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Polygenic scores provide an indication of an individual’s genetic propensity for a trait within a test population. These scores are calculated using results from genetic analysis conducted in discovery populations. However, when the test and discovery populations have different ancestries, predictions are less accurate. As many genetic analyses are conducted using European populations, this hinders the potential for making predictions in many of the underrepresented populations in research. To address this, UP and Downstream Genetic scoring (UPDOG) was developed to consider the genetic architecture of both the discovery and test cohorts before calculating polygenic scores. UPDOG was tested across four ancestries and six phenotypes and benchmarked against five existing tools for polygenic scoring. In approximately two-thirds of cases UPDOG improved trans-ancestral prediction, although the increases were small. Maximising the efficacy of polygenic scores and extending it to the global population is crucial for delivering personalised medicine and universal healthcare equality.

The Mood Disorder Questionnaire (MDQ) is a common screening tool for bipolar disorder that assesses manic symptoms. Its utility for genetic studies of mania or bipolar traits has not been fully examined. We psychometrically compared the MDQ to self-reported bipolar disorder in participants from the United Kingdom National Institute of Health and Care Research Mental Health BioResource. We conducted genome-wide association studies of manic symptom quantitative traits and symptom subgroups, derived from the MDQ items (N = 11,568–19,859). We calculated genetic correlations with bipolar disorder and other psychiatric and behavioral traits. The MDQ screener showed low positive predictive value (0.29) for self-reported bipolar disorder. Neither concurrent nor lifetime manic symptoms were genetically correlated with bipolar disorder. Lifetime manic symptoms had a highest genetic correlation (rg = 1.0) with posttraumatic stress disorder although this was not confirmed by within-cohort phenotypic correlations (rp = 0.41). Other significant genetic correlations included attention deficit hyperactivity disorder (rg = 0.69), insomnia (rg = 0.55), and major depressive disorder (rg = 0.42). Our study adds to existing literature questioning the MDQ's validity and suggests it may capture symptoms of general distress or psychopathology, rather than hypomania/mania specifically, in at-risk populations.

BackgroundPosttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are commonly reported co-occurring mental health consequences of psychological trauma exposure. The disorders have high genetic overlap. Trauma is a complex phenotype but research suggests that trauma sensitivity has a heritable basis. We investigated whether sensitivity to trauma in those with MDD reflects a similar genetic component in those with PTSD.MethodsGenetic correlations between PTSD and MDD in individuals reporting trauma and MDD in individuals not reporting trauma were estimated, as well as with recurrent MDD and single-episode MDD, using genome-wide association study (GWAS) summary statistics. Genetic correlations were replicated using PTSD data from the Psychiatric Genomics Consortium and the Million Veteran Program. Polygenic risk scores were generated in UK Biobank participants who met the criteria for lifetime MDD (N = 29 471). We investigated whether genetic loading for PTSD was associated with reporting trauma in these individuals.ResultsGenetic loading for PTSD was significantly associated with reporting trauma in individuals with MDD [OR 1.04 (95% CI 1.01–1.07), Empirical-p = 0.02]. PTSD was significantly more genetically correlated with recurrent MDD than with MDD in individuals not reporting trauma (rg differences = ~0.2, p < 0.008). Participants who had experienced recurrent MDD reported significantly higher rates of trauma than participants who had experienced single-episode MDD (χ2 > 166, p < 0.001)ConclusionsOur findings point towards the existence of genetic variants associated with trauma sensitivity that might be shared between PTSD and MDD, although replication with better powered GWAS is needed. Our findings corroborate previous research highlighting trauma exposure as a key risk factor for recurrent MDD.

