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Dashti HS, Jones SE, Wood AR, Lane JM, van Hees VT, Wang H, Rhodes JA, Song Y, Patel K, Anderson SG, Beaumont RN, Bechtold DA, Bowden J, Cade BE, Garaulet M, Kyle SD, Little MA, Loudon AS, Luik AI, Scheer FAJL, Spiegelhalder K, Tyrrell J, Gottlieb DJ, Tiemeier H, Ray DW, Purcell SM, Frayling TM, Redline S, Lawlor DA, Rutter MK, Weedon MN, Saxena R. Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nat Commun 2019; 10:1100. [PMID: 30846698 PMCID: PMC6405943 DOI: 10.1038/s41467-019-08917-4] [Citation(s) in RCA: 325] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 01/31/2019] [Indexed: 12/22/2022] Open
Abstract
Sleep is an essential state of decreased activity and alertness but molecular factors regulating sleep duration remain unknown. Through genome-wide association analysis in 446,118 adults of European ancestry from the UK Biobank, we identify 78 loci for self-reported habitual sleep duration (p < 5 × 10−8; 43 loci at p < 6 × 10−9). Replication is observed for PAX8, VRK2, and FBXL12/UBL5/PIN1 loci in the CHARGE study (n = 47,180; p < 6.3 × 10−4), and 55 signals show sign-concordant effects. The 78 loci further associate with accelerometer-derived sleep duration, daytime inactivity, sleep efficiency and number of sleep bouts in secondary analysis (n = 85,499). Loci are enriched for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission and plasticity, among others. Genetic correlation indicates shared links with anthropometric, cognitive, metabolic, and psychiatric traits and two-sample Mendelian randomization highlights a bidirectional causal link with schizophrenia. This work provides insights into the genetic basis for inter-individual variation in sleep duration implicating multiple biological pathways. Sleep is essential for homeostasis and insufficient or excessive sleep are associated with adverse outcomes. Here, the authors perform GWAS for self-reported habitual sleep duration in adults, supported by accelerometer-derived measures, and identify genetic correlation with psychiatric and metabolic traits
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Mariani S, Tarokh L, Djonlagic I, Cade BE, Morrical MG, Yaffe K, Stone KL, Loparo KA, Purcell SM, Redline S, Aeschbach D. Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource. Sleep Med 2018; 47:126-136. [PMID: 29803181 PMCID: PMC5976521 DOI: 10.1016/j.sleep.2017.11.1128] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2017] [Revised: 11/15/2017] [Accepted: 11/20/2017] [Indexed: 11/18/2022]
Abstract
STUDY OBJECTIVES We present an automated sleep electroencephalogram (EEG) spectral analysis pipeline that includes an automated artifact detection step, and we test the hypothesis that spectral power density estimates computed with this pipeline are comparable to those computed with a commercial method preceded by visual artifact detection by a sleep expert (standard approach). METHODS EEG data were analyzed from the C3-A2 lead in a sample of polysomnograms from 161 older women participants in a community-based cohort study. We calculated the sensitivity, specificity, accuracy, and Cohen's kappa measures from epoch-by-epoch comparisons of automated to visual-based artifact detection results; then we computed the average EEG spectral power densities in six commonly used EEG frequency bands and compared results from the two methods using correlation analysis and Bland-Altman plots. RESULTS Assessment of automated artifact detection showed high specificity [96.8%-99.4% in non-rapid eye movement (NREM), 96.9%-99.1% in rapid eye movement (REM) sleep] but low sensitivity (26.7%-38.1% in NREM, 9.1-27.4% in REM sleep). However, large artifacts (total power > 99th percentile) were removed with sensitivity up to 87.7% in NREM and 90.9% in REM, with specificities of 96.9% and 96.6%, respectively. Mean power densities computed with the two approaches for all EEG frequency bands showed very high correlation (≥0.99). The automated pipeline allowed for a 100-fold reduction in analysis time with regard to the standard approach. CONCLUSION Despite low sensitivity for artifact rejection, the automated pipeline generated results comparable to those obtained with a standard method that included manual artifact detection. Automated pipelines can enable practical analyses of recordings from thousands of individuals, allowing for use in genetics and epidemiological research requiring large samples.
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Fernández E, Collins MO, Frank RAW, Zhu F, Kopanitsa MV, Nithianantharajah J, Lemprière SA, Fricker D, Elsegood KA, McLaughlin CL, Croning MDR, Mclean C, Armstrong JD, Hill WD, Deary IJ, Cencelli G, Bagni C, Fromer M, Purcell SM, Pocklington AJ, Choudhary JS, Komiyama NH, Grant SGN. Arc Requires PSD95 for Assembly into Postsynaptic Complexes Involved with Neural Dysfunction and Intelligence. Cell Rep 2018; 21:679-691. [PMID: 29045836 PMCID: PMC5656750 DOI: 10.1016/j.celrep.2017.09.045] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 08/03/2017] [Accepted: 09/13/2017] [Indexed: 12/12/2022] Open
Abstract
Arc is an activity-regulated neuronal protein, but little is known about its interactions, assembly into multiprotein complexes, and role in human disease and cognition. We applied an integrated proteomic and genetic strategy by targeting a tandem affinity purification (TAP) tag and Venus fluorescent protein into the endogenous Arc gene in mice. This allowed biochemical and proteomic characterization of native complexes in wild-type and knockout mice. We identified many Arc-interacting proteins, of which PSD95 was the most abundant. PSD95 was essential for Arc assembly into 1.5-MDa complexes and activity-dependent recruitment to excitatory synapses. Integrating human genetic data with proteomic data showed that Arc-PSD95 complexes are enriched in schizophrenia, intellectual disability, autism, and epilepsy mutations and normal variants in intelligence. We propose that Arc-PSD95 postsynaptic complexes potentially affect human cognitive function. TAP tag and purification of endogenous Arc protein complexes from the mouse brain PSD95 is the major Arc binding protein, and both assemble into 1.5-MDa supercomplexes PSD95 is essential for recruitment of Arc to synapses Mutations and genetic variants in Arc-PSD95 are linked to cognition
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Özbek U, Lin HM, Lin Y, Weeks DE, Chen W, Shaffer JR, Purcell SM, Feingold E. Statistics for X-chromosome associations. Genet Epidemiol 2018; 42:539-550. [PMID: 29900581 DOI: 10.1002/gepi.22132] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Revised: 03/14/2018] [Accepted: 04/30/2018] [Indexed: 01/26/2023]
Abstract
In a genome-wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X-chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X-chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X-chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X-chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single-marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X-chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions.
