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1. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.

Author

Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Krämer, B, Overs, B, Hartberg, CB, Abé, C, Dima, D, Grotegerd, D, Sprooten, E, Bøen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsåshagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjó-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landén, M, and Lawrence, NS

Subjects
Brain, Cerebral Cortex, Frontal Lobe, Prefrontal Cortex, Temporal Lobe, Humans, Magnetic Resonance Imaging, Case-Control Studies, Bipolar Disorder, Psychotic Disorders, Age Factors, Sex Factors, Adolescent, Adult, Middle Aged, Female, Male, Young Adult, Neuroimaging, Gray Matter, Clinical Research, Serious Mental Illness, Biomedical Imaging, Brain Disorders, Mental Health, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Published
2018

2. Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis

Author

Wise, T, Radua, J, Via, E, Cardoner, N, Abe, O, Adams, TM, Amico, F, Cheng, Y, Cole, JH, de Azevedo Marques Périco, C, Dickstein, DP, Farrow, TFD, Frodl, T, Wagner, G, Gotlib, IH, Gruber, O, Ham, BJ, Job, DE, Kempton, MJ, Kim, MJ, Koolschijn, PCMP, Malhi, GS, Mataix-Cols, D, McIntosh, AM, Nugent, AC, O'Brien, JT, Pezzoli, S, Phillips, ML, Sachdev, PS, Salvadore, G, Selvaraj, S, Stanfield, AC, Thomas, AJ, van Tol, MJ, van der Wee, NJA, Veltman, DJ, Young, AH, Fu, CH, Cleare, AJ, and Arnone, D

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Biomedical Imaging, Brain Disorders, Neurosciences, Mental Health, Serious Mental Illness, Major Depressive Disorder, Depression, Mental health, Adult, Bipolar Disorder, Brain, Case-Control Studies, Depressive Disorder, Major, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Neuroimaging, Prefrontal Cortex, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression (MDD) and bipolar disorder (BD) are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore, the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry (VBM) studies in MDD and BD. We identified studies published up to January 2015 that compared grey matter in MDD (50 data sets including 4101 individuals) and BD (36 data sets including 2407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey-matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex and bilateral insula. Group comparisons indicated that findings of smaller grey-matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions were more substantial in major depression. These results suggest that MDD and BD are characterised by both common and distinct patterns of grey-matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.

Published
2017

3. Genome-wide association study of borderline personality disorder reveals genetic overlap with bipolar disorder, major depression and schizophrenia.

Author

Witt, SH, Streit, F, Jungkunz, M, Frank, J, Awasthi, S, Reinbold, CS, Treutlein, J, Degenhardt, F, Forstner, AJ, Heilmann-Heimbach, S, Dietl, L, Schwarze, CE, Schendel, D, Strohmaier, J, Abdellaoui, A, Adolfsson, R, Air, TM, Akil, H, Alda, M, Alliey-Rodriguez, N, Andreassen, OA, Babadjanova, G, Bass, NJ, Bauer, M, Baune, BT, Bellivier, F, Bergen, S, Bethell, A, Biernacka, JM, Blackwood, DHR, Boks, MP, Boomsma, DI, Børglum, AD, Borrmann-Hassenbach, M, Brennan, P, Budde, M, Buttenschøn, HN, Byrne, EM, Cervantes, P, Clarke, T-K, Craddock, N, Cruceanu, C, Curtis, D, Czerski, PM, Dannlowski, U, Davis, T, de Geus, EJC, Di Florio, A, Djurovic, S, Domenici, E, Edenberg, HJ, Etain, B, Fischer, SB, Forty, L, Fraser, C, Frye, MA, Fullerton, JM, Gade, K, Gershon, ES, Giegling, I, Gordon, SD, Gordon-Smith, K, Grabe, HJ, Green, EK, Greenwood, TA, Grigoroiu-Serbanescu, M, Guzman-Parra, J, Hall, LS, Hamshere, M, Hauser, J, Hautzinger, M, Heilbronner, U, Herms, S, Hitturlingappa, S, Hoffmann, P, Holmans, P, Hottenga, J-J, Jamain, S, Jones, I, Jones, LA, Juréus, A, Kahn, RS, Kammerer-Ciernioch, J, Kirov, G, Kittel-Schneider, S, Kloiber, S, Knott, SV, Kogevinas, M, Landén, M, Leber, M, Leboyer, M, Li, QS, Lissowska, J, Lucae, S, Martin, NG, Mayoral-Cleries, F, McElroy, SL, McIntosh, AM, McKay, JD, and McQuillin, A

Subjects
Bipolar Disorders Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Humans, Genetic Predisposition to Disease, Case-Control Studies, Bipolar Disorder, Depressive Disorder, Major, Borderline Personality Disorder, Schizophrenia, Genotype, Multifactorial Inheritance, Adolescent, Adult, Aged, Middle Aged, Female, Male, Genome-Wide Association Study, Young Adult, Depressive Disorder, Major, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Borderline personality disorder (BOR) is determined by environmental and genetic factors, and characterized by affective instability and impulsivity, diagnostic symptoms also observed in manic phases of bipolar disorder (BIP). Up to 20% of BIP patients show comorbidity with BOR. This report describes the first case-control genome-wide association study (GWAS) of BOR, performed in one of the largest BOR patient samples worldwide. The focus of our analysis was (i) to detect genes and gene sets involved in BOR and (ii) to investigate the genetic overlap with BIP. As there is considerable genetic overlap between BIP, major depression (MDD) and schizophrenia (SCZ) and a high comorbidity of BOR and MDD, we also analyzed the genetic overlap of BOR with SCZ and MDD. GWAS, gene-based tests and gene-set analyses were performed in 998 BOR patients and 1545 controls. Linkage disequilibrium score regression was used to detect the genetic overlap between BOR and these disorders. Single marker analysis revealed no significant association after correction for multiple testing. Gene-based analysis yielded two significant genes: DPYD (P=4.42 × 10-7) and PKP4 (P=8.67 × 10-7); and gene-set analysis yielded a significant finding for exocytosis (GO:0006887, PFDR=0.019; FDR, false discovery rate). Prior studies have implicated DPYD, PKP4 and exocytosis in BIP and SCZ. The most notable finding of the present study was the genetic overlap of BOR with BIP (rg=0.28 [P=2.99 × 10-3]), SCZ (rg=0.34 [P=4.37 × 10-5]) and MDD (rg=0.57 [P=1.04 × 10-3]). We believe our study is the first to demonstrate that BOR overlaps with BIP, MDD and SCZ on the genetic level. Whether this is confined to transdiagnostic clinical symptoms should be examined in future studies.

Published
2017

4. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group

Author

Schmaal, L, Hibar, DP, Sämann, PG, Hall, GB, Baune, BT, Jahanshad, N, Cheung, JW, van Erp, TGM, Bos, D, Ikram, MA, Vernooij, MW, Niessen, WJ, Tiemeier, H, Hofman, A, Wittfeld, K, Grabe, HJ, Janowitz, D, Bülow, R, Selonke, M, Völzke, H, Grotegerd, D, Dannlowski, U, Arolt, V, Opel, N, Heindel, W, Kugel, H, Hoehn, D, Czisch, M, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Goya-Maldonado, R, Gruber, O, Krämer, B, Hatton, SN, Lagopoulos, J, Hickie, IB, Frodl, T, Carballedo, A, Frey, EM, van Velzen, LS, Penninx, BWJH, van Tol, M-J, van der Wee, NJ, Davey, CG, Harrison, BJ, Mwangi, B, Cao, B, Soares, JC, Veer, IM, Walter, H, Schoepf, D, Zurowski, B, Konrad, C, Schramm, E, Normann, C, Schnell, K, Sacchet, MD, Gotlib, IH, MacQueen, GM, Godlewska, BR, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Hall, J, Sussmann, JE, Li, M, Walter, M, Aftanas, L, Brack, I, Bokhan, NA, Thompson, PM, and Veltman, DJ

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Major Depressive Disorder, Serious Mental Illness, Neurosciences, Biomedical Imaging, Depression, Basic Behavioral and Social Science, Mental Health, Brain Disorders, Clinical Research, Pediatric, Behavioral and Social Science, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Neurological, Mental health, Adolescent, Adult, Brain, Cerebral Cortex, Depressive Disorder, Major, Female, Frontal Lobe, Gray Matter, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Male, Neuroimaging, Prefrontal Cortex, Temporal Lobe, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen's d effect sizes: -0.10 to -0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: -0.26 to -0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

