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78. Preface

Author

Wiersma D. S., Wagner F., and Cifarelli L.

Subjects
Physics, QC1-999
Published
2013
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79. Complex photonic structures for energy efficiency

Author

Wiersma D. S. and Burresi M.

Subjects
Physics, QC1-999
Abstract

Photonic structures are playing an increasingly important role in energy efficiency. In particular, they can help to control the flow of light and improve the optical properties of photovoltaic solar cells. We will explain the physics of light transport in such structures with a special focus on disordered materials.

Published
2013
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80. Recovery from psychotic illness: a 15- and 25-year international follow-up study

Author

HARRISON, G., HOPPER, K., CRAIG, T., LASKA, E., SIEGEL, C., WANDERLING, J., DUBE, K. C., GANEV, K., GIEL, R., AN DER HEIDEN, W., HOLMBERG, S. K., JANCA, A., LEE, P. W. H., LEÓN, C. A., MALHOTRA, S., MARSELLA, A. J., NAKANE, Y., SARTORIUS, N., SHEN, Y., SKODA, C., THARA, R., TSIRKIN, S. J., VARMA, V. K., WALSH, D., and WIERSMA, D.

Published
2001

82. The prevalence of visual hallucinations in non-affective psychosis, and the role of perception and attention

Author

Van Ommen, M. M., Van Beilen, M., Cornelissen, F. W., Smid, H. G O M, Knegtering, H., Aleman, A., Van Laar, T., Bruggeman, R., Cahn, W., De Haan, L., Kahn, R. S., Meijer, C. J., Myin-Germeys, I., Van Os, J., Wiersma, D., Perceptual and Cognitive Neuroscience (PCN), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Clinical Neuropsychology, Clinical Cognitive Neuropsychiatry Research Program (CCNP), and Movement Disorder (MD)

Subjects
Male, Visual perception, SAMPLE, Databases, Factual, Hallucinations, INFORMATION, Audiology, Neuropsychological Tests, 0302 clinical medicine, Cognition, Face perception, psychotic disorders, Attention, Young adult, SCHIZOPHRENIC-PATIENTS, Applied Psychology, media_common, Netherlands, GENERAL-POPULATION, PARKINSON-DISEASE DEMENTIA, visual hallucinations, PAD model, LEWY BODIES, Visual Hallucination, Psychiatry and Mental health, Visual Perception, Female, Psychology, Facial Recognition, Adult, medicine.medical_specialty, Elementary cognitive task, Psychosis, media_common.quotation_subject, prevalence, 03 medical and health sciences, Young Adult, Perception, Journal Article, medicine, Humans, Psychiatry, Retrospective Studies, AUDITORY HALLUCINATIONS, RECOGNITION, medicine.disease, 030227 psychiatry, MODEL, Case-Control Studies, RISK-FACTORS, 030217 neurology & neurosurgery
Abstract

BackgroundLittle is known about visual hallucinations (VH) in psychosis. We investigated the prevalence and the role of bottom-up and top-down processing in VH. The prevailing view is that VH are probably related to altered top-down processing, rather than to distorted bottom-up processing. Conversely, VH in Parkinson's disease are associated with impaired visual perception and attention, as proposed by the Perception and Attention Deficit (PAD) model. Auditory hallucinations (AH) in psychosis, however, are thought to be related to increased attention.MethodOur retrospective database study included 1119 patients with non-affective psychosis and 586 controls. The Community Assessment of Psychic Experiences established the VH rate. Scores on visual perception tests [Degraded Facial Affect Recognition (DFAR), Benton Facial Recognition Task] and attention tests [Response Set-shifting Task, Continuous Performance Test-HQ (CPT-HQ)] were compared between 75 VH patients, 706 non-VH patients and 485 non-VH controls.ResultsThe lifetime VH rate was 37%. The patient groups performed similarly on cognitive tasks; both groups showed worse perception (DFAR) than controls. Non-VH patients showed worse attention (CPT-HQ) than controls, whereas VH patients did not perform differently.ConclusionsWe did not find significant VH-related impairments in bottom-up processing or direct top-down alterations. However, the results suggest a relatively spared attentional performance in VH patients, whereas face perception and processing speed were equally impaired in both patient groups relative to controls. This would match better with the increased attention hypothesis than with the PAD model. Our finding that VH frequently co-occur with AH may support an increased attention-induced ‘hallucination proneness’.