The burden of large and rare copy number genetic variants (CNVs) as well as certain specific CNVs increase the risk of developing schizophrenia. Several cognitive measures are purported schizophrenia endophenotypes and may represent an intermediate point between genetics and the illness. This paper investigates the influence of CNVs on cognition. We conducted a systematic review and meta-analysis of the literature exploring the effect of CNV burden on general intelligence. We included ten primary studies with a total of 18,847 participants and found no evidence of association. In a new psychosis family study, we investigated the effects of CNVs on specific cognitive abilities. We examined the burden of large and rare CNVs (>200 kb, This work was supported by the Medical Research Council (G0901310) and the Wellcome Trust (grants 085475/B/08/Z, 085475/Z/08/Z). This study was supported by the NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust and University College London and by the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust at King’s College London. Further support to EB: Mental Health Research UK’s John Grace QC award, BMA Margaret Temple grants 2016 and 2006, MRC—Korean Health Industry Development Institute Partnering Award (MC_PC_16014), MRC New Investigator Award and a MRC Centenary Award (G0901310), National Institute of Health Research UK post-doctoral fellowship, the Psychiatry Research Trust, the Schizophrenia Research Fund, the Brain and Behaviour Research foundation’s NARSAD Young Investigator Awards 2005, 2008, Wellcome Trust Research Training Fellowship, the NIHR Biomedical Research Centre at UCLH, and the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry King’s College London. Further support to co-authors: The Brain and Behaviour Research foundation’s (NARSAD’s) Young Investigator Award (Grant 22604, awarded to CI). The BMA Margaret Temple grant 2016 to JT. A 2014 European Research Council Marie Curie award to A Díez-Revuelta. HI has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 747429. A Medical Research Council doctoral studentship to JH-S, IA-Z and AB. A Mental Health Research UK studentship to RM. VB is supported by a Wellcome Trust Seed Award in Science (200589/Z/16/Z). FWO Senior Clinical Fellowship to RvW. The infrastructure for the GROUP consortium is funded through the Geestkracht programme of the Dutch Health Research Council (ZON-MW, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental health care organisations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Centre and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Groningen: University Medical Centre Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psycho-medical centre The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGZ Eindhoven en De Kempen, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Centre Utrecht and the mental health institutions Altrecht, GGZ Centraal and Delta). The Santander cohort was supported by Instituto de Salud Carlos III (PI020499, PI050427, PI060507), SENY Fundació (CI 2005-0308007), Fundacion Ramón Areces and Fundacion Marqués de Valdecilla (API07/011, API10/13). We thank Valdecilla Biobank for providing the biological PAFIP samples and associated data included in this study and for its help in the technical execution of this work; we also thank IDIVAL Neuroimaging Unit for its help in the acquisition and processing of imaging PAFIP data.

Background Progress towards understanding the aetiology of major depression is compromised by its clinical heterogeneity. The variety of contexts underlying the development of a major depressive episode may contribute to such heterogeneity. Aims To compare risk factor profiles for three subgroups of major depression according to episode context. Method Using self-report questionnaires and administrative records from the UK Biobank, we characterised three contextual subgroups of major depression: postpartum depression (3581 cases), depression following diagnosis of a chronic disease (409 cases) and a more typical (named heterogeneous) major depression phenotype excluding the two other contexts (34 699 cases). Controls with the same exposure were also defined. We tested each subgroup for association with the polygenic risk scores (PRS) for major depression and with other risk factors previously associated with major depression (bipolar disorder PRS, neuroticism, reported trauma in childhood and adulthood, socioeconomic status, family history of depression, education). Results Major depression PRS was associated with all subgroups, but postpartum depression cases had higher PRS than heterogeneous major depression cases (OR = 1.06, 95% CI 1.02–1.10). Relative to heterogeneous depression, postpartum depression was more weakly associated with adulthood trauma and neuroticism. Depression following diagnosis of a chronic disease had weaker association with neuroticism and reported trauma in adulthood and childhood relative to heterogeneous depression. Conclusions The observed differences in risk factor profiles according to the context of a major depressive episode help provide insight into the heterogeneity of depression. Future studies dissecting such heterogeneity could help reveal more refined aetiological insights.

Major depressive disorder (MDD) is defined differently across genetic research studies and this may be a key source of heterogeneity. While previous literature highlights differences between minimal and strict phenotypes, the components contributing to this heterogeneity have not been identified. Using the cardinal symptoms (depressed mood/anhedonia) as a baseline, we build MDD phenotypes using five components—(1) five or more symptoms, (2) episode duration, (3) functional impairment, (4) episode persistence, and (5) episode recurrence—to determine the contributors to such heterogeneity. Thirty-two depression phenotypes which systematically incorporate different combinations of MDD components were created using the mental health questionnaire data within the UK Biobank. SNP-based heritabilities and genetic correlations with three previously defined major depression phenotypes were calculated (Psychiatric Genomics Consortium (PGC) defined depression, 23andMe self-reported depression and broad depression) and differences between estimates analysed. All phenotypes were heritable (h2SNP range: 0.102–0.162) and showed substantial genetic correlations with other major depression phenotypes (Rg range: 0.651–0.895 (PGC); 0.652–0.837 (23andMe); 0.699–0.900 (broad depression)). The strongest effect on SNP-based heritability was from the requirement for five or more symptoms (1.4% average increase) and for a long episode duration (2.7% average decrease). No significant differences were noted between genetic correlations. While there is some variation, the two cardinal symptoms largely reflect the genetic aetiology of phenotypes incorporating more MDD components. These components may index severity, however, their impact on heterogeneity in genetic results is likely to be limited.