Published
2017

5. Subcortical volumetric abnormalities in bipolar disorder.

Author

Hibar, DP, Westlye, LT, van Erp, TGM, Rasmussen, J, Leonardo, CD, Faskowitz, J, Haukvik, UK, Hartberg, CB, Doan, NT, Agartz, I, Dale, AM, Gruber, O, Krämer, B, Trost, S, Liberg, B, Abé, C, Ekman, CJ, Ingvar, M, Landén, M, Fears, SC, Freimer, NB, Bearden, CE, Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Sprooten, E, Glahn, DC, Pearlson, GD, Emsell, L, Kenney, J, Scanlon, C, McDonald, C, Cannon, DM, Almeida, J, Versace, A, Caseras, X, Lawrence, NS, Phillips, ML, Dima, D, Delvecchio, G, Frangou, S, Satterthwaite, TD, Wolf, D, Houenou, J, Henry, C, Malt, UF, Bøen, E, Elvsåshagen, T, Young, AH, Lloyd, AJ, Goodwin, GM, Mackay, CE, Bourne, C, Bilderbeck, A, Abramovic, L, Boks, MP, van Haren, NEM, Ophoff, RA, Kahn, RS, Bauer, M, Pfennig, A, Alda, M, Hajek, T, Mwangi, B, Soares, JC, Nickson, T, Dimitrova, R, Sussmann, JE, Hagenaars, S, Whalley, HC, McIntosh, AM, Thompson, PM, and Andreassen, OA

Subjects
Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes, Brain, Humans, Magnetic Resonance Imaging, Organ Size, Case-Control Studies, Retrospective Studies, Bipolar Disorder, Adult, Middle Aged, Female, Male, Clinical Research, Mental Health, Brain Disorders, Serious Mental Illness, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Mental health, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Considerable uncertainty exists about the defining brain changes associated with bipolar disorder (BD). Understanding and quantifying the sources of uncertainty can help generate novel clinical hypotheses about etiology and assist in the development of biomarkers for indexing disease progression and prognosis. Here we were interested in quantifying case-control differences in intracranial volume (ICV) and each of eight subcortical brain measures: nucleus accumbens, amygdala, caudate, hippocampus, globus pallidus, putamen, thalamus, lateral ventricles. In a large study of 1710 BD patients and 2594 healthy controls, we found consistent volumetric reductions in BD patients for mean hippocampus (Cohen's d=-0.232; P=3.50 × 10-7) and thalamus (d=-0.148; P=4.27 × 10-3) and enlarged lateral ventricles (d=-0.260; P=3.93 × 10-5) in patients. No significant effect of age at illness onset was detected. Stratifying patients based on clinical subtype (BD type I or type II) revealed that BDI patients had significantly larger lateral ventricles and smaller hippocampus and amygdala than controls. However, when comparing BDI and BDII patients directly, we did not detect any significant differences in brain volume. This likely represents similar etiology between BD subtype classifications. Exploratory analyses revealed significantly larger thalamic volumes in patients taking lithium compared with patients not taking lithium. We detected no significant differences between BDII patients and controls in the largest such comparison to date. Findings in this study should be interpreted with caution and with careful consideration of the limitations inherent to meta-analyzed neuroimaging comparisons.

Published
2016

6. Response to Dr Fried & Dr Kievit, and Dr Malhi et al.

Author

Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, MW, Niessen, WJ, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Wright, MJ, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hall, GB, van Velzen, LS, van Tol, M-J, van der Wee, NJ, Veer, IM, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, AM, Whalley, HC, Sussmann, JE, Godlewska, BR, Fischer, FH, Penninx, BWJH, Thompson, PM, and Hibar, DP

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Published
2016

7. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.

Author

Schmaal, L, Veltman, DJ, van Erp, TGM, Sämann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Tiemeier, H, Hofman, A, Niessen, WJ, Vernooij, MW, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Völzke, H, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Krämer, B, Gruber, O, Couvy-Duchesne, B, Rentería, ME, Strike, LT, Mills, NT, de Zubicaray, GI, McMahon, KL, Medland, SE, Martin, NG, Gillespie, NA, Wright, MJ, Hall, GB, MacQueen, GM, Frey, EM, Carballedo, A, van Velzen, LS, van Tol, MJ, van der Wee, NJ, Veer, IM, Walter, H, Schnell, K, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, Nickson, T, McIntosh, AM, Papmeyer, M, Whalley, HC, Sussmann, JE, Godlewska, BR, Cowen, PJ, Fischer, FH, Rose, M, Penninx, BWJH, Thompson, PM, and Hibar, DP

Subjects
Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Case-Control Studies, Depressive Disorder, Major, Adult, Middle Aged, Female, Male, Neuroimaging, major depressive disorder, structural volumes, ENIGMA consortium, meta-analysis, hippocampus, Depressive Disorder, Major, Psychiatry, Medical and Health Sciences, Biological Sciences, Psychology and Cognitive Sciences
Abstract

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen's d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen's d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen's d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen's d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen's d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Published
2016

8. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium

Author

van Erp, TGM, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, McDonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MWJ, Koenders, L, de Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, van Haren, NEM, Pol, HEH, Ophoff, RA, Kahn, RS, Roiz-Santiañez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jönsson, EG, Dimitrova, R, Bois, C, Whalley, HC, McIntosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM, and Turner, JA

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Serious Mental Illness, Brain Disorders, Schizophrenia, Clinical Research, Neurosciences, Mental Health, Biomedical Imaging, Rare Diseases, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Adult, Brain, Brain Mapping, Case-Control Studies, Female, Functional Laterality, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Prospective Studies, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

Published
2016

9. Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system

Author

Barbu, MC, primary, Huider, F, additional, Campbell, A, additional, Amador, C, additional, Adams, MJ, additional, Lynall, ME, additional, Howard, DM, additional, Walker, RM, additional, Morris, SW, additional, Van Dongen, J, additional, Porteous, DJ, additional, Evans, KL, additional, Bullmore, E, additional, Willemsen, G, additional, Boomsma, DI, additional, Whalley, HC, additional, and McIntosh, AM, additional

Published
2020
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12. Three major dimensions of human brain cortical ageing in relation to cognitive decline across the 8thdecade of life

Author

Cox, SR, primary, Harris, MA, additional, Ritchie, SJ, additional, Buchanan, CR, additional, Valdés Hernández, MC, additional, Corley, J, additional, Taylor, AM, additional, Madole, JW, additional, Harris, SE, additional, Whalley, HC, additional, McIntosh, AM, additional, Russ, TC, additional, Bastin, ME, additional, Wardlaw, JM, additional, Deary, IJ, additional, and Tucker-Drob, EM, additional

Published
2020
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13. Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders

Author

Phil Lee, Richard Anney, Pieter J. Hoekstra, Barbara Franke, Geschwind Dh, Michael C. Neale, Yen-Chen Anne Feng, Edwin H. Cook, Anttila, E Stahl, Borglum Ad, Kenneth S. Kendler, Santangelo Sl, Wray Nr, Danielle Posthuma, Carol A. Mathews, Sintia Iole Belangero, Amanda B Zheutlin, Cynthia M. Bulik, Jeremiah M. Scharf, Laramie E. Duncan, Luis Augusto Rohde, Jacob Rosenthal, Anna Keski-Rahkonen, Stein Mb, Fanous Ah, Zhaozhong Zhu, Karen S. Mitchell, J Kaprio, Breen G, Michel G. Nivard, G. Kirov, John I. Nurnberger, Patrick F. Sullivan, Daly Mj, Michael Conlon O'Donovan, Andreassen Oa, Hyejung Won, Wang Mm, Faraone Sv, Mattheisen M, Benjamin M. Neale, Gelernter J, Smoller Jw, Henry R. Kranzler, Thornton Lm, Clement C. Zai, Ripke S, Elliot M. Tucker-Drob, Howard J. Edenberg, Lewis Cm, McIntosh Am, Tobias Banaschewski, Dongmei Yu, Andrew D. Grotzinger, Walters Jt, Raymond K. Walters, Giovanni Coppola, and Jurjen J. Luykx

Subjects
0303 health sciences, medicine.medical_specialty, Biology, medicine.disease, Genome, Tourette syndrome, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Pleiotropy, Anorexia nervosa (differential diagnoses), Autism spectrum disorder, Schizophrenia, medicine, Bipolar disorder, Psychiatry, 030217 neurology & neurosurgery, Depression (differential diagnoses), 030304 developmental biology
Abstract

SummaryGenetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders. We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

Published
2019
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14. Low-frequency variation in TP53 has large effects on head circumference and intracranial volume

Author

Haworth, S, Shapland, CY, Hayward, C, Prins, BP, Felix, JF, Medina-Gomez, C, Rivadeneira, F, Wang, C, Ahluwalia, TS, Vrijheid, M, Guxens, M, Sunyer, J, Tachmazidou, I, Walter, K, Iotchkova, V, Jackson, A, Cleal, L, Huffmann, J, Min, JL, Sass, L, Timmers, PRHJ, Al Turki, S, Anderson, CA, Anney, R, Antony, D, Artigas, MS, Ayub, M, Bala, S, Barrett, JC, Barroso, I, Beales, P, Bentham, J, Bhattacharya, S, Birney, E, Blackwood, D, Bobrow, M, Bochukova, E, Bolton, PF, Bounds, R, Boustred, C, Breen, G, Calissano, M, Carss, K, Charlton, R, Chatterjee, K, Chen, L, Ciampi, A, Cirak, S, Clapham, P, Clement, G, Coates, G, Cocca, M, Collier, DA, Cosgrove, C, Cox, T, Craddock, N, Crooks, L, Curran, S, Curtis, D, Daly, A, Danecek, P, Day, INM, Day-Williams, A, Dominiczak, A, Down, T, Du, Y, Dunham, I, Durbin, R, Edkins, S, Ekong, R, Ellis, P, Evans, DM, Farooqi, IS, Fitzpatrick, DR, Flicek, P, Floyd, J, Foley, AR, Franklin, CS, Futema, M, Gallagher, L, Gaunt, TR, Geihs, M, Geschwind, D, Greenwood, CMT, Griffin, H, Grozeva, D, Guo, X, Gurling, H, Hart, D, Hendricks, AE, Holmans, P, Howie, B, Huang, J, Huang, L, Hubbard, T, Humphries, SE, Hurles, ME, Hysi, P, Jackson, DK, Jamshidi, Y, Joyce, C, Karczewski, KJ, Kaye, J, Keane, T, Kemp, JP, Kennedy, K, Kent, A, Keogh, J, Khawaja, F, van Kogelenberg, M, Kolb-Kokocinski, A, Lachance, G, Langford, C, Lawson, D, Lee, I, Lek, M, Li, R, Li, Y, Liang, J, Lin, H, Liu, R, Lonnqvist, J, Lopes, LR, Lopes, M, MacArthur, DG, Mangino, M, Marchini, J, Marenne, G, Maslen, J, Mathieson, I, McCarthy, S, McGuffin, P, McIntosh, AM, McKechanie, AG, McQuillin, A, Memari, Y, Metrustry, S, Migone, N, Mitchison, HM, Moayyeri, A, Morris, A, Morris, J, Muddyman, D, Muntoni, F, Northstone, K, O'Donovan, MC, O'Rahilly, S, Onoufriadis, A, Oualkacha, K, Owen, MJ, Palotie, A, Panoutsopoulou, K, Parker, V, Parr, JR, Paternoster, L, Paunio, T, Payne, F, Payne, SJ, Perry, JRB, Pietilainen, O, Plagnol, V, Pollitt, RC, Porteous, DJ, Povey, S, Quail, MA, Quaye, L, Raymond, FL, Rehnstrom, K, Richards, JB, Ridout, CK, Ring, S, Ritchie, GRS, Roberts, N, Robinson, RL, Savage, DB, Scambler, P, Schiffels, S, Schmidts, M, Schoenmakers, N, Scott, RH, Semple, RK, Serra, E, Sharp, SI, Shaw, A, Shihab, HA, Shin, S-Y, Skuse, D, Small, KS, Smee, C, Smith, BH, Soranzo, N, Southam, L, Spasic-Boskovic, O, Spector, TD, St Clair, D, Stalker, J, Stevens, E, Sun, J, Surdulescu, G, Suvisaari, J, Syrris, P, Taylor, R, Tian, J, Tobin, MD, Valdes, AM, Vandersteen, AM, Vijayarangakannan, P, Visscher, PM, Wain, LV, Walters, JTR, Wang, G, Wang, J, Wang, Y, Ward, K, Wheeler, E, Whyte, T, Williams, HJ, Williamson, KA, Wilson, C, Wilson, SG, Wong, K, Xu, C, Yang, J, Zhang, F, Zhang, P, Zheng, H-F, Smith, GD, Fisher, SE, Wilson, JF, Cole, TJ, Fernandez-Orth, D, Bonnelykke, K, Bisgaard, H, Pennell, CE, Jaddoe, VWV, Dedoussis, G, Timpson, N, Zeggini, E, Vitart, V, St Pourcain, B, UK10K Consortium, Epidemiology, Erasmus MC other, Pediatrics, Internal Medicine, and Child and Adolescent Psychiatry / Psychology

Abstract

Cranial growth and development is a complex process which affects the closely related traits of head circumference (HC) and intracranial volume (ICV). The underlying genetic influences shaping these traits during the transition from childhood to adulthood are little understood, but might include both age-specific genetic factors and low-frequency genetic variation. Here, we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV + HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood, suggesting a previously unrecognized role of TP53 transcripts in human cranial development.

Published
2019

15. Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts

Author

Sanchez-Roige, S, Palmer, AA, Fontanillas, P, Elson, SL, 23andMe Research Team, Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Adams, MJ, Howard, DM, Edenberg, HJ, Davies, G, Crist, RC, Deary, IJ, McIntosh, AM, and Clarke, T-K

Subjects
Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team
Abstract

OBJECTIVE::Alcohol use disorders are common conditions that have enormous social and economic consequences. Genome-wide association analyses were performed to identify genetic variants associated with a proxy measure of alcohol consumption and alcohol misuse and to explore the shared genetic basis between these measures and other substance use, psychiatric, and behavioral traits. METHOD::This study used quantitative measures from the Alcohol Use Disorders Identification Test (AUDIT) from two population-based cohorts of European ancestry (UK Biobank [N=121,604] and 23andMe [N=20,328]) and performed a genome-wide association study (GWAS) meta-analysis. Two additional GWAS analyses were performed, a GWAS for AUDIT scores on items 1-3, which focus on consumption (AUDIT-C), and for scores on items 4-10, which focus on the problematic consequences of drinking (AUDIT-P). RESULTS::The GWAS meta-analysis of AUDIT total score identified 10 associated risk loci. Novel associations localized to genes including JCAD and SLC39A13; this study also replicated previously identified signals in the genes ADH1B, ADH1C, KLB, and GCKR. The dimensions of AUDIT showed positive genetic correlations with alcohol consumption (rg=0.76-0.92) and DSM-IV alcohol dependence (rg=0.33-0.63). AUDIT-P and AUDIT-C scores showed significantly different patterns of association across a number of traits, including psychiatric disorders. AUDIT-P score was significantly positively genetically correlated with schizophrenia (rg=0.22), major depressive disorder (rg=0.26), and attention deficit hyperactivity disorder (rg=0.23), whereas AUDIT-C score was significantly negatively genetically correlated with major depressive disorder (rg=-0.24) and ADHD (rg=-0.10). This study also used the AUDIT data in the UK Biobank to identify thresholds for dichotomizing AUDIT total score that optimize genetic correlations with DSM-IV alcohol dependence. Coding individuals with AUDIT total scores ≤4 as control subjects and those with scores ≥12 as case subjects produced a significant high genetic correlation with DSM-IV alcohol dependence (rg=0.82) while retaining most subjects. CONCLUSIONS::AUDIT scores ascertained in population-based cohorts can be used to explore the genetic basis of both alcohol consumption and alcohol use disorders.