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Ni G, Moser G, Wray NR, Lee SH, Ripke S, Neale BM, Corvin A, Walters JT, Farh KH, Holmans PA, Lee P, Bulik-Sullivan B, Collier DA, Huang H, Pers TH, Agartz I, Agerbo E, Albus M, Alexander M, Amin F, Bacanu SA, Begemann M, Belliveau RA, Bene J, Bergen SE, Bevilacqua E, Bigdeli TB, Black DW, Bruggeman R, Buccola NG, Buckner RL, Byerley W, Cahn W, Cai G, Campion D, Cantor RM, Carr VJ, Carrera N, Catts SV, Chambert KD, Chan RC, Chen RY, Chen EY, Cheng W, Cheung EF, Chong SA, Cloninger CR, Cohen D, Cohen N, Cormican P, Craddock N, Crowley JJ, Curtis D, Davidson M, Davis KL, Degenhardt F, Del Favero J, Demontis D, Dikeos D, Dinan T, Djurovic S, Donohoe G, Drapeau E, Duan J, Dudbridge F, Durmishi N, Eichhammer P, Eriksson J, Escott-Price V, Essioux L, Fanous AH, Farrell MS, Frank J, Franke L, Freedman R, Freimer NB, Friedl M, Friedman JI, Fromer M, Genovese G, Georgieva L, Giegling I, Giusti-Rodríguez P, Godard S, Goldstein JI, Golimbet V, Gopal S, Gratten J, de Haan L, Hammer C, Hamshere ML, Hansen M, Hansen T, Haroutunian V, Hartmann AM, Henskens FA, Herms S, Hirschhorn JN, Hoffmann P, Hofman A, Hollegaard MV, Hougaard DM, Ikeda M, Joa I, Juliá A, Kahn RS, Kalaydjieva L, Karachanak-Yankova S, Karjalainen J, Kavanagh D, Keller MC, Kennedy JL, Khrunin A, Kim Y, Klovins J, Knowles JA, Konte B, Kucinskas V, Kucinskiene ZA, Kuzelova-Ptackova H, Kähler AK, Laurent C, Keong JLC, Legge SE, Lerer B, Li M, Li T, Liang KY, Lieberman J, Limborska S, Loughland CM, Lubinski J, Lönnqvist J, Macek M, Magnusson PK, Maher BS, Maier W, Mallet J, Marsal S, Mattheisen M, Mattingsda M, McCarley RW, McDonald C, McIntosh AM, Meier S, Meijer CJ, Melegh B, Melle I, Mesholam-Gately RI, Metspalu A, Michie PT, Milani L, Milanova V, Mokrab Y, Morris DW, Mors O, Murphy KC, Murray RM, Myin-Germeys I, Müller-Myhsok B, Nelis M, Nenadic I, Nertney DA, Nestadt G, Nicodemus KK, Nikitina-Zake L, Nisenbaum L, Nordin A, O’Callaghan E, O’Dushlaine C, O’Neill FA, Oh SY, Olinc A, Olsen L, Van Os J, Pantelis C, Papadimitriou GN, Papio S, Parkhomenko E, Pato MT, Paunio T, Pejovic-Milovancevic M, Perkins DO, Pietiläinenl O, Pimm J, Pocklington AJ, Powell J, Price A, Pulver AE, Purcell SM, Quested D, Rasmussen HB, Reichenberg A, Reimers MA, Richards AL, Roffman JL, Roussos P, Ruderfer DM, Salomaa V, Sanders AR, Schall U, Schubert CR, Schulze TG, Schwab SG, Scolnick EM, Scott RJ, Seidman LJ, Shi J, Sigurdsson E, Silagadze T, Silverman JM, Sim K, Slominsky P, Smoller JW, So HC, Spencer CC, Stah EA, Stefansson H, Steinberg S, Stogmann E, Straub RE, Strengman E, Strohmaier J, Stroup TS, Subramaniam M, Suvisaari J, Svrakic DM, Szatkiewicz JP, Söderman E, Thirumalai S, Toncheva D, Tosato S, Veijola J, Waddington J, Walsh D, Wang D, Wang Q, Webb BT, Weiser M, Wildenauer DB, Williams NM, Williams S, Witt SH, Wolen AR, Wong EH, Wormley BK, Xi HS, Zai CC, Zheng X, Zimprich F, Stefansson K, Visscher PM, Adolfsson R, Andreassen OA, Blackwood DH, Bramon E, Buxbaum JD, Børglum AD, Cichon S, Darvasi A, Domenici E, Ehrenreich H, Esko T, Gejman PV, Gill M, Gurling H, Hultman CM, Iwata N, Jablensky AV, Jönsson EG, Kendler KS, Kirov G, Knight J, Lencz T, Levinson DF, Li QS, Liu J, Malhotra AK, McCarrol SA, McQuillin A, Moran JL, Mortensen PB, Mowry BJ, Nöthen MM, Ophoff RA, Owen MJ, Palotie A, Pato CN, Petryshen TL, Posthuma D, Rietsche M, Riley BP, Rujescu D, Sham PC, Sklar P, St Clair D, Weinberger DR, Wendland JR, Werge T, Daly MJ, Sullivan PF, O’Donovan MC. Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood. Am J Hum Genet 2018; 102:1185-1194. [PMID: 29754766 PMCID: PMC5993419 DOI: 10.1016/j.ajhg.2018.03.021] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 03/20/2018] [Indexed: 10/16/2022] Open
Abstract
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.
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Blokland GAM, del Re EC, Mesholam-Gately RI, Jovicich J, Trampush JW, Keshavan MS, DeLisi LE, Walters JTR, Turner JA, Malhotra AK, Lencz T, Shenton ME, Voineskos AN, Rujescu D, Giegling I, Kahn RS, Roffman JL, Holt DJ, Ehrlich S, Kikinis Z, Dazzan P, Murray RM, Di Forti M, Lee J, Sim K, Lam M, Wolthusen RPF, de Zwarte SMC, Walton E, Cosgrove D, Kelly S, Maleki N, Osiecki L, Picchioni MM, Bramon E, Russo M, David AS, Mondelli V, Reinders AATS, Falcone MA, Hartmann AM, Konte B, Morris DW, Gill M, Corvin AP, Cahn W, Ho NF, Liu JJ, Keefe RSE, Gollub RL, Manoach DS, Calhoun VD, Schulz SC, Sponheim SR, Goff DC, Buka SL, Cherkerzian S, Thermenos HW, Kubicki M, Nestor PG, Dickie EW, Vassos E, Ciufolini S, Marques TR, Crossley NA, Purcell SM, Smoller JW, van Haren NEM, Toulopoulou T, Donohoe G, Goldstein JM, Seidman LJ, McCarley RW, Petryshen TL. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project. Schizophr Res 2018; 195:306-317. [PMID: 28982554 PMCID: PMC5882601 DOI: 10.1016/j.schres.2017.09.024] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/15/2017] [Accepted: 09/20/2017] [Indexed: 01/02/2023]
Abstract
BACKGROUND Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection. METHODS We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site. RESULTS Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10-10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other. CONCLUSIONS The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.