Published
2016

86. Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Author

Maier, R. M., Zhu, Z., Lee, S. H., Trzaskowski, M., Ruderfer, D. M., Stahl, E. A., Ripke, S., Wray, N. R., Yang, J., Visscher, P. M., Robinson, M. R., Forstner, A. J., Mcquillin, A., Trubetskoy, V., Wang, W., Wang, Y., Coleman, J. R. I., Gaspar, H. A., Leeuw, C. A., Whitehead Pavlides, J. M., Olde Loohuis, L. M., Pers, T. H., Lee, P. H., Charney, A. W., Dobbyn, A. L., Huckins, L., Boocock, J., Giambartolomei, C., Roussos, P., Mullins, N., Awasthi, S., Agerbo, E., Als, T. D., Pedersen, C. B., Grove, J., Kupka, R., Regeer, E. J., Anjorin, A., Casas, M., Mahon, P. B., Allardyce, J., Escott-Price, V., Forty, L., Fraser, C., Kogevinas, M., Frank, J., Streit, F., Strohmaier, J., Treutlein, J., Witt, S. H., Kennedy, J. L., Strauss, J. S., Garnham, J., O Donovan, C., Slaney, C., Steinberg, S., Thorgeirsson, T. E., Hautzinger, M., Steffens, M., Perlis, R. H., Sánchez-Mora, C., Hipolito, M., Lawson, W. B., Nwulia, E. A., Levy, S. E., Foroud, T. M., Jamain, S., Young, A. H., Mckay, J. D., Albani, D., Zandi, P., Potash, J. B., Zhang, P., Raymond Depaulo, J., Bergen, S. E., Juréus, A., Karlsson, R., Kandaswamy, R., Mcguffin, P., Rivera, M., Lissowska, J., Cruceanu, C., Lucae, S., Cervantes, P., Budde, M., Gade, K., Heilbronner, U., Pedersen, M. G., Morris, D. W., Weickert, C. S., Weickert, T. W., Macintyre, D. J., Lawrence, J., Elvsåshagen, T., Smeland, O. B., Djurovic, S., Xi, S., Green, E. K., Czerski, P. M., Hauser, J., Xu, W., Vedder, H., Oruc, L., Spijker, A. T., Gordon, S. D., Medland, S. E., Curtis, D., Mühleisen, T. W., Badner, J. A., Scheftner, W. A., Sigurdsson, E., Schork, N. J., Schatzberg, A. F., Bækvad-Hansen, M., Bybjerg-Grauholm, J., Hansen, C. S., Knowles, J. A., Szelinger, S., Montgomery, G. W., Boks, M., Adolfsson, A. N., Hoffmann, P., Bauer, M., Pfennig, A., Leber, M., Kittel-Schneider, S., Reif, A., Del-Favero, J., Fischer, S. B., Herms, S., Reinbold, C. S., Degenhardt, F., Koller, A. C., Maaser, A., Ori, A. P. S., Dale, A. M., Fan, C. C., Greenwood, T. A., Nievergelt, C. M., Shehktman, T., Shilling, P. D., Byerley, W., Bunney, W., Alliey-Rodriguez, N., Clarke, T. K., Liu, C., Coryell, W., Akil, H., Burmeister, M., Flickinger, M., Li, J. Z., Mcinnis, M. G., Meng, F., Thompson, R. C., Watson, S. J., Zollner, S., Guan, W., Green, M. J., Craig, D., Sobell, J. L., Milani, L., Gordon-Smith, Katherine, Knott, Sarah, Perry, Amy, Parra, J. G., Mayoral, F., Rivas, F., Rice, J. P., Barchas, J. D., Børglum, A. D., Mortensen, P. B., Mors, O., Grigoroiu-Serbanescu, M., Bellivier, F., Etain, B., Leboyer, M., Ramos-Quiroga, J. A., Agartz, I., Amin, F., Azevedo, M. H., Bass, N., Black, D. W., Blackwood, D. H. R., Bruggeman, R., Buccola, N. G., Choudhury, K., Cloninger, C. R., Corvin, A., Craddock, N., Daly, M. J., Datta, S., Donohoe, G. J., Duan, J., Dudbridge, F., Fanous, A., Freedman, R., Freimer, N. B., Friedl, M., Gill, M., Gurling, H., Haan, L., Hamshere, M. L., Hartmann, A. M., Holmans, P. A., Kahn, R. S., Keller, M. C., Kenny, E., Kirov, G. K., Krabbendam, L., Krasucki, R., Lencz, T., Levinson, D. F., Lieberman, J. A., Lin, D. -Y, Linszen, D. H., Magnusson, P. K. E., Maier, W., Malhotra, A. K., Mattheisen, M., Mattingsdal, M., Mccarroll, S. A., Medeiros, H., Melle, I., Milanova, V., Myin-Germeys, I., Neale, B. M., Ophoff, R. A., Owen, M. J., Pimm, J., Purcell, S. M., Puri, V., Digby Quested, Rossin, L., Sanders, A. R., Shi, J., Sklar, P., St Clair, D., Stroup, T. S., Os, J., Wiersma, D., Zammit, S., Maier, Robert M, Zhu, Zhihong, Lee, Sang Hong, Trzaskowski, Maciej, Ruderfer, Douglas M, Stahl, Eli A, Ripke, Stephan, Wray, Naomi R, Yang, Jian, Visscher, Peter M, Robinson, Matthew R, Bipolar Disorder Working Grp Psy, Schizophrenia Working Grp Psychiat, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, APH - Mental Health, ANS - Complex Trait Genetics, Adult Psychiatry, and Psychiatry