Published
2018

16. Genome-wide association study reveals greater polygenic loading for schizophrenia in cases with a family history of illness

Author

Bigdeli, T. B, Ripke, S, Bacanu, Sa, Lee, S. H, Wray, Nr, Gejman, P. V, Rietschel, M, Cichon, S, St Clair, D, Corvin, A, Kirov, G, Mcquillin, A, Gurling, H, Rujescu, D, Andreassen, O. A, Werge, T, Blackwood, D. H. R, Pato, C. N, Pato, M. T, Malhotra, A. K, O'Donovan, M. C, Kendler, K. S, Fanous, A. H, Neale, Bm, 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, Begemann, M, Belliveau, Ra, Bene, J, Bergen, Se, Bevilacqua, E, Bigdeli, Tb, Black, Dw, Børglum, Ad, 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, C, Cohen, D, Cohen, N, Cormican, P, Craddock, N, Crowley, Jj, Curtis, D, Davidson, M, Davis, Kl, Degenhardt, F, Del, Favero, J, Delisi, Le, 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, Gershon, Es, Giegling, I, Giusti Rodríguez, P, Godard, S, Goldstein, Ji, Golimbet, V, Gopal, S, Gratten, J, Grove, J, Haan, De, 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, Julìa, A, Kahn, Rs, Kalaydjieva, L, Karachanak Yankova, S, Karjalainen, J, Kavanagh, D, Keller, Mc, Kelly, Bj, Kennedy, Jl, Khrunin, A, Kim, Y, Klovins, J, Knowles, Ja, Konte, B, Kucinskas, V, Kucinskiene, Za, Kuzelova Ptackova, H, Kähler, Ak, Laurent, C, Keong, Jl, Lee, S, 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, Mattingsdal, 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, Mortensen, Pb, 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, F, Sy, Oh, Olincy, A, Olsen, L, Jv, Os, Pantelis, C, Papadimitriou, Gn, Papiol, S, Parkhomenko, E, Pato, Mt, Paunio, T, Pejovic Milovancevic, M, Perkins, Do, 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, 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, Hc, So, Spencer, Cc, Stahl, Ea, Stefansson, H, Steinberg, S, Stogmann, E, Straub, Re, Strengman, E, Strohmaier, J, Stroup, T, Subramaniam, M, Suvisaari, J, Svrakic, Dm, Szatkiewicz, Jp, Söderman, E, Thirumalai, S, Toncheva, D, Tooney, Pa, Tosato, Sarah, 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, Jq, Wu, Hs, Xi, Zai, Cc, Zheng, X, Zimprich, F, Stefansson, K, Visscher, Pm, Adolfsson, R, Andreassen, Oa, Blackwood, Dh, Bramon, E, Buxbaum, Jd, Darvasi, A, Domenici, E, Ehrenreich, H, Esko, T, Gejman, Pv, Gill, M, Hultman, Cm, Iwata, N, Jablensky, Av, Jönsson, Eg, Kendler, Ks, Knight, J, Lencz, T, Levinson, Df, Qs, Li, Liu, J, Malhotra, Ak, Mccarroll, Sa, Moran, Jl, Mowry, Bj, Nöthen, Mm, Ophoff, Ra, Owen, Mj, Palotie, A, Pato, Cn, Petryshen, Tl, Posthuma, D, Riley, Bp, Sham, Pc, Sklar, P, Clair, Ds, Weinberger, Dr, Wendland, Jr, Daly, Mj, Sullivan, Pf, O'Donovan, Mc, Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Bigdeli, Tim B, Ripke, Stephan, Bacanu, Silviu-Alin, Lee, Sang Hong, Fanous, Ayman H, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Psychosis Endophenotype International Consortium, Wellcome Trust Case-Control Consortium 2, Other departments, Adult Psychiatry, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), and Del-Favero, Jurgen

Subjects
0301 basic medicine, Multifactorial Inheritance, Bipolar Disorder, Inheritance Patterns, Genome-wide association study, Single-nucleotide polymorphism, polygenic, heritability, Biology, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, medicine, Humans, SNP, GWAS, Family, Genetic Predisposition to Disease, Family history, Allele, Genetics (clinical), Genetic association, Genetics & Heredity, Psychiatry, Genetics, Depressive Disorder, Major, family history, Models, Genetic, Case-control study, medicine.disease, 3. Good health, schizophrenia, Psychiatry and Mental health, 030104 developmental biology, Schizophrenia, Case-Control Studies, Human medicine, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Genome-wide association studies (GWAS) of schizophrenia have yielded more than 100 common susceptibility variants, and strongly support a substantial polygenic contribution of a large number of small allelic effects. It has been hypothesized that familial schizophrenia is largely a consequence of inherited rather than environmental factors. We investigated the extent to which familiality of schizophrenia is associated with enrichment for common risk variants detectable in a large GWAS. We analyzed single nucleotide polymorphism (SNP) data for cases reporting a family history of psychotic illness (N=978), cases reporting no such family history (N=4,503), and unscreened controls (N=8,285) from the Psychiatric Genomics Consortium (PGC1) study of schizophrenia. We used a multinomial logistic regression approach with model-fitting to detect allelic effects specific to either family history subgroup. We also considered a polygenic model, in which we tested whether family history positive subjects carried more schizophrenia risk alleles than family history negative subjects, on average. Several individual SNPs attained suggestive but not genome-wide significant association with either family history subgroup. Comparison of genome-wide polygenic risk scores based on GWAS summary statistics indicated a significant enrichment for SNP effects among family history positive compared to family history negative cases (Nagelkerke's R-2=0.0021; P=0.00331; P-value threshold

Published
2016

17. Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders

Author

Calafato, MS, Thygesen, JH, Ranlund, S, Zartaloudi, E, Cahn, W, Crespo-Facorro, B, Díez-Revuelta, Á, Di Forti, M, Consortium, Genetic Risk And Outcome Of Psychosis (Group), Hall, M-H, Iyegbe, C, Jablensky, A, Kahn, R, Kalaydjieva, L, Kravariti, E, Lin, K, McDonald, C, McIntosh, AM, McQuillin, A, (PEIC), Psychosis Endophenotypes International Consortium, Picchioni, M, Rujescu, D, Shaikh, M, Toulopoulou, T, Os, JV, Vassos, E, Walshe, M, Powell, J, Lewis, CM, Murray, RM, Bramon, E, (WTCCC2), Wellcome Trust Case Control Consortium 2, Toulopoulou, Timothea, Genetic Risk and Outcome of Psychosis (GROUP) consortium, Psychosis Endophenotypes International Consortium (PEIC), Universidad de Cantabria, RS: MHeNs - R2 - Mental Health, MUMC+: MA Psychiatrie (3), MUMC+: Hersen en Zenuw Centrum (3), and Psychiatrie & Neuropsychologie

Subjects
0301 basic medicine, Male, Multifactorial Inheritance, LOCI, polygenic, Disease, FAMILY-HISTORY, DISEASE, 0302 clinical medicine, Risk Factors, psychotic disorders, EMERGING MOLECULAR ARCHITECTURE, Family history, POPULATION, education.field_of_study, Psychotic disorders, PSYCHIATRIC-DISORDERS, Middle Aged, 3. Good health, Europe, Psychiatry and Mental health, polygenic risk scores, Schizophrenia, Female, CLINICAL-IMPLICATIONS, Risk assessment, Adult, Psychosis, medicine.medical_specialty, Bipolar disorder, Population, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Young Adult, mental disorders, medicine, Genetic predisposition, Humans, Genetic Predisposition to Disease, GXE RESEARCH, GENOME-WIDE ASSOCIATION, education, Psychiatry, business.industry, Australia, prediction, medicine.disease, schizophrenia, Polygenic risk scores, 030104 developmental biology, Logistic Models, Case-Control Studies, Polygenic, business, ROC CURVE, Prediction, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

BACKGROUND: There is increasing evidence for shared genetic susceptibility between schizophrenia and bipolar disorder. Although genetic variants only convey subtle increases in risk individually, their combination into a polygenic risk score constitutes a strong disease predictor.AimsTo investigate whether schizophrenia and bipolar disorder polygenic risk scores can distinguish people with broadly defined psychosis and their unaffected relatives from controls. METHOD: Using the latest Psychiatric Genomics Consortium data, we calculated schizophrenia and bipolar disorder polygenic risk scores for 1168 people with psychosis, 552 unaffected relatives and 1472 controls. RESULTS: Patients with broadly defined psychosis had dramatic increases in schizophrenia and bipolar polygenic risk scores, as did their relatives, albeit to a lesser degree. However, the accuracy of predictive models was modest. CONCLUSIONS: Although polygenic risk scores are not ready for clinical use, it is hoped that as they are refined they could help towards risk reduction advice and early interventions for psychosis.Declaration of interestR.M.M. has received honoraria for lectures from Janssen, Lundbeck, Lilly, Otsuka and Sunovian. Funding: This work was funded by the Medical Research Council (G0901310), the Wellcome Trust (grants 085475/B/08/Z, 085475/Z/08/Z), the European Union’s Seventh Framework Programme for research, technological development and demonstration (grant 602450). This study was also supported by the NIHR Biomedical Research Centre at University College London (mental health theme) and by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry – Kings College London. Further support: NHIR Academic Clinical fellowship awarded to M.S.C.. E.B. acknowledges research funding from: 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 and the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry Kings College London. The Brain and Behaviour Research foundation’s (NARSAD’s) Young Investigator Award (Grant 22604, awarded to C.I.). The BMA Margaret Temple grant 2016 to J. H.T. European Research Council Marie Curie award to A.D.-R. 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 healthcare organisations. Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. 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 Zorggroep, Prins Clauscentrum Sittard, RIAGG Roermond, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psychomedical center (The Hague). Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal, Riagg Amersfoort and Delta. The sample from Spain was collected at the Hospital Universitario Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Carlos III Health Institute PI020499, PI050427, PI060507, Plan Nacional de Drugs Research Grant 2005- Orden sco/3246/2004, SENY Fundació Research Grant CI 2005-0308007 and Fundación Marqués de Valdecilla API07/011. The present data were obtained at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: MINECO Exp.: SAF2013-46292-R.