Background The impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health is still being unravelled. It is important to identify which individuals are at greatest risk of worsening symptoms. This study aimed to examine changes in depression, anxiety and post-traumatic stress disorder (PTSD) symptoms using prospective and retrospective symptom change assessments, and to find and examine the effect of key risk factors. Method Online questionnaires were administered to 34 465 individuals (aged 16 years or above) in April/May 2020 in the UK, recruited from existing cohorts or via social media. Around one-third (n = 12 718) of included participants had prior diagnoses of depression or anxiety and had completed pre-pandemic mental health assessments (between September 2018 and February 2020), allowing prospective investigation of symptom change. Results Prospective symptom analyses showed small decreases in depression (PHQ-9: −0.43 points) and anxiety [generalised anxiety disorder scale – 7 items (GAD)-7: −0.33 points] and increases in PTSD (PCL-6: 0.22 points). Conversely, retrospective symptom analyses demonstrated significant large increases (PHQ-9: 2.40; GAD-7 = 1.97), with 55% reported worsening mental health since the beginning of the pandemic on a global change rating. Across both prospective and retrospective measures of symptom change, worsening depression, anxiety and PTSD symptoms were associated with prior mental health diagnoses, female gender, young age and unemployed/student status. Conclusions We highlight the effect of prior mental health diagnoses on worsening mental health during the pandemic and confirm previously reported sociodemographic risk factors. Discrepancies between prospective and retrospective measures of changes in mental health may be related to recall bias-related underestimation of prior symptom severity.

A low loss metamaterial unit cell is presented with an integrated GaAs air-bridged Schottky diode to produce a dynamically tunable reflective phase shifter that is capable of up to 250° phase shift with an experimentally measured average loss of 6.2 dB at V-band. The air-bridged Schottky diode provides a tuneable capacitance in the range between 30 and 50 fF under an applied reverse voltage bias. This can be used to alter the resonant frequency and phase response of a split patch unit cell of a periodic metasurface. The air-bridged diode die, which is flip-chip soldered to the patch, has ultra-low parasitic capacitance and resistance. Simulated and measured results are presented which verify the potential for the attainment of diode switching speeds with acceptable losses at mmWave frequencies. Furthermore the study shows that this diode-based unit cell can be integrated into metamaterial components, which have potential applications in future mmWave antenna beam-steering, intelligent reflecting surfaces for 6G communications, reflect-arrays, transmit-arrays or holographic antennas.