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Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TMF, Bacanu SA, Bækvad-Hansen M, Beekman AFT, Bigdeli TB, Binder EB, Blackwood DRH, Bryois J, Buttenschøn HN, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen JH, Clarke TK, Coleman JIR, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford GE, Crowley CA, Dashti HS, Davies G, Deary IJ, Degenhardt F, Derks EM, Direk N, Dolan CV, Dunn EC, Eley TC, Eriksson N, Escott-Price V, Kiadeh FHF, Finucane HK, Forstner AJ, Frank J, Gaspar HA, Gill M, Giusti-Rodríguez P, Goes FS, Gordon SD, Grove J, Hall LS, Hannon E, Hansen CS, Hansen TF, Herms S, Hickie IB, Hoffmann P, Homuth G, Horn C, Hottenga JJ, Hougaard DM, Hu M, Hyde CL, Ising M, Jansen R, Jin F, Jorgenson E, Knowles JA, Kohane IS, Kraft J, Kretzschmar WW, Krogh J, Kutalik Z, Lane JM, Li Y, Li Y, Lind PA, Liu X, Lu L, MacIntyre DJ, MacKinnon DF, Maier RM, Maier W, Marchini J, Mbarek H, McGrath P, McGuffin P, Medland SE, Mehta D, Middeldorp CM, Mihailov E, Milaneschi Y, Milani L, Mill J, Mondimore FM, Montgomery GW, Mostafavi S, Mullins N, Nauck M, Ng B, Nivard MG, Nyholt DR, O'Reilly PF, Oskarsson H, Owen MJ, Painter JN, Pedersen CB, Pedersen MG, Peterson RE, Pettersson E, Peyrot WJ, Pistis G, Posthuma D, Purcell SM, Quiroz JA, Qvist P, Rice JP, Riley BP, Rivera M, Saeed Mirza S, Saxena R, Schoevers R, Schulte EC, Shen L, Shi J, Shyn SI, Sigurdsson E, Sinnamon GBC, Smit JH, Smith DJ, Stefansson H, Steinberg S, Stockmeier CA, Streit F, Strohmaier J, Tansey KE, Teismann H, Teumer A, Thompson W, Thomson PA, Thorgeirsson TE, Tian C, Traylor M, Treutlein J, Trubetskoy V, Uitterlinden AG, Umbricht D, Van der Auwera S, van Hemert AM, Viktorin A, Visscher PM, Wang Y, Webb BT, Weinsheimer SM, Wellmann J, Willemsen G, Witt SH, Wu Y, Xi HS, Yang J, Zhang F, Arolt V, Baune BT, Berger K, Boomsma DI, Cichon S, Dannlowski U, de Geus ECJ, DePaulo JR, Domenici E, Domschke K, Esko T, Grabe HJ, Hamilton SP, Hayward C, Heath AC, Hinds DA, Kendler KS, Kloiber S, Lewis G, Li QS, Lucae S, Madden PFA, Magnusson PK, Martin NG, McIntosh AM, Metspalu A, Mors O, Mortensen PB, Müller-Myhsok B, Nordentoft M, Nöthen MM, O'Donovan MC, Paciga SA, Pedersen NL, Penninx BWJH, Perlis RH, Porteous DJ, Potash JB, Preisig M, Rietschel M, Schaefer C, Schulze TG, Smoller JW, Stefansson K, Tiemeier H, Uher R, Völzke H, Weissman MM, Werge T, Winslow AR, Lewis CM, Levinson DF, Breen G, Børglum AD, Sullivan PF. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 2018; 50:668-681. [PMID: 29700475 PMCID: PMC5934326 DOI: 10.1038/s41588-018-0090-3] [Citation(s) in RCA: 1745] [Impact Index Per Article: 290.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Accepted: 02/14/2018] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.
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Stone KL, Peters KE, Redline S, Yaffe K, Purcell SM, Mariani S, Djonlagic I, Younes M. 1013 Novel Quantitative EEG Exposures and Risk of Incident MCI and Dementia in Older Women. Sleep 2018. [DOI: 10.1093/sleep/zsy061.1012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Smoll EJ, Tesa-Serrate MA, Purcell SM, D'Andrea L, Bruce DW, Slattery JM, Costen ML, Minton TK, McKendrick KG. Determining the composition of the vacuum-liquid interface in ionic-liquid mixtures. Faraday Discuss 2018; 206:497-522. [PMID: 28944811 DOI: 10.1039/c7fd00175d] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The vacuum-liquid interfaces of a number of ionic-liquid mixtures have been investigated using the combination of reactive-atom scattering with laser-induced fluorescence detection (RAS-LIF), selected surface tension measurements, and molecular dynamics (MD) simulations. The mixtures are based on the widespread 1-alkyl-3-methylimidazolium ([Cnmim]+) cation, including mixed cations which differ in chain length or chemical functionality with a common anion; and different anions for a common cation. RAS-LIF results imply that the surface compositions exhibit a general form of non-stoichiometric behaviour that mimics the well-known Henry's and Raoult's laws at low and high mole fraction, respectively. The extended Langmuir model provides a moderately good single-parameter fit, but higher-order terms are required for an accurate description. The quantitative relationship between RAS-LIF and surface tension, which probes the surface composition only indirectly, is explored for mixtures of [C2mim]+ and [C12mim]+ with a common bis(trifluoromethylsulfonyl)imide ([NTf2]-) anion. Extended Langmuir model fits to surface tension data are broadly consistent with those to RAS-LIF; however, several other common approaches to extracting surface compositions from measured surface tensions result in much larger discrepancies. MD simulations suggest that RAS-LIF faithfully reports on the alkyl-chain exposure at the surface, which is only subtly modified by composition-dependent structural reorganisation.
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Nguyen HT, Bryois J, Kim A, Dobbyn A, Huckins LM, Munoz-Manchado AB, Ruderfer DM, Genovese G, Fromer M, Xu X, Pinto D, Linnarsson S, Verhage M, Smit AB, Hjerling-Leffler J, Buxbaum JD, Hultman C, Sklar P, Purcell SM, Lage K, He X, Sullivan PF, Stahl EA. Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders. Genome Med 2017; 9:114. [PMID: 29262854 PMCID: PMC5738153 DOI: 10.1186/s13073-017-0497-y] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 11/16/2017] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified. METHODS We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls). RESULTS For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq. CONCLUSIONS We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.