Published
2018

19. Associations between vascular risk factors and brain MRI indices in UK Biobank

Author

Cox, SR, primary, Lyall, DM, additional, Ritchie, SJ, additional, Bastin, ME, additional, Harris, MA, additional, Buchanan, CR, additional, Fawns-Ritchie, C, additional, Barbu, MC, additional, de Nooij, L, additional, Reus, LM, additional, Alloza, C, additional, Shen, X, additional, Neilson, E, additional, Alderson, HL, additional, Hunter, S, additional, Liewald, DC, additional, Whalley, HC, additional, McIntosh, AM, additional, Lawrie, SJ, additional, Pell, JP, additional, Tucker-Drob, EM, additional, Wardlaw, JM, additional, Gale, CR, additional, and Deary, IJ, additional

Published
2019
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20. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

Author

Gusev, A, Lee, Sh, SWE SCZ, Consortium, O'Dushlaine, Cgusev, Trynka, G, Finucane, H, Vilhjálmsson, Bj, Xu, H, Zang, C, Ripke, S, Bulik Sullivan, B, Stahl, E, Schizophrenia, Working, Neale, Bm, Corvin, A, Walters, Jt, Farh, Kh, Holmans, Pa, Lee, P, 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, Børglum, Ad, 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, Delisi, Le, 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, Gershon, Es, Giegling, I, Giusti Rodrguez, P, Godard, S, Goldstein, Ji, Golimbet, V, Gopal, S, Gratten, J, Grove, J, Haan, De, 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, Julià, A, Kahn, Rs, Kalaydjieva, L, Karachanak Yankova, S, Karjalainen, J, Kavanagh, D, Keller, Mc, Kelly, Bj, Kennedy, Jl, Khrunin, A, Kim, Y, Klovins, J, Knowles, Ja, Konte, B, Kucinskas, V, Kucinskiene, Za, Kuzelova Ptackova, H, Kähler, Ak, Laurent, C, Keong, Jl, Legge, Se, Lerer, B, Li, M, Li, T, Liang, Ky, Lieberman, J, Limborska, S, Loughland, Cm, Lubinski, J, Lnnqvist, J, Macek, M, Magnusson, Pk, Maher, Bs, Maier, W, Mallet, J, Marsal, S, Mattheisen, M, Mattingsdal, 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, Mortensen, Pb, Murphy, Kc, Murray, Rm, Myin Germeys, I, Mller 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, Sy, Oh, Olincy, A, Olsen, L, Van, Os, Pantelis, C, Papadimitriou, Gn, Papiol, S, Parkhomenko, E, Pato, Mt, Paunio, T, Pejovic Milovancevic, M, Perkins, Do, Pietilinen, 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, Hc, So, Spencer, Cc, Stahl, Ea, Stefansson, H, Steinberg, S, Stogmann, E, Straub, Re, Strengman, E, Strohmaier, J, Stroup, Ts, Subramaniam, M, Suvisaari, J, Svrakic, Dm, Szatkiewicz, Jp, Sderman, E, Thirumalai, S, Toncheva, D, Tooney, Pa, Tosato, Sarah, 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, Jq, Wu, Hs, Xi, Zai, Cc, Zheng, X, Zimprich, F, Wray, Nr, Stefansson, K, Visscher, Pm, Adolfsson, R, Andreassen, Oa, Blackwood, Dh, Bramon, E, Buxbaum, Jd, Brglum, 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, Qs, Li, Liu, J, Malhotra, Ak, Mccarroll, Sa, Mcquillin, A, Moran, Jl, Mowry, Bj, Nthen, Mm, Ophoff, Ra, Owen, Mj, Palotie, A, Pato, Cn, Petryshen, Tl, Posthuma, D, Rietschel, M, Riley, Bp, Rujescu, D, Sham, Pc, Sklar, P, Clair, St, Weinberger, Dr, Wendland, Jr, Werge, T, Daly, Mj, Sullivan, Pf, O'Donovan, Mc, Chambert, K, Akterin, S, Bergen, S, Ruderfer, D, Scolnick, E, Purcell, S, Mccarroll, S, Daly, M, Pasaniuc, B, Raychaudhuri, S, Price, Al, Gusev, Alexander, Lee, S Hong, Trynka, Gosia, Finucane, Hilary K, Price, Alkes L, Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, ANS - Amsterdam Neuroscience, Adult Psychiatry, Other departments, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, Complex Trait Genetics, Functional Genomics, and Neuroscience Campus Amsterdam - Brain Mechanisms in Health & Disease

Subjects
Linkage disequilibrium, GWAS, schizophrenia, SNP, trait heritability, disease architecture, Inheritance Patterns, Single-nucleotide polymorphism, Genome-wide association study, Biology, Article, Open Reading Frames, SDG 3 - Good Health and Well-being, Genetic, Models, Genotype, Genetics, Humans, Genetics(clinical), Computer Simulation, Regulatory Elements, Transcriptional, Exome, Genetics (clinical), genotype imputation, Genetic association, Genetics & Heredity, genome-wide association study, Models, Genetic, Genetic Diseases, Inborn, Genetic Variation, Heritability, exome chips, Regulatory Elements, Inborn, Genetic Diseases, Transcriptional, coding variants, Genome-Wide Association Study
Abstract

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (h(g)(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of h(g)(2) from imputed SNPs (5.1 x enrichment; p = 3.7 x 10(-17)) and 38% (SE = 4%) of h(g)(2) from genotyped SNPs (1.6 x enrichment, p = 1.0 x 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained

Published
2014

22. Erratum: Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium (Molecular Psychiatry (2015) DOI:10.1038/mp.2015.63)

Author

Van Erp, TGM, Hibar, DP, Rasmussen, JM, Glahn, DC, Pearlson, GD, Andreassen, OA, Agartz, I, Westlye, LT, Haukvik, UK, Dale, AM, Melle, I, Hartberg, CB, Gruber, O, Kraemer, B, Zilles, D, Donohoe, G, Kelly, S, Mcdonald, C, Morris, DW, Cannon, DM, Corvin, A, Machielsen, MWJ, Koenders, L, De Haan, L, Veltman, DJ, Satterthwaite, TD, Wolf, DH, Gur, RC, Gur, RE, Potkin, SG, Mathalon, DH, Mueller, BA, Preda, A, Macciardi, F, Ehrlich, S, Walton, E, Hass, J, Calhoun, VD, Bockholt, HJ, Sponheim, SR, Shoemaker, JM, Van Haren, NEM, Pol, HEH, Ophoff, RA, Kahn, RS, Roiz-Santiañez, R, Crespo-Facorro, B, Wang, L, Alpert, KI, Jönsson, EG, Dimitrova, R, Bois, C, Whalley, HC, Mcintosh, AM, Lawrie, SM, Hashimoto, R, Thompson, PM, and Turner, JA

Published
2016

23. Size matters; but so does what you do with it! Response

Author

Schmaal, L, Veltman, DJ, van Erp, TGM, Saemann, PG, Frodl, T, Jahanshad, N, Loehrer, E, Vernooij, MW, Niessen, WJ, Ikram, MA, Wittfeld, K, Grabe, HJ, Block, A, Hegenscheid, K, Hoehn, D, Czisch, M, Lagopoulos, J, Hatton, SN, Hickie, IB, Goya-Maldonado, R, Kraemer, B, Gruber, O, Couvy-Duchesne, B, Renteria, ME, Strike, LT, Wright, MJ, de Zubicaray, GI, McMahon, KL, Medland, SE, Gillespie, NA, Hall, GB, van Velzen, LS, van Tol, M-J, van der Wee, NJ, Veer, IM, Walter, H, Schramm, E, Normann, C, Schoepf, D, Konrad, C, Zurowski, B, McIntosh, AM, Whalley, HC, Sussmann, JE, Godlewska, BR, Fischer, FH, Penninx, BWJH, Thompson, PM, Hibar, DP, Disorder, E-MD, Epidemiology, Radiology & Nuclear Medicine, Medical Informatics, and Neurology