ENIGMA-CNV Working Group: van der Meer, Dennis; Sonderby, Ida E; Kaufmann, Tobias; Walters, G Bragi; Abdellaoui, Abdel; Ames, David; Amunts, Katrin; Andersson, Micael; Armstrong, Nicola J; Bernard, Manon; Blackburn, Nicholas B; Blangero, John; Boomsma, Dorret I; Brodaty, Henry; Brouwer, Rachel M; Bulow, Robin; Cahn, Wiepke; Calhoun, Vince D; Caspers, Svenja; Cavalleri, Gianpiero L; Ching, Christopher R K; Cichon, Sven; Ciufolini, Simone; Corvin, Aiden; Crespo-Facorro, Benedicto; Curran, Joanne E; Dalvie, Shareefa; Dazzan, Paola; de Geus, Eco J C; de Zubicaray, Greig I; de Zwarte, Sonja M C; Delanty, Norman; den Braber, Anouk; Desrivieres, Sylvane; Di Forti, Marta; Doherty, Joanne L; Donohoe, Gary; Ehrlich, Stefan; Eising, Else; Espeseth, Thomas; Fisher, Simon E; Fladby, Tormod; Frei, Oleksandr; Frouin, Vincent; Fukunaga, Masaki; Gareau, Thomas; Glahn, David C; Grabe, Hans J; Groenewold, Nynke A; Gustafsson, Omar; Haavik, Jan; Haberg, Asta K; Hashimoto, Ryota; Hehir-Kwa, Jayne Y; Hibar, Derrek P; Hillegers, Manon H J; Hoffmann, Per; Holleran, Laurena; Hottenga, Jouke-Jan; Hulshoff Pol, Hilleke E; Ikeda, Masashi; Jacquemont, Sebastien; Jahanshad, Neda; Jockwitz, Christiane; Johansson, Stefan; Jonsson, Erik G; Kikuchi, Masataka; Knowles, Emma E M; Kwok, John B; Le Hellard, Stephanie; Linden, David E J; Liu, Jingyu; Lundervold, Arvid; Lundervold, Astri J; Martin, Nicholas G; Mather, Karen A; Mathias, Samuel R; McMahon, Katie L; McRae, Allan F; Medland, Sarah E; Moberget, Torgeir; Moreau, Clara; Morris, Derek W; Muhleisen, Thomas W; Murray, Robin M; Nordvik, Jan E; Nyberg, Lars; Olde Loohuis, Loes M; Ophoff, Roel A; Owen, Michael J; Paus, Tomas; Pausova, Zdenka; Peralta, Juan M; Pike, Bruce; Prieto, Carlos; Quinlan, Erin Burke; Reinbold, Celine S; Reis Marques, Tiago; Rucker, James J H; Sachdev, Perminder S; Sando, Sigrid B; Schofield, Peter R; Schork, Andrew J; Schumann, Gunter; Shin, Jean; Shumskaya, Elena; Silva, Ana I; Sisodiya, Sanjay M; Steen, Vidar M; Stein, Dan J; Strike, Lachlan T; Tamnes, Christian K; Teumer, Alexander; Thalamuthu, Anbupalam; Tordesillas-Gutierrez, Diana; Uhlmann, Anne; Ulfarsson, Magnus O; van 't Ent, Dennis; van den Bree, Marianne B M; Vassos, Evangelos; Wen, Wei; Wittfeld, Katharina; Wright, Margaret J; Zayats, Tetyana; Dale, Anders M; Djurovic, Srdjan; Agartz, Ingrid; Westlye, Lars T; Stefansson, Hreinn; Stefansson, Kari; Thompson, Paul M; Andreassen, Ole A., [Importance] Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities., [Objective] To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance., [Design, Setting, and Participants] In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019., [Main Outcomes and Measures] The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort., [Results] Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = −0.41; SE, 0.08; P = 4.9 × 10−8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10−7), and a smaller nucleus accumbens (Cohen d = −0.27; SE, 0.07; P = 7.3 × 10−5). There was also a significant negative dose response on cortical thickness (β = −0.24; SE, 0.05; P = 6.8 × 10−7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks., [Conclusions and Relevance] These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders., This study is supported in part by grants U54 EB20403, R01MH116147, and R56AG058854 from the National Institutes of Health, grant 609020 from the European Union Seventh Framework Programme, and grants 223273 and 276082 from the Research Council Norway. Part of this work was performed using the Service for Sensitive Data (TSD), which is developed and operated by the TSD Service Groupand owned by the University of Oslo.

Schizophrenia (SZ) and bipolar disorder (BD) are highly heritable, share symptomatology, and have a polygenic architecture. The impact of recent polygenic risk scores (PRS) for psychosis, which combine multiple genome-wide associated risk variations, should be assessed on heritable brain phenotypes also previously associated with the illnesses, for a better understanding of the pathways to disease. We have recently reported on the current SZ PRS's ability to predict 1st episode of psychosis case-control status and general cognition. Herein, we test its penetrance on white matter microstructure, which is known to be impaired in SZ, in BD and their relatives, using 141 participants (including SZ, BP, relatives of SZ or BP patients, and healthy volunteers), and two white matter integrity indexes: fractional anisotropy (FA) and mean diffusivity (MD). No significant correlation between the SZ PRS and FA or MD was found, thus it remains unclear whether white matter changes are primarily associated with SZ genetic risk profiles.