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Direk N, Williams S, Smith JA, Ripke S, Air T, Amare AT, Amin N, Baune BT, Bennett DA, Blackwood DH, Boomsma D, Breen G, Buttenschøn HN, Byrne EM, Børglum AD, Castelao E, Cichon S, Clarke TK, Cornelis MC, Dannlowski U, De Jager PL, Demirkan A, Domenici E, van Duijn CM, Dunn EC, Eriksson JG, Esko T, Faul JD, Ferrucci L, Fornage M, de Geus E, Gill M, Gordon SD, Jörgen Grabe H, van Grootheest G, Hamilton SP, Hartman CA, Heath AC, Hek K, Hofman A, Homuth G, Horn C, Hottenga JJ, Kardia SL, Kloiber S, Koenen K, Kutalik Z, Ladwig KH, Lahti J, Levinson DF, Lewis CM, Lewis G, Li QS, Llewellyn DJ, Lucae S, Lunetta KL, MacIntyre DJ, Madden P, Martin NG, McIntosh AM, Metspalu A, Milaneschi Y, Montgomery GW, Mors O, Mosley TH, Murabito JM, Müller-Myhsok B, Nöthen MM, Nyholt DR, O’Donovan MC, Penninx BW, Pergadia ML, Perlis R, Potash JB, Preisig M, Purcell SM, Quiroz JA, Räikkönen K, Rice JP, Rietschel M, Rivera M, Schulze TG, Shi J, Shyn S, Sinnamon GC, Smit JH, Smoller JW, Snieder H, Tanaka T, Tansey KE, Teumer A, Uher R, Umbricht D, Van der Auwera S, Ware EB, Weir DR, Weissman MM, Willemsen G, Yang J, Zhao W, Tiemeier H, Sullivan PF. An Analysis of Two Genome-wide Association Meta-analyses Identifies a New Locus for Broad Depression Phenotype. Biol Psychiatry 2017; 82:322-329. [PMID: 28049566 PMCID: PMC5462867 DOI: 10.1016/j.biopsych.2016.11.013] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 11/11/2016] [Accepted: 11/22/2016] [Indexed: 10/20/2022]
Abstract
BACKGROUND The genetics of depression has been explored in genome-wide association studies that focused on either major depressive disorder or depressive symptoms with mostly negative findings. A broad depression phenotype including both phenotypes has not been tested previously using a genome-wide association approach. We aimed to identify genetic polymorphisms significantly associated with a broad phenotype from depressive symptoms to major depressive disorder. METHODS We analyzed two prior studies of 70,017 participants of European ancestry from general and clinical populations in the discovery stage. We performed a replication meta-analysis of 28,328 participants. Single nucleotide polymorphism (SNP)-based heritability and genetic correlations were calculated using linkage disequilibrium score regression. Discovery and replication analyses were performed using a p-value-based meta-analysis. Lifetime major depressive disorder and depressive symptom scores were used as the outcome measures. RESULTS The SNP-based heritability of major depressive disorder was 0.21 (SE = 0.02), the SNP-based heritability of depressive symptoms was 0.04 (SE = 0.01), and their genetic correlation was 1.001 (SE = 0.2). We found one genome-wide significant locus related to the broad depression phenotype (rs9825823, chromosome 3: 61,082,153, p = 8.2 × 10-9) located in an intron of the FHIT gene. We replicated this SNP in independent samples (p = .02) and the overall meta-analysis of the discovery and replication cohorts (1.0 × 10-9). CONCLUSIONS This large study identified a new locus for depression. Our results support a continuum between depressive symptoms and major depressive disorder. A phenotypically more inclusive approach may help to achieve the large sample sizes needed to detect susceptibility loci for depression.
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Purcell SM, Manoach DS, Demanuele C, Cade BE, Mariani S, Cox R, Panagiotaropoulou G, Saxena R, Pan JQ, Smoller JW, Redline S, Stickgold R. Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource. Nat Commun 2017. [PMID: 28649997 PMCID: PMC5490197 DOI: 10.1038/ncomms15930] [Citation(s) in RCA: 230] [Impact Index Per Article: 32.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders. Sleep patterns vary and are associated with health and disease. Here Purcell et al characterize sleep spindle activity in 11,630 individuals and describe age-related changes, genetic influences, and possible confounding effects, serving as a resource for further understanding the physiology of sleep.
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Panagiotaropoulou G, Cade B, Mariani S, Demanuale C, Cox R, Saxena R, Pan J, Smoller J, Stickgold R, Manoach D, Redline S, Purcell SM. 0001 GENOME-WIDE ANALYSES OF SLEEP SPINDLES IN THE NATIONAL SLEEP RESEARCH RESOURCE. Sleep 2017. [DOI: 10.1093/sleepj/zsx050.000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Lane JM, Liang J, Vlasac I, Anderson SG, Bechtold DA, Bowden J, Emsley R, Gill S, Little MA, Luik AI, Loudon A, Scheer FAJL, Purcell SM, Kyle SD, Lawlor DA, Zhu X, Redline S, Ray DW, Rutter MK, Saxena R. Genome-wide association analyses of sleep disturbance traits identify new loci and highlight shared genetics with neuropsychiatric and metabolic traits. Nat Genet 2017; 49:274-281. [PMID: 27992416 PMCID: PMC5491693 DOI: 10.1038/ng.3749] [Citation(s) in RCA: 238] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 11/21/2016] [Indexed: 12/16/2022]
Abstract
Chronic sleep disturbances, associated with cardiometabolic diseases, psychiatric disorders and all-cause mortality, affect 25-30% of adults worldwide. Although environmental factors contribute substantially to self-reported habitual sleep duration and disruption, these traits are heritable and identification of the genes involved should improve understanding of sleep, mechanisms linking sleep to disease and development of new therapies. We report single- and multiple-trait genome-wide association analyses of self-reported sleep duration, insomnia symptoms and excessive daytime sleepiness in the UK Biobank (n = 112,586). We discover loci associated with insomnia symptoms (near MEIS1, TMEM132E, CYCL1 and TGFBI in females and WDR27 in males), excessive daytime sleepiness (near AR-OPHN1) and a composite sleep trait (near PATJ (INADL) and HCRTR2) and replicate a locus associated with sleep duration (at PAX8). We also observe genetic correlation between longer sleep duration and schizophrenia risk (rg = 0.29, P = 1.90 × 10-13) and between increased levels of excessive daytime sleepiness and increased measures for adiposity traits (body mass index (BMI): rg = 0.20, P = 3.12 × 10-9; waist circumference: rg = 0.20, P = 2.12 × 10-7).