Published
2016

24. Cortical Surface Area Differentiates Familial High Risk Individuals Who Go on to Develop Schizophrenia

Author

Bois, C, Ronan, Lisa, Levita, L, Whalley, HC, Giles, S, McIntosh, AM, Fletcher, Paul, Owens, DC, Johnstone, EC, and Lawrie, SM

Abstract

BACKGROUND: Schizophrenia is associated with structural brain abnormalities that may be present before disease\ud onset. It remains unclear whether these represent general vulnerability indicators or are associated with the clinical state itself.\ud \ud METHODS: To investigate this, structural brain scans were acquired at two time points (mean scan interval\ud 1.87 years) in a cohort of individuals at high familial risk of schizophrenia (n 5 142) and control subjects (n 5 36).\ud Cortical reconstructions were generated using FreeSurfer. The high-risk cohort was subdivided into individuals that\ud remained well during the study, individuals that had transient psychotic symptoms, and individuals that subsequently\ud became ill. Baseline measures and longitudinal change in global estimates of thickness and surface area and lobar\ud values were compared, focusing on overall differences between high-risk individuals and control subjects and then\ud on group differences within the high-risk cohort.\ud \ud RESULTS: Longitudinally, control subjects showed a significantly greater reduction in cortical surface area\ud compared with the high-risk group. Within the high-risk group, differences in surface area at baseline predicted\ud clinical course, with individuals that subsequently became ill having significantly larger surface area than individuals\ud that remained well during the study. For thickness, longitudinal reductions were most prominent in the frontal,\ud cingulate, and occipital lobes in all high-risk individuals compared with control subjects.\ud \ud CONCLUSIONS: Our results suggest that larger surface areas at baseline may be associated with mechanisms that\ud go above and beyond a general familial disposition. A relative preservation over time of surface area, coupled with a\ud thinning of the cortex compared with control subjects, may serve as vulnerability markers of schizophrenia.

Published
2015

26. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel

Author

Huang, J, Howie, B, Mccarthy, S, Memari, Y, Walter, K, Min, Jl, Danecek, P, Malerba, Giovanni, Trabetti, Elisabetta, Zheng, Hf, Gambaro, G, Richards, Jb, Durbin, R, Timpson, Nj, Marchini, J, Soranzo, N, Al Turki, S, Amuzu, A, Anderson, Ca, Anney, R, Antony, D, Artigas, Ms, Ayub, M, Bala, S, Barrett, Jc, Barroso, I, Beales, P, Benn, M, Bentham, J, Bhattacharya, S, Birney, E, Blackwood, D, Bobrow, M, Bochukova, E, Bolton, Pf, Bounds, R, Boustred, C, Breen, G, Calissano, M, Carss, K, Casas, Jp, Chambers, Jc, Charlton, R, Chatterjee, K, Chen, L, Ciampi, A, Cirak, S, Clapham, P, Clement, G, Coates, G, Cocca, M, Collier, Da, Cosgrove, C, Cox, T, Craddock, N, Crooks, L, Curran, S, Curtis, D, Daly, A, Day, In, Day Williams, A, Dedoussis, G, Down, T, Du, Y, van Duijn, Cm, Dunham, I, Edkins, S, Ekong, R, Ellis, P, Evans, Dm, Farooqi, Is, Fitzpatrick, Dr, Flicek, P, Floyd, J, Foley, Ar, Franklin, Cs, Futema, M, Gallagher, L, Gasparini, P, Gaunt, Tr, Geihs, M, Geschwind, D, Greenwood, C, Griffin, H, Grozeva, D, Guo, X, Gurling, H, Hart, D, Hendricks, Ae, Holmans, P, Huang, L, Hubbard, T, Humphries, Se, Hurles, Me, Hysi, P, Iotchkova, V, Isaacs, A, Jackson, Dk, Jamshidi, Y, Johnson, J, Joyce, C, Karczewski, Kj, Kaye, J, Keane, T, Kemp, Jp, Kennedy, K, Kent, A, Keogh, J, Khawaja, F, Kleber, Me, van Kogelenberg, M, Kolb Kokocinski, A, Kooner, Js, Lachance, G, Langenberg, C, Langford, C, Lawson, D, Lee, I, van Leeuwen, Em, Lek, M, Li, R, Li, Y, Liang, J, Lin, H, Liu, R, Lönnqvist, J, Lopes, Lr, Lopes, M, Luan, J, Macarthur, Dg, Mangino, M, Marenne, G, März, W, Maslen, J, Matchan, A, Mathieson, I, Mcguffin, P, Mcintosh, Am, Mckechanie, Ag, Mcquillin, A, Metrustry, S, Migone, N, Mitchison, Hm, Moayyeri, A, Morris, J, Morris, R, Muddyman, D, Muntoni, F, Nordestgaard, Bg, Northstone, K, O'Donovan, Mc, O'Rahilly, S, Onoufriadis, A, Oualkacha, K, Owen, Mj, Palotie, A, Panoutsopoulou, K, Parker, V, Parr, Jr, Paternoster, L, Paunio, T, Payne, F, Payne, Sj, Perry, Jr, Pietilainen, O, Plagnol, V, Pollitt, Rc, Povey, S, Quail, Ma, Quaye, L, Raymond, L, Rehnström, K, Ridout, Ck, Ring, S, Ritchie, Gr, Roberts, N, Robinson, Rl, Savage, Db, Scambler, P, Schiffels, S, Schmidts, M, Schoenmakers, N, Scott, Rh, Scott, Ra, Semple, Rk, Serra, E, Sharp, Si, Shaw, A, Shihab, Ha, Shin, Sy, Skuse, D, Small, Ks, Smee, C, Smith, Gd, Southam, L, Spasic Boskovic, O, Spector, Td, St Clair, D, St Pourcain, B, Stalker, J, Stevens, E, Sun, J, Surdulescu, G, Suvisaari, J, Syrris, P, Tachmazidou, I, Taylor, R, Tian, J, Tobin, Md, Toniolo, D, Traglia, M, Tybjaerg Hansen, A, Valdes, Am, Vandersteen, Am, Varbo, A, Vijayarangakannan, P, Visscher, Pm, Wain, Lv, Walters, Jt, Wang, G, Wang, J, Wang, Y, Ward, K, Wheeler, E, Whincup, P, Whyte, T, Williams, Hj, Williamson, Ka, Wilson, C, Wilson, Sg, Wong, K, Xu, C, Yang, J, Zaza, Gianluigi, Zeggini, E, Zhang, F, Zhang, P, Zhang, W., Clinicum, Department of Psychiatry, Jie, Huang, Bryan, Howie, Shane, Mccarthy, Yasin, Memari, Klaudia, Walter, Josine L., Min, Petr, Danecek, Giovanni, Malerba, Elisabetta, Trabetti, Hou Feng, Zheng, Saeed Al, Turki, Antoinette, Amuzu, Carl A., Anderson, Richard, Anney, Dinu, Antony, María Soler, Artiga, Muhammad, Ayub, Senduran, Bala, Jeffrey C., Barrett, Inês, Barroso, Phil, Beale, Marianne, Benn, Jamie, Bentham, Shoumo, Bhattacharya, Ewan, Birney, Douglas, Blackwood, Martin, Bobrow, Elena, Bochukova, Patrick F., Bolton, Rebecca, Bound, Chris, Boustred, Gerome, Breen, Mattia, Calissano, Keren, Car, Juan Pablo, Casa, John C., Chamber, Ruth, Charlton, Krishna, Chatterjee, Lu, Chen, Antonio, Ciampi, Sebahattin, Cirak, Peter, Clapham, Gail, Clement, Guy, Coate, Cocca, Massimiliano, David A., Collier, Catherine, Cosgrove, Tony, Cox, Nick, Craddock, Lucy, Crook, Sarah, Curran, David, Curti, Allan, Daly, Ian N. M., Day, Aaron Day, William, George, Dedoussi, Thomas, Down, Yuanping, Du, Cornelia M., van Duijn, Ian, Dunham, Sarah, Edkin, Rosemary, Ekong, Peter, Elli, David M., Evan, I., Sadaf Farooqi, David R., Fitzpatrick, Paul, Flicek, James, Floyd, A., Reghan Foley, Christopher S., Franklin, Marta, Futema, Louise, Gallagher, Gasparini, Paolo, Tom R., Gaunt, Matthias, Geih, Daniel, Geschwind, Celia, Greenwood, Heather, Griffin, Detelina, Grozeva, Xiaosen, Guo, Xueqin, Guo, Hugh, Gurling, Deborah, Hart, Audrey E., Hendrick, Peter, Holman, Liren, Huang, Tim, Hubbard, Steve E., Humphrie, Matthew E., Hurle, Pirro, Hysi, Valentina, Iotchkova, Aaron, Isaac, David K., Jackson, Yalda, Jamshidi, Jon, Johnson, Chris, Joyce, Konrad J., Karczewski, Jane, Kaye, Thomas, Keane, John P., Kemp, Karen, Kennedy, Alastair, Kent, Julia, Keogh, Farrah, Khawaja, Marcus E., Kleber, Margriet van, Kogelenberg, Anja Kolb, Kokocinski, Jaspal S., Kooner, Genevieve, Lachance, Claudia, Langenberg, Cordelia, Langford, Daniel, Lawson, Irene, Lee, Elisabeth M., van Leeuwen, Monkol, Lek, Rui, Li, Yingrui, Li, Jieqin, Liang, Hong, Lin, Ryan, Liu, Jouko, Lönnqvist, Luis R., Lope, Margarida, Lope, Jian'An, Luan, Daniel G., Macarthur, Massimo, Mangino, Gaëlle, Marenne, Winfried, März, John, Maslen, Angela, Matchan, Iain, Mathieson, Peter, Mcguffin, Andrew M., Mcintosh, Andrew G., Mckechanie, Andrew, Mcquillin, Sarah, Metrustry, Nicola, Migone, Hannah M., Mitchison, Alireza, Moayyeri, James, Morri, Richard, Morri, Dawn, Muddyman, Francesco, Muntoni, Børge G., Nordestgaard, Kate, Northstone, Michael C., O'Donovan, Stephen, O'Rahilly, Alexandros, Onoufriadi, Karim, Oualkacha, Michael J., Owen, Aarno, Palotie, Kalliope, Panoutsopoulou, Victoria, Parker, Jeremy R., Parr, Lavinia, Paternoster, Tiina, Paunio, Felicity, Payne, Stewart J., Payne, John R. B., Perry, Olli, Pietilainen, Vincent, Plagnol, Rebecca C., Pollitt, Sue, Povey, Michael A., Quail, Lydia, Quaye, Lucy, Raymond, Karola, Rehnström, Cheryl K., Ridout, Susan, Ring, Graham R. S., Ritchie, Nicola, Robert, Rachel L., Robinson, David B., Savage, Peter, Scambler, Stephan, Schiffel, Miriam, Schmidt, Nadia, Schoenmaker, Richard H., Scott, Robert A., Scott, Robert K., Semple, Eva, Serra, Sally I., Sharp, Adam, Shaw, Hashem A., Shihab, So Youn, Shin, David, Skuse, Kerrin S., Small, Carol, Smee, George Davey, Smith, Lorraine, Southam, Olivera Spasic, Boskovic, Timothy D., Spector, David St, Clair, Beate St, Pourcain, Jim, Stalker, Elizabeth, Steven, Jianping, Sun, Gabriela, Surdulescu, Jaana, Suvisaari, Petros, Syrri, Ioanna, Tachmazidou, Rohan, Taylor, Jing, Tian, Martin D., Tobin, Daniela, Toniolo, Michela, Traglia, Anne Tybjaerg, Hansen, Ana M., Valde, Anthony M., Vandersteen, Anette, Varbo, Parthiban, Vijayarangakannan, Peter M., Visscher, Louise V., Wain, James T. R., Walter, Guangbiao, Wang, Jun, Wang, Yu, Wang, Kirsten, Ward, Eleanor, Wheeler, Peter, Whincup, Tamieka, Whyte, Hywel J., William, Kathleen A., Williamson, Crispian, Wilson, Scott G., Wilson, Kim, Wong, Changjiang, Xu, Jian, Yang, Gianluigi, Zaza, Eleftheria, Zeggini, Feng, Zhang, Pingbo, Zhang, Weihua, Zhang, Giovanni, Gambaro, J., Brent Richard, Richard, Durbin, Nicholas J., Timpson, Jonathan, Marchini, and Nicole, Soranzo