Publisher's version (útgefin grein), Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = −0.71 to −1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = −0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10−6, 1.7 × 10− 9, 3.5 × 10−12 and 1.0 × 10−4, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes., 1000BRAINS: 1000BRAINS is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported in part by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support in terms of the Heinz Nixdorf Study. The HNR study is also supported by the German Ministry of Education and Science (FKZ 01EG940), and the German Research Council (DFG, ER 155/6-1). The authors are supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 7202070 (Human Brain Project SGA1; Katrin Amunts, Sven Cichon). This work was further supported by the German Federal Ministry of Education and Research (BMBF) through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental Disorders) under the auspices of the e:Med Program (grant 01ZX1314A to M.M.N. and S.C.), and by the Swiss National Science Foundation (SNSF, grant 156791 to S.C.). 16p.11.2 European Consortium: B.D. is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant Nr 32003B_159780) and Foundation Synapsis. LREN is very grateful to the Roger De Spoelberch and Partridge Foundations for their generous financial support. This work was supported by grants from the Simons Foundation (SFARI274424) and the Swiss National Science Foundation (31003A_160203) to A.R. and S.J. Betula: The relevant Betula data collection and analyses were supported by a grant from the Knut & Alice Wallenberg (KAW) to L. Nyberg. Brainscale: the Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Dorret Boomsma), 51.02.060 (Hilleke Hulshoff Pol), 668.772 (Dorret Boomsma & Hilleke Hulshoff Pol); NWO/SPI 56-464-14192 (Dorret Boomsma), the European Research Council (ERC-230374) (Dorret Boomsma), High Potential Grant Utrecht University (Hilleke Hulshoff Pol), NWO Brain and Cognition 433-09-220 (Hilleke Hulshoff Pol). Brain Imaging Genetics (BIG): This work makes use of the BIG database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics (www.cognomics.nl), a joint initiative by researchers of the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud university medical centre and the Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative has received supported from the participating departments and centres and from external grants, i.e., the Biobanking and Biomolecular Resources Research Infrastructure (the Netherlands) (BBMRI-NL), the Hersenstichting Nederland, and the Netherlands Organisation for Scientific Research (NWO). The research leading to these results also receives funding from the NWO Gravitation grant ‘Language in Interaction’, the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreements n° 602450 (IMAGEMEND), n°278948 (TACTICS), and n°602805 (Aggressotype) as well as from the European Community’s Horizon 2020 programme under grant agreement n° 643051 (MiND) and from ERC-2010-AdG 268800-NEUROSCHEMA. In addition, the work was supported by a grant for the ENIGMA Consortium (grant number U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. COBRE: This work was supported by a NIH COBRE Phase I grant (1P20RR021938, Lauriello, PI and 2P20GM103472, Calhoun, PI) awarded to the Mind Research Network. We wish to express our gratitude to numerous investigators who were either external consultants to the Cores and projects, mentors on the projects, members of the external advisory committee and members of the internal advisory committee. Decode: The research leading to these results has received financial contribution from the European Union’s Seventh Framework Programme (EU-FP7/2007–2013), EU-FP7 funded grant no. 602450 (IMAGEMEND) as well as support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no.115300 (EUAIMS). DemGene: Norwegian Health Association and Research Council of Norway. Dublin: Work was supported by Science Foundation Ireland (SFI grant 12/IP/1359 to Gary Donohoe and SFI08/IN.1/B1916-Corvin to Aidan C Corvin) and the European Research Council (ERC-StG-2015-677467). EPIGEN-UK (SMS, CL): The work was partly undertaken at UCLH/UCL, which received a proportion of funding from the UK Department of Health’s NIHR Biomedical Research Centres funding scheme. We are grateful to the Wolfson Trust and the Epilepsy Society for supporting the Epilepsy Society MRI scanner, and the Epilepsy Society for supporting CL. Haavik: The work at the K.G.Jebsen center for neuropsychiatric disorders at the University of Bergen, Norway, was supported by Stiftelsen K.G. Jebsen, European Community’s Seventh Framework Program under grant agreement no 602805 and the H2020 Research and Innovation Program under grant agreement numbers 643051 and 667302. HUNT: The HUNT Study is a collaboration between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. IMAGEN: The work received support from the European Union-funded FP6Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), the FP7 projects IMAGEMEND (602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EU-AIMS (115300), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-1, SM 80/7-2, SFB 940/1). Further support was provided by grants from: ANR (project AF12-NEUR0008-01—WM2NA, and ANR-12-SAMA-0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. MCIC: This work was supported primarily by the Department of Energy DE-FG02-99ER62764 through its support of the Mind Research Network and the consortium as well as by the National Association for Research in Schizophrenia and Affective Disorders (NARSAD) Young Investigator Award (to SE) as well as through the Blowitz-Ridgeway and Essel Foundations, and through NWO ZonMw TOP 91211021, the DFG research fellowship (to SE), the Mind Research Network, National Institutes of Health through NCRR 5 month-RR001066 (MGH General Clinical Research Center), NIMH K08 MH068540, the Biomedical Informatics Research Network with NCRR Supplements to P41 RR14075 (MGH), M01 RR 01066 (MGH), NIBIB R01EB006841 (MRN), R01EB005846 (MRN), 2R01 EB000840 (MRN), 1RC1MH089257 (MRN), as well as grant U24 RR021992. NCNG: this sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the K.G. Jebsen Foundation, the Research Council of Norway, to SLH, VMS and TE. The Bergen group was supported by grants from the Western Norway Regional Health Authority (Grant 911593 to AL, Grant 911397 and 911687 to AJL). NESDA: Funding for NESDA was obtained from the Netherlands Organization for Scientific Research (Geestkracht program grant 10-000-1002); the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University’s Institutes for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health.Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by NWO. NTR: The NTR study was supported by the Netherlands Organization for Scientific Research (NWO), MW904-61-193 (Eco de Geus & Dorret Boomsma), MaGW-nr: 400-07- 080 (Dennis van ‘t Ent), MagW 480-04-004 (Dorret Boomsma), NWO/SPI 56-464-14192 (Dorret Boomsma), the European Research Council, ERC-230374 (Dorret Boomsma), and Amsterdam Neuroscience. OATS: OATS (Older Australian Twins Study) was facilitated by access to Twins Research Australia, which is funded by a National Health and Medical Research Council (NHMRC) Enabling Grant 310667. OATS is also supported via a NHMRC/Australian Research Council Strategic Award (401162) and a NHMRC Project Grant (1045325). DNA extraction was performed by Genetic Repositories Australia, which was funded by a NHMRC Enabling Grant (401184). OATS genotyping was partly funded by a Commonwealth Scientific and Industrial Research Organisation Flagship Collaboration Fund Grant. PAFIP: PAFIP data were collected at the Hospital Universitario Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Carlos III Health Institute PIE14/00031 and SAF2013-46292-R and SAF2015-71526-REDT. We wish to acknowledge IDIVAL Neuroimaging Unit for imaging acquirement and analysis.We want to particularly acknowledge the patients and the BioBankValdecilla (PT13/0010/0024) integrated in the Spanish National Biobanks Network for its collaboration. QTIM: The QTIM study was supported by grants from the US National Institute of Child Health and Human Development (R01 HD050735) and the Australian National Health and Medical Research Council (NHMRC) (486682, 1009064). Genotyping was supported by NHMRC (389875). Lachlan Strike is supported by an Australian Postgraduate Award (APA). AFM is supported by NHMRC CDF 1083656. We thank the twins and siblings for their participation, the many research assistants, as well as the radiographers, for their contribution to data collection and processing of the samples. SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, 01ZZ0403 and 01ZZ0701), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg- West Pomerania. Whole-body MR imaging was supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. StrokeMRI: StrokeMRI has been supported by the Research Council of Norway (249795), the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073) and the Norwegian ExtraFoundation for Health and Rehabilitation. TOP: TOP is supported by the Research Council of Norway (223273, 213837, 249711), the South East Norway Health Authority (2017-112), the Kristian Gerhard Jebsen Stiftelsen (SKGJ‐MED‐008) and the European Community’s Seventh Framework Programme (FP7/2007–2013), grant agreement no. 602450 (IMAGEMEND). We acknowledge the technical support and service from the Genomics Core Facility at the Department of Clinical Science, the University of Bergen