Subjects
0301 basic medicine, Bipolar Disorder, Chemistry(all), Science, General Physics and Astronomy, Genomics, Genome-wide association study, Computational biology, Biology, Physics and Astronomy(all), Risk Assessment, Biochemistry, General Biochemistry, Genetics and Molecular Biology, Article, predictive medicine, quantitative trait, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, Pleiotropy, Genetic Pleiotropy, Humans, Genetic Predisposition to Disease, lcsh:Science, Multidisciplinary, Models, Statistical, Bipolar Disorder/genetics, Genome-Wide Association Study, Schizophrenia/genetics, Biochemistry, Genetics and Molecular Biology(all), General Chemistry, Precision medicine, R1, Biobank, 3. Good health, genome wide association studies, 030104 developmental biology, Trait, Schizophrenia, statistical methods, lcsh:Q, Risk assessment, 030217 neurology & neurosurgery, Genetics and Molecular Biology(all)
Abstract

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait., Genetic prediction of complex traits so far has limited accuracy because of insufficient understanding of the genetic risk. Here, Maier et al. develop an improved method for trait prediction that makes use of genetic correlations between traits and apply it to summary statistics of psychiatric diseases.

Published
2018
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95. Variability in Working Memory Performance Explained by Epistasis vs Polygenic Scores in the ZNF804A Pathway

Author

Nicodemus, Kk, Hargreaves, A, Morris, D, Anney, R, Gill, M, Corvin, A, Donohoe, G, Ripke, S, Sanders, Ar, Kendler, Ks, Levinson, Df, Sklar, P, Holmans, Pa, Lin, Dy, Duan, J, Ophoff, Ra, Andreassen, Oa, Scolnick, E, Cichon, S, Clair, St, D, Gurling, H, Werge, T, Rujescu, D, Blackwood, Dh, Pato, Cn, Malhotra, Ak, Purcell, S, Dudbridge, F, Neale, Bm, Rossin, L, Visscher, Pm, Posthuma, D, Ruderfer, Dm, Fanous, A, Stefansson, H, Steinberg, S, Mowry, Bj, Golimbet, V, Hert, De, M, Jönsson, Eg, Bitter, I, Pietiläinen, Op, Collier, Da, Tosato, Sarah, Agartz, I, Albus, M, Alexander, M, Amdur, Rl, Amin, F, Bass, N, Bergen, Se, Black, Dw, Børglum, Ad, Brown, Ma, Bruggeman, R, Buccola, Ng, Byerley, Wf, Cahn, W, Cantor, Rm, Carr, Vj, Catts, Sv, Choudhury, K, Cloninger, C, Cormican, P, Craddock, N, Danoy, Pa, Datta, S, Haan, De, L, Demontis, D, Dikeos, D, Djurovic, S, Donnelly, P, Duong, L, Dwyer, S, Fink Jensen, A, Freedman, R, Freimer, Nb, Friedl, M, Georgieva, L, Giegling, I, Glenthøj, B, Godard, S, Hamshere, M, Hansen, M, Hansen, T, Hartmann, Am, Henskens, Fa, Hougaard, Dm, Hultman, Cm, Ingason, A, Jablensky, Av, Jakobsen, Kd, Jay, M, Jürgens, G, Kahn, Rs, Keller, Mc, Kenis, G, Kenny, E, Kim, Y, Kirov, Gk, Konnerth, H, Konte, B, Krabbendam, L, Krasucki, R, Lasseter, Vk, Laurent, C, Lawrence, J, Lencz, T, Lerer, F, Liang, Ky, Lichtenstein, P, Lieberman, Ja, Linszen, Dh, Lönnqvist, J, Loughland, Cm, Maclean, Aw, Maher, Bs, Maier, W, Mallet, J, Malloy, P, Mattheisen, M, Mattingsdal, M, Mcghee, Ka, Mcgrath, Jj, Mcintosh, A, Mclean, De, Mcquillin, A, Melle, I, Michie, Pt, Milanova, V, Morris, Dw, Mors, O, Mortensen, Pb, Moskvina, V, Muglia, P, Myin Germeys, I, Nertney, Da, Nestadt, G, Nielsen, J, Nikolov, I, Nordentoft, M, Norton, N, Nöthen, Mm, O'Dushlaine, Ct, Olincy, A, Olsen, L, O'Neill, F, Ørntoft, Tf, Owen, Mj, Pantelis, C, Papadimitriou, G, Pato, Mt, Peltonen, L, Petursson, H, Pickard, B, Pimm, J, Pulver, Ae, Puri, V, Quested, D, Quinn, Em, Rasmussen, Hb, Réthelyi, Jm, Ribble, R, Rietschel, M, Riley, Bp, Ruggeri, Mirella, Schall, U, Schulze, Tg, Schwab, Sg, Scott, Rj, Shi, J, Sigurdsson, E, Silverman, Jm, Spencer, Cc, Stefansson, K, Strange, A, Strengman, E, Stroup, T, Suvisaari, J, Terenius, L, Thirumalai, S, Thygesen, Jh, Timm, S, Toncheva, D, Van, Den, Oord, E, Van, Os, Van, J, Winkel, R, Veldink, J, Walsh, D, Wang, Ag, Wiersma, D, Wildenauer, Db, Williams, Hj, Williams, Nm, Wormley, B, Zammit, S, Sullivan, Pf, O'Donovan, Mc, Daly, Mj, Gejman, P., Functional Genomics, Complex Trait Genetics, De Hert, Marc, Myin-Germeys, Inez, van Winkel, Ruud, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, Amsterdam Neuroscience, and Adult Psychiatry