Subjects
Computer science, General Physics and Astronomy, Genome-wide association study, 0302 clinical medicine, Gene Frequency, Haplotype, Genetics,Biological sciences, Settore MED/14 - NEFROLOGIA, Aged, 80 and over, Genetics, 0303 health sciences, education.field_of_study, Multidisciplinary, TWINSUK, Middle Aged, single-nucleotide polymorphism, Whole-genome sequencing, WGS imputation panel, single-nucleotide polymorphism, Biological sciences, Italy, MAP, Adult, Adolescent, Genotype, WGS imputation panel, Population, Single-nucleotide polymorphism, FORMAT, Computational biology, GENOTYPE IMPUTATION, Polymorphism, Single Nucleotide, Article, White People, General Biochemistry, Genetics and Molecular Biology, Young Adult, 03 medical and health sciences, Humans, GENOME-WIDE ASSOCIATION, 1000 Genomes Project, education, Allele frequency, Alleles, Aged, 030304 developmental biology, Whole-genome sequencing, Models, Statistical, Models, Genetic, Genome, Human, Genetic Variation, General Chemistry, United Kingdom, Minor allele frequency, Renal disorders Radboud Institute for Molecular Life Sciences [Radboudumc 11], Haplotypes, 3111 Biomedicine, 030217 neurology & neurosurgery, Imputation (genetics)
Abstract

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants., Imputation uses genotype information from SNP arrays to infer the genotypes of missing markers. Here, the authors show that an imputation reference panel derived from whole-genome sequencing of 3,781 samples from the UK10K project improves the imputation accuracy and coverage of low frequency variants compared to existing methods.

Published
2015

27. Dissection of Major Depressive Disorder using polygenic risk scores for Schizophrenia in two independent cohort

Author

Whalley, HC, primary, Adams, MJ, additional, Hall, LS, additional, Clarke, T-K, additional, Fernandez-Pujals, AM, additional, Gibson, J, additional, Wigmore, E, additional, Hafferty, Jonathan, additional, Hagenaars, SP, additional, Davies, G, additional, Campbell, A, additional, Hayward, C, additional, Lawrie, SM, additional, Porteous, DJ, additional, Deary, IJ, additional, and McIntosh, AM, additional

Published
2016
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28. Intelligence and neuroticism in relation to depression and psychological distress: evidence of interaction using data from Generation Scotland: Scottish Family Health Study and UK Biobank

Author

Navrady, LB, primary, Ritchie, SJ, additional, Chan, SWY, additional, Kerr, DM, additional, Adams, MJ, additional, Hawkins, E, additional, Porteous, DJ, additional, Deary, IJ, additional, Gale, CR, additional, Batty, GD, additional, and McIntosh, AM, additional

Published
2016
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29. Infectious diseases and their prevention across the seven ages of Man (English)

Author

E. DAVID G. MCINTOSH AM and Namazova-baranova, L.

Subjects
virus diseases
Abstract

This paper will describe some important infectious diseases and their prevention, to illustrate the interconnectedness of people at different ages. The diseases of interest are congenital infections, bacterial meningitis, rotavirus and human papillomavirus, healthcare associated infections, and influenza. Vaccination of individuals in one age group, for example children, may confer benefits on adults and vice versa. Of particular interest is vaccination of the pregnant woman, especially with regards to pertussis and influenza.

Published
2012

35. Structural magnetic resonance imaging markers of susceptibility and transition to schizophrenia: A review of familial and clinical high risk population studies.