This work was supported by funding from the European Community’s Seventh Framework Programme under grant agreement No. HEALTH-F2-2010-241909 (Project EU-GEI). (...) CA was supported by the Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), Fundación Familia Alonso and Fundación Alicia Koplowitz. MB was supported by the Ministry of Economy and Competitivity (PI08/0208; PI11/00325; PI14/00612), Instituto de Salud Carlos III – ERDF Funds from the European Commission, ‘A way of making Europe’, CIBERSAM, by the CERCA Programme/Generalitat de Catalunya and Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement (2017SGR1355). Departament de Salut de la Generalitat de Catalunya, en la convocatoria corresponent a l’any 2017 de concessió de subvencions del PERIS 2016-2020, modalitat Projectes de recerca orientats a l’atenció primària, amb el codi d’expedient SLT006/17/00345; and grateful for the support of the Institut de Neurociències, Universitat de Barcelona., Rodriguez V., Alameda L., Quattrone D., Tripoli G., Gayer-Anderson C., Spinazzola E., Trotta G., Jongsma H.E., Stilo S., La Cascia C., Ferraro L., La Barbera D., Lasalvia A., Tosato S., Tarricone I., Bonora E., Jamain S., Selten J.-P., Velthorst E., De Haan L., Llorca P.-M., Arrojo M., Bobes J., Bernardo M., Arango C., Kirkbride J., Jones P.B., Rutten B.P., Richards A., Sham P.C., O'Donovan M., Van Os J., Morgan C., Di Forti M., Murray R.M., Vassos E.

