Seidman, Larry J; Shi, Jianxin; Silverman, Jeremy M; Smoller, Jordan W; Söderman, Erik; Spencer, Chris C A; Stahl, Eli A; Strengman, Eric; Strohmaier, Jana; Stroup, T Scott; Suvisaari, Jaana; Svrakic, Dragan M; Szatkiewicz, Jin P; Thirumalai, Srinivas; Tooney, Paul A; Veijola, Juha; Visscher, Peter M; Waddington, John; Walsh, Dermot; Webb, Bradley T; Weiser, Mark; Wildenauer, Dieter B; Williams, Nigel M; Williams, Stephanie; Witt, Stephanie H; Wolen, Aaron R; Wormley, Brandon K; Wray, Naomi R; Wu, Jing Qin; Zai, Clement C; Adolfsson, Rolf; Andreassen, Ole A; Blackwood, Douglas H R; Bramon, Elvira; Buxbaum, Joseph D; Cichon, Sven; Collier, David A; Corvin, Aiden; Daly, Mark J; Darvasi, Ariel; Domenici, Enrico; Esko, Tõnu; Gejman, Pablo V; Gill, Michael; Gurling, Hugh; Hultman, Christina M; Iwata, Nakao; Jablensky, Assen V; Jönsson, Erik G; Kendler, Kenneth S; Kirov, George; Knight, Jo; Levinson, Douglas F; Li, Qingqin S; McCarroll, Steven A; McQuillin, Andrew; Moran, Jennifer L; Mowry, Bryan J; Nöthen, Markus M; Ophoff, Roel A; Owen, Michael J; Palotie, Aarno; Pato, Carlos N; Petryshen, Tracey L; Posthuma, Danielle; Rietschel, Marcella; Riley, Brien P; Rujescu, Dan; Sklar, Pamela; St Clair, David; Walters, James T R; Werge, Thomas; Sullivan, Patrick F; O'Donovan, Michael C; Scherer, Stephen W; Neale, Benjamin M; Sebat, Jonathan (2016). Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nature Genetics 49 (1), 27-35
    Description: Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (odds ratio (OR) = 1.11, P = 5.7 x 10(-15)), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 x 10(-6)). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 x 10(-11)) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 x 10(-5)). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by nonallelic homologous recombination.
    Keywords: Gene ; 16p11.2 ; Autism ; Risk ; Cnvs ; Rearrangements ; Duplications ; Phenotypes ; Disorders ; Mutations
    ISSN: 10614036
    E-ISSN: 15461718
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  • 9
    Language: English
    In: The American Journal of Human Genetics, 04 December 2014, Vol.95(6), pp.744-753
    Description: Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in 〉100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans ( ) and  ( ), two microRNA genes important for neuronal function. We sequenced ∼6.9 kb / and upstream regulatory sequences in 2,610 SZ cases and 2,611 controls of European ancestry. We identified 133 rare variants with minor allele frequency (MAF) 〈0.5%. The rare variant burden in promoters and enhancers, but not insulators, was associated with SZ (p = 0.021 for MAF 〈 0.5%, p = 0.003 for MAF 〈 0.1%). A rare enhancer SNP, 1:g.98515539A〉T, presented exclusively in 11 SZ cases (nominal p = 4.8 × 10 ). We further identified its risk allele T in 2 of 2,434 additional SZ cases, 11 of 4,339 bipolar (BP) cases, and 3 of 3,572 SZ/BP study controls and 1,688 population controls; yielding combined p values of 0.0007, 0.0013, and 0.0001 for SZ, BP, and SZ/BP, respectively. The risk allele T of 1:g.98515539A〉T reduced enhancer activity of its flanking sequence by 〉50% in human neuroblastoma cells, predicting lower expression of . Both empirical and computational analyses showed weaker transcription factor (YY1) binding by the risk allele. Chromatin conformation capture (3C) assay further indicated that 1:g.98515539A〉T influenced , but not the nearby . Our results suggest that rare noncoding risk variants are associated with SZ and BP at locus, with risk alleles decreasing expression.
    Keywords: Biology
    ISSN: 0002-9297
    E-ISSN: 1537-6605
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  • 10
    Language: English
    In: Nature genetics, 2011, Vol.43(10), pp.969-976
    Description: We examined the role of common genetic variation in schizophrenia in a genome-wide association study of substantial size: a stage 1 discovery sample of 21,856 individuals of European ancestry and a stage 2 replication sample of 29,839 independent subjects. The combined stage 1 and 2 analysis yielded genome-wide significant associations with schizophrenia for seven loci, five of which are new (1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33) and two of which have been previously implicated (6p21.32-p22.1 and 18q21.2). The strongest new finding (P = 1.6 × 10(-11)) was with rs1625579 within an intron of a putative primary transcript for MIR137 (microRNA 137), a known regulator of neuronal development. Four other schizophrenia loci achieving genome-wide significance contain predicted targets of MIR137, suggesting MIR137-mediated dysregulation as a previously unknown etiologic mechanism in schizophrenia. In a joint analysis with a bipolar disorder sample (16,374 affected individuals and 14,044 controls), three loci reached genome-wide significance: CACNA1C (rs4765905, P = 7.0 × 10(-9)), ANK3 (rs10994359, P = 2.5 × 10(-8)) and the ITIH3-ITIH4 region (rs2239547, P = 7.8 × 10(-9))
    Keywords: Schizophrenia ; Mental Disorders ; Bipolar Disorder ; Replication ; Mirna ; Introns ; Genetic Diversity ; Transcription ; Development & Cell Cycle ; Behavioral and Cognitive Neuroscience ; RNA;
    ISSN: 1061-4036
    ISSN: 1546-1718
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