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Charney AW, Ruderfer DM, Stahl EA, Moran JL, Chambert K, Belliveau RA, Forty L, Gordon-Smith K, Di Florio A, Lee PH, Bromet EJ, Buckley PF, Escamilla MA, Fanous AH, Fochtmann LJ, Lehrer DS, Malaspina D, Marder SR, Morley CP, Nicolini H, Perkins DO, Rakofsky JJ, Rapaport MH, Medeiros H, Sobell JL, Green EK, Backlund L, Bergen SE, Juréus A, Schalling M, Lichtenstein P, Roussos P, Knowles JA, Jones I, Jones LA, Hultman CM, Perlis RH, Purcell SM, McCarroll SA, Pato CN, Pato MT, Craddock N, Landén M, Smoller JW, Sklar P. Evidence for genetic heterogeneity between clinical subtypes of bipolar disorder. Transl Psychiatry 2017; 7:e993. [PMID: 28072414 PMCID: PMC5545718 DOI: 10.1038/tp.2016.242] [Citation(s) in RCA: 130] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Revised: 09/28/2016] [Accepted: 09/28/2016] [Indexed: 01/12/2023] Open
Abstract
We performed a genome-wide association study of 6447 bipolar disorder (BD) cases and 12 639 controls from the International Cohort Collection for Bipolar Disorder (ICCBD). Meta-analysis was performed with prior results from the Psychiatric Genomics Consortium Bipolar Disorder Working Group for a combined sample of 13 902 cases and 19 279 controls. We identified eight genome-wide significant, associated regions, including a novel associated region on chromosome 10 (rs10884920; P=3.28 × 10-8) that includes the brain-enriched cytoskeleton protein adducin 3 (ADD3), a non-coding RNA, and a neuropeptide-specific aminopeptidase P (XPNPEP1). Our large sample size allowed us to test the heritability and genetic correlation of BD subtypes and investigate their genetic overlap with schizophrenia and major depressive disorder. We found a significant difference in heritability of the two most common forms of BD (BD I SNP-h2=0.35; BD II SNP-h2=0.25; P=0.02). The genetic correlation between BD I and BD II was 0.78, whereas the genetic correlation was 0.97 when BD cohorts containing both types were compared. In addition, we demonstrated a significantly greater load of polygenic risk alleles for schizophrenia and BD in patients with BD I compared with patients with BD II, and a greater load of schizophrenia risk alleles in patients with the bipolar type of schizoaffective disorder compared with patients with either BD I or BD II. These results point to a partial difference in the genetic architecture of BD subtypes as currently defined.
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Marshall CR, Howrigan DP, Merico D, Thiruvahindrapuram B, Wu W, Greer DS, Antaki D, Shetty A, Holmans PA, Pinto D, Gujral M, Brandler WM, Malhotra D, Wang Z, Fajarado KVF, Maile MS, Ripke S, Agartz I, Albus M, Alexander M, Amin F, Atkins J, Bacanu SA, Belliveau RA, Bergen SE, Bertalan M, Bevilacqua E, Bigdeli TB, Black DW, Bruggeman R, Buccola NG, Buckner RL, Bulik-Sullivan B, Byerley W, Cahn W, Cai G, Cairns MJ, Campion D, Cantor RM, Carr VJ, Carrera N, Catts SV, Chambert KD, Cheng W, Cloninger CR, Cohen D, Cormican P, Craddock N, Crespo-Facorro B, Crowley JJ, Curtis D, Davidson M, Davis KL, Degenhardt F, Del Favero J, DeLisi LE, Dikeos D, Dinan T, Djurovic S, Donohoe G, Drapeau E, Duan J, Dudbridge F, Eichhammer P, Eriksson J, Escott-Price V, Essioux L, Fanous AH, Farh KH, Farrell MS, Frank J, Franke L, Freedman R, Freimer NB, Friedman JI, Forstner AJ, Fromer M, Genovese G, Georgieva L, Gershon ES, Giegling I, Giusti-Rodríguez P, Godard S, Goldstein JI, Gratten J, de Haan L, Hamshere ML, Hansen M, Hansen T, Haroutunian V, Hartmann AM, Henskens FA, Herms S, Hirschhorn JN, Hoffmann P, Hofman A, Huang H, Ikeda M, Joa I, Kähler AK, Kahn RS, Kalaydjieva L, Karjalainen J, Kavanagh D, Keller MC, Kelly BJ, Kennedy JL, Kim Y, Knowles JA, Konte B, Laurent C, Lee P, Lee SH, Legge SE, Lerer B, Levy DL, Liang KY, Lieberman J, Lönnqvist J, Loughland CM, Magnusson PKE, Maher BS, Maier W, Mallet J, Mattheisen M, Mattingsdal M, McCarley RW, McDonald C, McIntosh AM, Meier S, Meijer CJ, Melle I, Mesholam-Gately RI, Metspalu A, Michie PT, Milani L, Milanova V, Mokrab Y, Morris DW, Müller-Myhsok B, Murphy KC, Murray RM, Myin-Germeys I, Nenadic I, Nertney DA, Nestadt G, Nicodemus KK, Nisenbaum L, Nordin A, O'Callaghan E, O'Dushlaine C, Oh SY, Olincy A, Olsen L, O'Neill FA, Van Os J, Pantelis C, Papadimitriou GN, Parkhomenko E, Pato MT, Paunio T, Perkins DO, Pers TH, Pietiläinen O, Pimm J, Pocklington AJ, Powell J, Price A, Pulver AE, Purcell SM, Quested D, Rasmussen HB, Reichenberg A, Reimers MA, Richards AL, Roffman JL, Roussos P, Ruderfer DM, Salomaa V, Sanders AR, Savitz A, Schall U, Schulze TG, Schwab SG, Scolnick EM, Scott RJ, Seidman LJ, Shi J, Silverman JM, Smoller JW, Söderman E, Spencer CCA, Stahl EA, Strengman E, Strohmaier J, Stroup TS, Suvisaari J, Svrakic DM, Szatkiewicz JP, Thirumalai S, Tooney PA, Veijola J, Visscher PM, Waddington J, Walsh D, Webb BT, Weiser M, Wildenauer DB, Williams NM, Williams S, Witt SH, Wolen AR, Wormley BK, Wray NR, Wu JQ, Zai CC, Adolfsson R, Andreassen OA, Blackwood DHR, Bramon E, Buxbaum JD, Cichon S, Collier DA, Corvin A, Daly MJ, Darvasi A, Domenici E, Esko T, Gejman PV, Gill M, Gurling H, Hultman CM, Iwata N, Jablensky AV, Jönsson EG, Kendler KS, Kirov G, Knight J, Levinson DF, Li QS, McCarroll SA, McQuillin A, Moran JL, Mowry BJ, Nöthen MM, Ophoff RA, Owen MJ, Palotie A, Pato CN, Petryshen TL, Posthuma D, Rietschel M, Riley BP, Rujescu D, Sklar P, St Clair D, Walters JTR, Werge T, Sullivan PF, O'Donovan MC, Scherer SW, Neale BM, Sebat J. Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects. Nat Genet 2017; 49:27-35. [PMID: 27869829 PMCID: PMC5737772 DOI: 10.1038/ng.3725] [Citation(s) in RCA: 658] [Impact Index Per Article: 94.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 10/24/2016] [Indexed: 12/14/2022]
Abstract
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 × 10-15), which persisted after excluding loci implicated in previous studies (OR = 1.07, P = 1.7 × 10-6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 × 10-11) and neurobehavioral phenotypes in mouse (OR = 1.18, P = 7.3 × 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.