BackgroundMood disorders are characterised by pronounced symptom heterogeneity, which presents a substantial challenge both to clinical practice and research. Identification of subgroups of individuals with homogeneous symptom profiles that cut across current diagnostic categories could provide insights in to the transdiagnostic relevance of individual symptoms, which current categorical diagnostic systems cannot impart.AimsTo identify groups of people with homogeneous clinical characteristics, using symptoms of manic and/or irritable mood, and explore differences between groups in diagnoses, functional outcomes and genetic liability.MethodWe used latent class analysis on eight binary self-reported symptoms of manic and irritable mood in the UK Biobank and PROTECT studies, to investigate how individuals formed latent subgroups. We tested associations between the latent classes and diagnoses of psychiatric disorders, sociodemographic characteristics and polygenic risk scores.ResultsFive latent classes were derived in UK Biobank (N = 42 183) and were replicated in the independent PROTECT cohort (N = 4445), including ‘minimally affected’, ‘inactive restless’, active restless’, ‘focused creative’ and ‘extensively affected’ individuals. These classes differed in disorder risk, polygenic risk score and functional outcomes. One class that experienced disruptive episodes of mostly irritable mood largely comprised cases of depression/anxiety, and a class of individuals with increased confidence/creativity reported comparatively lower disruptiveness and functional impairment.ConclusionsFindings suggest that data-driven investigations of psychopathological symptoms that include sub-diagnostic threshold conditions can complement research of clinical diagnoses. Improved classification systems of psychopathology could investigate a weighted approach to symptoms, toward a more dimensional classification of mood disorders.

Ursini et al reported recently that the liability of schizophrenia explained by a polygenic risk score (PRS) derived from the variants most associated with schizophrenia was increased 5-fold in individuals who experienced complications during pregnancy or birth. Follow-up gene expression analysis showed that the genes mapping to the most associated genetic variants are highly expressed in placental tissues. If confirmed, these findings will have major implications in our understanding of the joint effect of genes and environment in the pathogenesis of schizophrenia. We examined the interplay between PRS and obstetric complications (OCs) in 5 independent samples (effective N = 2110). OCs were assessed with the full or modified Lewis-Murray scale, or with birth weight < 2.5 kg as a proxy. In a large cohort we tested whether the pathways from placenta-relevant variants in the original report were associated with case-control status. Unlike in the original study, we did not find significant effect of PRS on the presence of OCs in cases, nor a substantial difference in the association of PRS with case-control status in samples stratified by the presence of OCs. Furthermore, none of the PRS by OCs interactions were significant, nor were any of the biological pathways, examined in the Swedish cohort. Our study could not support the hypothesis of a mediating effect of placenta biology in the pathway from genes to schizophrenia. Methodology differences, in particular the different scales measuring OCs, as well as power constraints for interaction analyses in both studies, may explain this discrepancy.