Subjects
epistasis, Male, Multifactorial Inheritance, polygenic, Neuropsychological Tests, involvement, 0302 clinical medicine, Neural Pathways, 2.1 Biological and endogenous factors, Psychology, schizophrenia risk, psychosis variant, Aetiology, 0303 health sciences, susceptibility gene znf804a, medicine.diagnostic_test, phenotypes, Zinc Fingers, Neuropsychological test, Single Nucleotide, Middle Aged, Serious Mental Illness, Psychiatry and Mental health, Memory, Short-Term, Mental Health, Schizophrenia, Major depressive disorder, Female, Cognitive Sciences, social and economic factors, Adult, medicine.medical_specialty, Psychosis, working memory, schizophrenia, IQ, Schizoaffective disorder, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Gene interaction, Genetic, Social cognition, Memory, Clinical Research, 2.3 Psychological, Behavioral and Social Science, medicine, Genetics, Humans, Genetic Predisposition to Disease, Bipolar disorder, Polymorphism, Psychiatry, 030304 developmental biology, Other Medical and Health Sciences, Prevention, Wellcome Trust Case Control Consortium 2, Neurosciences, Genetic Variation, Epistasis, Genetic, Schizophrenia Psychiatric Genome-wide Association Study (GWAS) Consortium, medicine.disease, attention, Brain Disorders, deficits, Short-Term, Psychotic Disorders, genome-wide association, Epistasis, healthy controls, identification, Cognition Disorders, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