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Ganna A, Genovese G, Howrigan DP, Byrnes A, Kurki M, Zekavat SM, Whelan CW, Kals M, Nivard MG, Bloemendal A, Bloom JM, Goldstein JI, Poterba T, Seed C, Handsaker RE, Natarajan P, Mägi R, Gage D, Robinson EB, Metspalu A, Salomaa V, Suvisaari J, Purcell SM, Sklar P, Kathiresan S, Daly MJ, McCarroll SA, Sullivan PF, Palotie A, Esko T, Hultman C, Neale BM. Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat Neurosci 2016; 19:1563-1565. [PMID: 27694993 PMCID: PMC5127781 DOI: 10.1038/nn.4404] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 09/07/2016] [Indexed: 12/14/2022]
Abstract
Disruptive, damaging ultra-rare variants in highly constrained genes are enriched in individuals with neurodevelopmental disorders. In the general population, this class of variants was associated with a decrease in years of education (YOE). This effect was stronger among highly brain-expressed genes and explained more YOE variance than pathogenic copy number variation but less than common variants. Disruptive, damaging ultra-rare variants in highly constrained genes influence the determinants of YOE in the general population.
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Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, Ruderfer DM, Oh EC, Topol A, Shah HR, Klei LL, Kramer R, Pinto D, Gümüş ZH, Cicek AE, Dang KK, Browne A, Lu C, Xie L, Readhead B, Stahl EA, Xiao J, Parvizi M, Hamamsy T, Fullard JF, Wang YC, Mahajan MC, Derry JMJ, Dudley JT, Hemby SE, Logsdon BA, Talbot K, Raj T, Bennett DA, De Jager PL, Zhu J, Zhang B, Sullivan PF, Chess A, Purcell SM, Shinobu LA, Mangravite LM, Toyoshiba H, Gur RE, Hahn CG, Lewis DA, Haroutunian V, Peters MA, Lipska BK, Buxbaum JD, Schadt EE, Hirai K, Roeder K, Brennand KJ, Katsanis N, Domenici E, Devlin B, Sklar P. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat Neurosci 2016; 19:1442-1453. [PMID: 27668389 PMCID: PMC5083142 DOI: 10.1038/nn.4399] [Citation(s) in RCA: 735] [Impact Index Per Article: 91.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 09/01/2016] [Indexed: 12/15/2022]
Abstract
Over 100 genetic loci harbor schizophrenia-associated variants, yet how these variants confer liability is uncertain. The CommonMind Consortium sequenced RNA from dorsolateral prefrontal cortex of people with schizophrenia (N = 258) and control subjects (N = 279), creating a resource of gene expression and its genetic regulation. Using this resource, ∼20% of schizophrenia loci have variants that could contribute to altered gene expression and liability. In five loci, only a single gene was involved: FURIN, TSNARE1, CNTN4, CLCN3 or SNAP91. Altering expression of FURIN, TSNARE1 or CNTN4 changed neurodevelopment in zebrafish; knockdown of FURIN in human neural progenitor cells yielded abnormal migration. Of 693 genes showing significant case-versus-control differential expression, their fold changes were ≤ 1.33, and an independent cohort yielded similar results. Gene co-expression implicates a network relevant for schizophrenia. Our findings show that schizophrenia is polygenic and highlight the utility of this resource for mechanistic interpretations of genetic liability for brain diseases.
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Minikel EV, Vallabh SM, Lek M, Estrada K, Samocha KE, Sathirapongsasuti JF, McLean CY, Tung JY, Yu LPC, Gambetti P, Blevins J, Zhang S, Cohen Y, Chen W, Yamada M, Hamaguchi T, Sanjo N, Mizusawa H, Nakamura Y, Kitamoto T, Collins SJ, Boyd A, Will RG, Knight R, Ponto C, Zerr I, Kraus TFJ, Eigenbrod S, Giese A, Calero M, de Pedro-Cuesta J, Haïk S, Laplanche JL, Bouaziz-Amar E, Brandel JP, Capellari S, Parchi P, Poleggi A, Ladogana A, O'Donnell-Luria AH, Karczewski KJ, Marshall JL, Boehnke M, Laakso M, Mohlke KL, Kähler A, Chambert K, McCarroll S, Sullivan PF, Hultman CM, Purcell SM, Sklar P, van der Lee SJ, Rozemuller A, Jansen C, Hofman A, Kraaij R, van Rooij JGJ, Ikram MA, Uitterlinden AG, van Duijn CM, Daly MJ, MacArthur DG. Quantifying prion disease penetrance using large population control cohorts. Sci Transl Med 2016; 8:322ra9. [PMID: 26791950 DOI: 10.1126/scitranslmed.aad5169] [Citation(s) in RCA: 228] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
More than 100,000 genetic variants are reported to cause Mendelian disease in humans, but the penetrance-the probability that a carrier of the purported disease-causing genotype will indeed develop the disease-is generally unknown. We assess the impact of variants in the prion protein gene (PRNP) on the risk of prion disease by analyzing 16,025 prion disease cases, 60,706 population control exomes, and 531,575 individuals genotyped by 23andMe Inc. We show that missense variants in PRNP previously reported to be pathogenic are at least 30 times more common in the population than expected on the basis of genetic prion disease prevalence. Although some of this excess can be attributed to benign variants falsely assigned as pathogenic, other variants have genuine effects on disease susceptibility but confer lifetime risks ranging from <0.1 to ~100%. We also show that truncating variants in PRNP have position-dependent effects, with true loss-of-function alleles found in healthy older individuals, a finding that supports the safety of therapeutic suppression of prion protein expression.