IMPORTANCE: Urban residence has been highlighted as an environmental risk factor for schizophrenia and, to a lesser extent, several other psychiatric disorders. However, few studies have explored genetic effects on the choice of residence. OBJECTIVE: To investigate whether individuals with genetic predisposition to a range of psychiatric disorders have an increased likelihood to live in urban areas. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional retrospective cohort study including genotypes, address history, and geographic distribution of population density in the UK based on census data from 1931-2011 was conducted. Polygenic risk score (PRS) analyses, genome-wide association studies, genetic correlation, and 2-sample mendelian randomization analyses were applied to 385 793 UK Biobank participants with self-reported or general practitioner registration–based address history. The study was conducted from February 2018 to May 2021, and data analysis was performed from April 2018 to May 2021. MAIN OUTCOMES AND MEASURES: Population density of residence at different ages and movement during the life span between urban and rural environments. RESULTS: In this cohort study of 385 793 unrelated UK Biobank participants (207 963 [54%] were women; age, 37-73 years; mean [SD], 56.7 [8] years), PRS analyses showed significant associations with higher population density across adult life (age 25 to >65 years) reaching highest significance at the 45- to 55-year age group for schizophrenia (88 people/km(2); 95% CI, 65-98 people/km(2)), bipolar disorder (44 people/km(2); 95% CI, 34-54 people/km(2)), anorexia nervosa (36 people/km(2); 95% CI, 22-50 people/km(2)), and autism spectrum disorder (35 people/km(2); 95% CI, 25-45 people/km(2)). The schizophrenia PRS was also significantly associated with higher birthplace population density (37 people/km(2); 95% CI, 19-55 people/km(2); P = 8 × 10(−5)). Attention-deficit/hyperactivity disorder PRS was significantly associated with reduced population density in adult life (−31 people/km(2); 95% CI, −42 to −20 people/km(2) at age 35-45 years). Individuals with higher PRS for schizophrenia, bipolar disorder, anorexia nervosa, and autism spectrum disorder and lower PRS for attention-deficit/hyperactivity disorder preferentially moved from rural environments to cities (difference in PRS with Tukey pairwise comparisons for schizophrenia: 0.05; 95% CI, 0.03 to 0.60; bipolar disorder: 0.10; 95% CI, 0.08 to 0.13; anorexia nervosa: 0.05; 95% CI, 0.03 to 0.07; autism spectrum disorder: 0.04; 95% CI 0.03 to 0.06; and attention-deficit/hyperactivity disorder: −0.09, 95% CI, −0.12 to −0.06). Genetic correlation results were largely consistent with PRS analyses, whereas mendelian randomization provided support for associations between schizophrenia and bipolar disorder and living in high population-density areas. CONCLUSIONS AND RELEVANCE: These findings suggest that a high genetic risk for a variety of psychiatric disorders may affect an individual’s choice of residence. This result supports the hypothesis of genetic selection of an individual’s environment, which intersects the traditional gene-environment dichotomy.

Premorbid functioning and cognitive measures may reflect gradients of developmental impairment across diagnostic categories in psychosis. In this study, we sought to examine the associations of current cognition and premorbid adjustment with symptom dimensions in a large first episode psychosis (FEP) sample. We used data from the international EU-GEI study. Bifactor modelling of the Operational Criteria in Studies of Psychotic Illness (OPCRIT) ratings provided general and specific symptom dimension scores. Premorbid Adjustment Scale estimated premorbid social (PSF) and academic adjustment (PAF), and WAIS-brief version measured IQ. A MANCOVA model examined the relationship between symptom dimensions and PSF, PAF, and IQ, having age, sex, country, self-ascribed ethnicity and frequency of cannabis use as confounders. In 785 patients, better PSF was associated with fewer negative (B=-0.12, 95% C.I. -0.18, -0.06, p

Intelligent reflection surfaces (IRS) facilitate wireless environments by increasing spectrum and energy efficiencies. IRS will be considered a key element in 5G and Beyond cellular networks. IRS design is based on the unit cells. In this paper, we present the design of unit cells based on transparent materials at millimetre-wave frequencies. The transparency of the surface is achieved by using materials such as Indium tin oxide (ITO) and quartz. Simulations have been carried out using CST microwave studio to evaluate the reflection characteristics of the proposed unit cells. Simulations suggest a maximum shift in the reflection phase up to 336° for variation in the dimensions of the unit cell with low reflection losses at 60GHz for 5G and Beyond Wireless Networks.

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well.

BackgroundEpisodic changes in mood characterise disorders such as bipolar disorder, which includes distinct periods of manic excitability or irritability, along with additional symptoms experienced during these periods. Common clinical understanding informs diagnostic criteria and epidemiological studies reflect clinical thresholds.AimsTo use a data-driven approach to defining groupings of symptoms experienced during periods of manic or irritable mood, which could inform understanding of mood disorders and guide case classification by identifying subgroups with homogeneous clinical/functional outcomes.MethodsWe used latent class analysis (LCA) to conduct an exploration of the latent structure in symptom responses in the UK Biobank and PROTECT studies, by investigating how symptoms, experienced during periods of manic or irritable mood, formed latent subgroups. We tested associations of latent subgroups with sociodemographic characteristics, diagnoses of psychiatric disorders and polygenic risk scores (PRS).ResultsFive latent classes were identified that captured patterns of symptoms experienced during periods of manic or irritable mood (N=42,183) in UK Biobank. We identified one class that experienced disruptive episodes of mostly irritable mood that was largely comprised of cases of depression/anxiety, and a class of individuals with increased confidence/creativity that reported lower disruptiveness and lower functional impairment. The five latent classes were replicated in an independent cohort, the PROTECT study (N=4,445), with similar distinctions between classes.ConclusionOur data-driven approach to grouping individuals identified distinct latent classes. A dimensional classification of mood disorders informed by our findings will be able to better assess or subtype these disorders in future studies.