IMPORTANCE We investigated the variation in neuropsychological function explained by risk alleles at the psychosis susceptibility gene ZNF804A and its interacting partners using single nucleotide polymorphisms (SNPs), polygenic scores, and epistatic analyses. Of particular importance was the relative contribution of the polygenic score vs epistasis in variation explained. OBJECTIVES To (1) assess the association between SNPs in ZNF804A and the ZNF804A polygenic score with measures of cognition in cases with psychosis and (2) assess whether epistasis within the ZNF804A pathway could explain additional variation above and beyond that explained by the polygenic score. DESIGN, SETTING, AND PARTICIPANTS Patients with psychosis (n = 424)were assessed in areas of cognitive ability impaired in schizophrenia including IQ, memory, attention, and social cognition.We used the Psychiatric GWAS Consortium 1 schizophrenia genome-wide association study to calculate a polygenic score based on identified risk variants within this genetic pathway. Cognitive measures significantly associated with the polygenic score were tested for an epistatic component using a training set (n = 170), which was used to develop linear regression models containing the polygenic score and 2-SNP interactions. The best-fitting models were tested for replication in 2 independent test sets of cases: (1) 170 individuals with schizophrenia or schizoaffective disorder and (2) 84 patients with broad psychosis (including bipolar disorder, major depressive disorder, and other psychosis). MAIN OUTCOMES AND MEASURES Participants completed a neuropsychological assessment battery designed to target the cognitive deficits of schizophrenia including general cognitive function, episodic memory, working memory, attentional control, and social cognition. RESULTS Higher polygenic scores were associated with poorer performance among patients on IQ, memory, and social cognition, explaining 1%to 3%of variation on these scores (range, P = .01 to .03). Using a narrow psychosis training set and independent test sets of narrow phenotype psychosis (schizophrenia and schizoaffective disorder), broad psychosis, and control participants (n = 89), the addition of 2 interaction terms containing 2 SNPs each increased the R2 for spatial working memory strategy in the independent psychosis test sets from 1.2%using the polygenic score only to 4.8%(P = .11 and .001, respectively) but did not explain additional variation in control participants. CONCLUSIONS AND RELEVANCE These data support a role for the ZNF804A pathway in IQ, memory, and social cognition in cases. Furthermore, we showed that epistasis increases the variation explained above the contribution of the polygenic score. © 2014 American Medical Association. All rights reserved.

Published
2014
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96. The Groningen Social Disabilities Schedule: Development, Relationship with I.C.I.D.H., and Psychometric Properties.

Author

Wiersma, D.

Abstract

The Groningen Social Disabilities Schedule for the assessment of social dysfunctioning of psychiatric patients is described. The measure is based on social role theory and the hierarchy in social disabilities as well as compatibility with the International Classification of Impairments. The semistructured measure allows for posing supplementary questions. Interrater reliability is good. (Author/DB)

Published
1988

97. Cognitive subtypes in non-affected siblings of schizophrenia patients: characteristics and profile congruency with affected family members

Author

Quee, P.J., Alizadeh, BZ, Aleman, A., van den Heuvel, E.R., Bruggeman, R., Cahn, W., de Haan, L., Kahn, R., Krabbendam, L., Linzen, D., Myin-Germeys, I., van Os, J, Wiersma, D., Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, Educational Neuroscience, LEARN! - Social cognition and learning, LEARN! - Brain, learning and development, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Life Course Epidemiology (LCE), Clinical Neuropsychology, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Adult Psychiatry, and ANS - Amsterdam Neuroscience

Subjects
DISORDER, Adult, Male, Proband, SAMPLE, Adolescent, Endophenotypes, neuropsychology, RELATIVES, GROUP, Clustering, Developmental psychology, NEUROCOGNITIVE DEFICITS, Young Adult, SYNDROME SCALE PANSS, Cognition, HISTORY, medicine, Cluster Analysis, Humans, Sibling, METAANALYSIS, Applied Psychology, Siblings, Neuropsychology, methodology, Middle Aged, medicine.disease, Cognitive test, Psychiatry and Mental health, Schizophrenia, Endophenotype, SDG 1 - No Poverty, Female, SUSTAINED ATTENTION, Cognition Disorders, Psychology, Neurocognitive, Clinical psychology
Abstract

BackgroundAlthough cognitive subtypes have been suggested in schizophrenia patients, similar analyses have not been carried out in their non-affected siblings. Subtype classification may provide more insight into genetically driven variation in cognitive function. We investigated cognitive subtypes in siblings.MethodCluster analyses were performed in 654 non-affected siblings, on a cognitive battery that included tests of attention, intellectual function and episodic memory. Resulting subtypes in the siblings were analyzed for cognitive, demographic and clinical characteristics and compared with those of their probands.ResultsThree sibling subtypes of cognitive function were distinguished: ‘normal’, ‘mixed’ and ‘impaired’. Normal profile siblings (n = 192) were unimpaired on cognitive tests, in contrast to their proband (n = 184). Mixed profile siblings (n = 228) and their probands (n = 222) had a more similar performance pattern. Impaired profile siblings had poorer functional outcomes (n = 234) and their profile was almost identical to that of their proband (n = 223). Probands with cognitively impaired siblings could be distinguished from other schizophrenia patients by their own cognitive performance. They also had poorer clinical characteristics, including achievement of symptomatic remission.ConclusionsUnaffected siblings of patients with schizophrenia are heterogeneous with respect to cognitive function. The poorer the cognitive profile of the sibling, the higher the level of correspondence with the proband. The sibling's cognitive subtype was predictive for disease course in the proband. Distinguishing cognitive subtypes of unaffected siblings may be of relevance for genetic studies.

Published
2013
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