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Manoach DS, Pan JQ, Purcell SM, Stickgold R. Reduced Sleep Spindles in Schizophrenia: A Treatable Endophenotype That Links Risk Genes to Impaired Cognition? Biol Psychiatry 2016; 80:599-608. [PMID: 26602589 PMCID: PMC4833702 DOI: 10.1016/j.biopsych.2015.10.003] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 09/18/2015] [Accepted: 10/05/2015] [Indexed: 11/26/2022]
Abstract
Although schizophrenia (SZ) is defined by waking phenomena, abnormal sleep is a common feature. In particular, there is accumulating evidence of a sleep spindle deficit. Sleep spindles, a defining thalamocortical oscillation of non-rapid eye movement stage 2 sleep, correlate with IQ and are thought to promote long-term potentiation and enhance memory consolidation. We review evidence that reduced spindle activity in SZ is an endophenotype that impairs sleep-dependent memory consolidation, contributes to symptoms, and is a novel treatment biomarker. Studies showing that spindles can be pharmacologically enhanced in SZ and that increasing spindles improves memory in healthy individuals suggest that treating spindle deficits in patients with SZ may improve cognition. Spindle activity is highly heritable, and recent large-scale genome-wide association studies have identified SZ risk genes that may contribute to spindle deficits and illuminate their mechanisms. For example, the SZ risk gene CACNA1I encodes a calcium channel that is abundantly expressed in the thalamic spindle generator and plays a critical role in spindle activity based on a mouse knockout. Future genetic studies of animals and humans can delineate the role of this and other genes in spindles. Such cross-disciplinary research, by forging empirical links in causal chains from risk genes to proteins and cellular functions to endophenotypes, cognitive impairments, symptoms, and diagnosis, has the potential to advance the mechanistic understanding, treatment, and prevention of SZ. This review highlights the importance of deficient sleep-dependent memory consolidation among the cognitive deficits of SZ and implicates reduced sleep spindles as a potentially treatable mechanism.
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Genovese G, Fromer M, Stahl EA, Ruderfer DM, Chambert K, Landén M, Moran JL, Purcell SM, Sklar P, Sullivan PF, Hultman CM, McCarroll SA. Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat Neurosci 2016; 19:1433-1441. [PMID: 27694994 PMCID: PMC5104192 DOI: 10.1038/nn.4402] [Citation(s) in RCA: 316] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Accepted: 09/06/2016] [Indexed: 12/15/2022]
Abstract
By analyzing the exomes of 12,332 unrelated Swedish individuals – including 4,877 affected with schizophrenia – in ways informed by exome sequences from 45,376 other individuals, we identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) that were unique to individual Swedes. We found that gene-disruptive and putatively protein-damaging URVs (but not synonymous URVs) were more abundant in schizophrenia cases than controls (P = 1.3 × 10−10). This elevation of protein-compromising URVs was several times larger than an analogously elevated rate for de novo mutations, suggesting that most rare-variant effects on schizophrenia risk are inherited. Among individuals with schizophrenia, the elevated frequency of protein-compromising URVs was concentrated in brain-expressed genes, particularly in neuronally expressed genes; most of this genetic signal arose from large sets of genes whose RNAs have been found to interact with synaptically localized proteins. Our results suggest that synaptic dysfunction may mediate a large fraction of strong, individually rare genetic influences on schizophrenia risk.
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Lee SH, Byrne EM, Hultman CM, Kähler A, Vinkhuyzen AAE, Ripke S, Andreassen OA, Frisell T, Gusev A, Hu X, Karlsson R, Mantzioris VX, McGrath JJ, Mehta D, Stahl EA, Zhao Q, Kendler KS, Sullivan PF, Price AL, O'Donovan M, Okada Y, Mowry BJ, Raychaudhuri S, Wray NR, Byerley W, Cahn W, Cantor RM, Cichon S, Cormican P, Curtis D, Djurovic S, Escott-Price V, Gejman PV, Georgieva L, Giegling I, Hansen TF, Ingason A, Kim Y, Konte B, Lee PH, McIntosh A, McQuillin A, Morris DW, Nöthen MM, O'Dushlaine C, Olincy A, Olsen L, Pato CN, Pato MT, Pickard BS, Posthuma D, Rasmussen HB, Rietschel M, Rujescu D, Schulze TG, Silverman JM, Thirumalai S, Werge T, Agartz I, Amin F, Azevedo MH, Bass N, Black DW, Blackwood DHR, Bruggeman R, Buccola NG, Choudhury K, Cloninger RC, Corvin A, Craddock N, Daly MJ, Datta S, Donohoe GJ, Duan J, Dudbridge F, Fanous A, Freedman R, Freimer NB, Friedl M, Gill M, Gurling H, De Haan L, Hamshere ML, Hartmann AM, Holmans PA, Kahn RS, Keller MC, Kenny E, Kirov GK, Krabbendam L, Krasucki R, Lawrence J, Lencz T, Levinson DF, Lieberman JA, Lin DY, Linszen DH, Magnusson PKE, Maier W, Malhotra AK, Mattheisen M, Mattingsdal M, McCarroll SA, Medeiros H, Melle I, Milanova V, Myin-Germeys I, Neale BM, Ophoff RA, Owen MJ, Pimm J, Purcell SM, Puri V, Quested DJ, Rossin L, Ruderfer D, Sanders AR, Shi J, Sklar P, St Clair D, Stroup TS, Van Os J, Visscher PM, Wiersma D, Zammit S, Bridges SL, Choi HK, Coenen MJH, de Vries N, Dieud P, Greenberg JD, Huizinga TWJ, Padyukov L, Siminovitch KA, Tak PP, Worthington J, De Jager PL, Denny JC, Gregersen PK, Klareskog L, Mariette X, Plenge RM, van Laar M, van Riel P. New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis. Int J Epidemiol 2016; 44:1706-21. [PMID: 26286434 DOI: 10.1093/ije/dyv136] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
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.