Author

Bois, C, Whalley, HC, McIntosh, AM, and Lawrie, SM

Subjects
DIAGNOSIS of schizophrenia, MAGNETIC resonance imaging of the brain, DISEASE susceptibility, SYMPTOMS, PSYCHOSES risk factors
Abstract

There is a growing consensus that a symptomatology as complex and heterogeneous as schizophrenia is likely to be produced by widespread perturbations of brain structure, as opposed to isolated deficits in specific brain regions. Structural brain-imaging studies have shown that several features of the brain, such as grey matter, white matter integrity and the morphology of the cortex differ in individuals at high risk of the disorder compared to controls, but to a lesser extent than in patients, suggesting that structural abnormalities may form markers of vulnerability to the disorder. Research has had some success in delineating abnormalities specific to those individuals that transition to psychosis, compared to those at high risk that do not, suggesting that a general risk for the disorder can be distinguished from alterations specific to frank psychosis. In this paper, we review cross-sectional and longitudinal studies of individuals at familial or clinical high risk of the disorder. We conclude that the search for reliable markers of schizophrenia is likely to be enhanced by methods which amalgamate structural neuroimaging data into a coherent framework that takes into account the widespread distribution of brain alterations, and relates this to leading hypotheses of schizophrenia. [ABSTRACT FROM PUBLISHER]

Published
2015
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36. Hippocampal function in schizophrenia and bipolar disorder.

Author

Hall J, Whalley HC, Marwick K, McKirdy J, Sussmann J, Romaniuk L, Johnstone EC, Wan HI, McIntosh AM, and Lawrie SM

Abstract

BACKGROUND: The hippocampus plays a central role in memory formation. There is considerable evidence of abnormalities in hippocampal structure and function in schizophrenia, which may differentiate it from bipolar disorder. However, no previous studies have compared hippocampal activation in schizophrenia and bipolar disorder directly. METHOD: Fifteen patients with schizophrenia, 14 patients with bipolar disorder and 14 healthy comparison subjects took part in the study. Subjects performed a face-name pair memory task during functional magnetic resonance imaging (fMRI). Differences in blood oxygen level-dependent (BOLD) activity were determined during encoding and retrieval of the face-name pairs. RESULTS: The patient groups showed significant differences in hippocampal and prefrontal cortex (PFC) activation during face-name pair learning. During encoding, patients with schizophrenia showed decreased anterior hippocampal activation relative to subjects with bipolar disorder, whereas patients with bipolar disorder showed decreased dorsal PFC activation relative to patients with schizophrenia. During retrieval, patients with schizophrenia showed greater activation of the dorsal PFC than patients with bipolar disorder. Patients with schizophrenia also differed from healthy control subjects in the activation of several brain regions, showing impaired superior temporal cortex activation during encoding and greater dorsal PFC activation during retrieval. These effects were evident despite matched task performance. CONCLUSIONS: Patients with schizophrenia showed deficits in hippocampal activation during a memory task relative to patients with bipolar disorder. The disorders were further distinguished by differences in PFC activation. The results demonstrate that these disorders can distinguished at a group level using non-invasive neuroimaging. [ABSTRACT FROM AUTHOR]

Published
2010
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40. The Herbert Moran Oration

Author

McINTOSH Am

Subjects
History, Association (object-oriented programming), General Medicine, Demography
Published
1951

41. SURGERY IN SYDNEY IN THE 1830's

Author

Mcintosh Am

Subjects
medicine.medical_specialty, business.industry, medicine, General Medicine, business, Surgery
Published
1956

42. ARMY MEDICAL SERVICES IN NEW SOUTH WALES PRIOR TO FEDERATION

Author

McINTOSH Am

Subjects
Medical services, medicine.medical_specialty, Military Personnel, business.industry, Family medicine, Australia, medicine, Humans, Optometry, General Medicine, New South Wales, Military Medicine, business
Published
1948

43. D <scp>r</scp> . ISAAC AARON

Author

Mcintosh Am

Subjects
History, Humans, History, 19th Century, General Medicine, History, 18th Century
Published
1957

46. A NOTE ON THE ROYAL ARMY MEDICAL CORPS

Author

McINTOSH Am

Subjects
medicine.medical_specialty, Military personnel, business.industry, Ophthalmology, medicine, General Medicine, Medical emergency, medicine.disease, business, Military medicine
Published
1948

48. Low birthweight and preterm birth in young people with special educational needs: a magnetic resonance imaging analysis.

Author

Spencer MD, Moorhead TW, Gibson RJ, McIntosh AM, Sussmann JE, Owens DG, Lawrie SM, Johnstone EC, Spencer, Michael D, Moorhead, T William J, Gibson, Rod J, McIntosh, Andrew M, Sussmann, Jessika E D, Owens, David G C, Lawrie, Stephen M, and Johnstone, Eve C

Abstract

Background: Although neuroanatomical and cognitive sequelae of low birthweight and preterm birth have been investigated, little is understood as to the likely prevalence of a history of low birthweight or preterm birth, or neuroanatomical correlates of such a history, within the special educational needs population. Our aim was to address these issues in a sample of young people receiving additional learning support.Methods: One hundred and thirty-seven participants aged 13-22 years, receiving additional learning support, were recruited via their schools or colleges and underwent structural magnetic resonance imaging (MRI). Obstetric records, available in 98 cases, included birthweight and gestational data in 90 and 95 cases, respectively. Both qualitative and quantitative voxel-based analyses of MRI data were conducted.Results: A history of low birthweight and preterm birth was present in 13.3% and 13.7% of cases, respectively. Low birthweight and preterm birth were associated with specific qualitative anomalies, including enlargement of subarachnoid cisterns and thinning of the corpus callosum. Low birthweight was associated with reduced grey matter density (GMD) in the superior temporal gyrus (STG) bilaterally, left inferior temporal gyrus and left insula. Prematurity of birth was associated with reduced GMD in the STG bilaterally, right inferior frontal gyrus and left cerebellar hemisphere. Comparison of subjects with no history of low birthweight or preterm birth with a previously defined control sample of cognitively unimpaired adolescents (n = 72) demonstrated significantly greater scores for several anomalies, including thinning of the corpus callosum, loss of white matter and abnormalities of shape of the lateral ventricles.Conclusion: Although a two-fold increased prevalence of a history of low birthweight and preterm birth exists within the special educational needs population, other aetiological factors must be considered for the overwhelming majority of cases. Neuroanatomical findings within this sample include qualitative anomalies of brain structure and grey matter deficits within temporal lobe structures and the cerebellum that persist into adolescence. These findings suggest a neurodevelopmental mechanism for the cognitive difficulties associated with these obstetric risk factors. [ABSTRACT FROM AUTHOR]

Published
2008
Full Text
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49. Risperidone reduced the risk of relapse in outpatient schizophrenia and schizoaffective disorder.

Author

McIntosh AM

Abstract

QUESTION: In outpatients with schizophrenia or schizoaffective disorder, is risperidone more effective than haloperidol for reducing the risk of relapse?DesignRandomised {allocation concealed*}**, blinded {patients and clinicians*}**,* controlled trial with follow up to time of treatment discontinuation (Risperidone-USA-79 Study).Setting40 sites in the US.Patients397 patients who were 18-65 years of age, had a diagnosis of schizophrenia or schizoaffective disorder according to DSM-IV criteria, had required intensive psychiatric treatment within 24 months before study entry, had received a stable dose of antipsychotic medication for >/=30 days before study entry, had resided at the same address for >/=30 days before study entry, and were judged clinically stable by the principal investigator. Exclusion criteria included another current DSM-IV Axis I diagnosis and an Axis II diagnosis of borderline personality disorder or antisocial personality disorder. 365 patients (92%) (mean age 40 y, 70% men, 48% white) were included in the analysis.InterventionPatients were allocated to 1 of 2 groups. Risperidone was increased from 1 mg/day to 4 mg/day on days 1-3 and doses were adjusted in the range of 2 mg/day to 8 mg/day from day 8 onward Haloperidol was increased from 2 mg/day to 10 mg/day on days 1-3 and doses were adjusted in the range of 5 mg/day to 20 mg/day from day 8 onward. Lower doses were permitted in rare cases. Antipsychotic medications taken before study entry were gradually reduced during days 1-7 to minimise possible adverse events associated with medication discontinuation.Main outcome measuresRelapse rates anti time to first relapse.Main resultsAnalyses over the 2.2 year period were done on patients who were assessed at least once during treatment, and data for the time to relapse analysis were censored at time of treatment discontinuation. Median duration of treatment was 364 days in the risperidone group (n=177) and 238 days in the haloperidol group (n=188). Relapse occurred in 45 patients (25%) receiving risperidone and in 75 patients (40%) receiving haloperidol. Risk of relapse was lower in the risperidone group than in the haloperidol group (table).ConclusionIn patients with schizophrenia or schizoaffective disorder in the outpatient setting, risperidone reduced the risk of relapse more than haloperidol.*See glossary.**Information provided by author. [ABSTRACT FROM AUTHOR]

Published
2002

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