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Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O'Donnell-Luria AH, Ware JS, Hill AJ, Cummings BB, Tukiainen T, Birnbaum DP, Kosmicki JA, Duncan LE, Estrada K, Zhao F, Zou J, Pierce-Hoffman E, Berghout J, Cooper DN, Deflaux N, DePristo M, Do R, Flannick J, Fromer M, Gauthier L, Goldstein J, Gupta N, Howrigan D, Kiezun A, Kurki MI, Moonshine AL, Natarajan P, Orozco L, Peloso GM, Poplin R, Rivas MA, Ruano-Rubio V, Rose SA, Ruderfer DM, Shakir K, Stenson PD, Stevens C, Thomas BP, Tiao G, Tusie-Luna MT, Weisburd B, Won HH, Yu D, Altshuler DM, Ardissino D, Boehnke M, Danesh J, Donnelly S, Elosua R, Florez JC, Gabriel SB, Getz G, Glatt SJ, Hultman CM, Kathiresan S, Laakso M, McCarroll S, McCarthy MI, McGovern D, McPherson R, Neale BM, Palotie A, Purcell SM, Saleheen D, Scharf JM, Sklar P, Sullivan PF, Tuomilehto J, Tsuang MT, Watkins HC, Wilson JG, Daly MJ, MacArthur DG. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016; 536:285-91. [PMID: 27535533 PMCID: PMC5018207 DOI: 10.1038/nature19057] [Citation(s) in RCA: 7408] [Impact Index Per Article: 926.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 06/24/2016] [Indexed: 02/02/2023]
Abstract
Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.
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Goes FS, Pirooznia M, Parla JS, Kramer M, Ghiban E, Mavruk S, Chen YC, Monson ET, Willour VL, Karchin R, Flickinger M, Locke AE, Levy SE, Scott LJ, Boehnke M, Stahl E, Moran JL, Hultman CM, Landén M, Purcell SM, Sklar P, Zandi PP, McCombie WR, Potash JB. Exome Sequencing of Familial Bipolar Disorder. JAMA Psychiatry 2016; 73:590-7. [PMID: 27120077 PMCID: PMC5600716 DOI: 10.1001/jamapsychiatry.2016.0251] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
IMPORTANCE Complex disorders, such as bipolar disorder (BD), likely result from the influence of both common and rare susceptibility alleles. While common variation has been widely studied, rare variant discovery has only recently become feasible with next-generation sequencing. OBJECTIVE To utilize a combined family-based and case-control approach to exome sequencing in BD using multiplex families as an initial discovery strategy, followed by association testing in a large case-control meta-analysis. DESIGN, SETTING, AND PARTICIPANTS We performed exome sequencing of 36 affected members with BD from 8 multiplex families and tested rare, segregating variants in 3 independent case-control samples consisting of 3541 BD cases and 4774 controls. MAIN OUTCOMES AND MEASURES We used penalized logistic regression and 1-sided gene-burden analyses to test for association of rare, segregating damaging variants with BD. Permutation-based analyses were performed to test for overall enrichment with previously identified gene sets. RESULTS We found 84 rare (frequency <1%), segregating variants that were bioinformatically predicted to be damaging. These variants were found in 82 genes that were enriched for gene sets previously identified in de novo studies of autism (19 observed vs. 10.9 expected, P = .0066) and schizophrenia (11 observed vs. 5.1 expected, P = .0062) and for targets of the fragile X mental retardation protein (FMRP) pathway (10 observed vs. 4.4 expected, P = .0076). The case-control meta-analyses yielded 19 genes that were nominally associated with BD based either on individual variants or a gene-burden approach. Although no gene was individually significant after correction for multiple testing, this group of genes continued to show evidence for significant enrichment of de novo autism genes (6 observed vs 2.6 expected, P = .028). CONCLUSIONS AND RELEVANCE Our results are consistent with the presence of prominent locus and allelic heterogeneity in BD and suggest that very large samples will be required to definitively identify individual rare variants or genes conferring risk for this disorder. However, we also identify significant associations with gene sets composed of previously discovered de novo variants in autism and schizophrenia, as well as targets of the FRMP pathway, providing preliminary support for the overlap of potential autism and schizophrenia risk genes with rare, segregating variants in families with BD.
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Ruderfer DM, Charney AW, Readhead B, Kidd BA, Kähler AK, Kenny PJ, Keiser MJ, Moran JL, Hultman CM, Scott SA, Sullivan PF, Purcell SM, Dudley JT, Sklar P. Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach. Lancet Psychiatry 2016; 3:350-7. [PMID: 26915512 PMCID: PMC4982509 DOI: 10.1016/s2215-0366(15)00553-2] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 12/01/2015] [Accepted: 12/02/2015] [Indexed: 12/21/2022]
Abstract
BACKGROUND Therapeutic treatments for schizophrenia do not alleviate symptoms for all patients and efficacy is limited by common, often severe, side-effects. Genetic studies of disease can identify novel drug targets, and drugs for which the mechanism has direct genetic support have increased likelihood of clinical success. Large-scale genetic studies of schizophrenia have increased the number of genes and gene sets associated with risk. We aimed to examine the overlap between schizophrenia risk loci and gene targets of a comprehensive set of medications to potentially inform and improve treatment of schizophrenia. METHODS We defined schizophrenia risk loci as genomic regions reaching genome-wide significance in the latest Psychiatric Genomics Consortium schizophrenia genome-wide association study (GWAS) of 36 989 cases and 113 075 controls and loss of function variants observed only once among 5079 individuals in an exome-sequencing study of 2536 schizophrenia cases and 2543 controls (Swedish Schizophrenia Study). Using two large and orthogonally created databases, we collated drug targets into 167 gene sets targeted by pharmacologically similar drugs and examined enrichment of schizophrenia risk loci in these sets. We further linked the exome-sequenced data with a national drug registry (the Swedish Prescribed Drug Register) to assess the contribution of rare variants to treatment response, using clozapine prescription as a proxy for treatment resistance. FINDINGS We combined results from testing rare and common variation and, after correction for multiple testing, two gene sets were associated with schizophrenia risk: agents against amoebiasis and other protozoal diseases (106 genes, p=0·00046, pcorrected =0·024) and antipsychotics (347 genes, p=0·00078, pcorrected=0·046). Further analysis pointed to antipsychotics as having independent enrichment after removing genes that overlapped these two target sets. We noted significant enrichment both in known targets of antipsychotics (70 genes, p=0·0078) and novel predicted targets (277 genes, p=0·019). Patients with treatment-resistant schizophrenia had an excess of rare disruptive variants in gene targets of antipsychotics (347 genes, p=0·0067) and in genes with evidence for a role in antipsychotic efficacy (91 genes, p=0·0029). INTERPRETATION Our results support genetic overlap between schizophrenia pathogenesis and antipsychotic mechanism of action. This finding is consistent with treatment efficacy being polygenic and suggests that single-target therapeutics might be insufficient. We provide evidence of a role for rare functional variants in antipsychotic treatment response, pointing to a subset of patients where their genetic information could inform treatment. Finally, we present a novel framework for identifying treatments from genetic data and improving our understanding of therapeutic mechanism. FUNDING US National Institutes of Health.
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