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1. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries

Author

García-Marín, Luis M., Campos, Adrian I., Diaz-Torres, Santiago, Rabinowitz, Jill A., Ceja, Zuriel, Mitchell, Brittany L., Grasby, Katrina L., Thorp, Jackson G., Agartz, Ingrid, Alhusaini, Saud, Ames, David, Amouyel, Philippe, Andreassen, Ole A., Arfanakis, Konstantinos, Arias-Vasquez, Alejandro, Armstrong, Nicola J., Athanasiu, Lavinia, Bastin, Mark E., Beiser, Alexa S., Bennett, David A., Bis, Joshua C., Boks, Marco P. M., Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buitelaar, Jan K., Burkhardt, Ralph, Cahn, Wiepke, Calhoun, Vince D., Carmichael, Owen T., Chakravarty, Mallar, Chen, Qiang, Ching, Christopher R. K., Cichon, Sven, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dale, Anders M., Smith, George Davey, de Geus, Eco J. C., De Jager, Philip L., de Zubicaray, Greig I., Debette, Stéphanie, DeCarli, Charles, Depondt, Chantal, Desrivières, Sylvane, Djurovic, Srdjan, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fernández, Guillén, Filippi, Irina, Fisher, Simon E., Fleischman, Debra A., Fletcher, Evan, Fornage, Myriam, Forstner, Andreas J., Francks, Clyde, Franke, Barbara, Ge, Tian, Goldman, Aaron L., Grabe, Hans J., Green, Robert C., Grimm, Oliver, Groenewold, Nynke A., Gruber, Oliver, Gudnason, Vilmundur, Håberg, Asta K., Haukvik, Unn K., Heinz, Andreas, Hibar, Derrek P., Hilal, Saima, Himali, Jayandra J., Ho, Beng-Choon, Hoehn, David F., Hoekstra, Pieter J., Hofer, Edith, Hoffmann, Wolfgang, Holmes, Avram J., Homuth, Georg, Hosten, Norbert, Ikram, M. Kamran, Ipser, Jonathan C., Jack Jr, Clifford R., Jahanshad, Neda, Jönsson, Erik G., Kahn, Rene S., Kanai, Ryota, Klein, Marieke, Knol, Maria J., Launer, Lenore J., Lawrie, Stephen M., Hellard, Stephanie Le, Lee, Phil H., Lemaître, Hervé, Li, Shuo, Liewald, David C. M., Lin, Honghuang, Longstreth, Jr, W. T., Lopez, Oscar L., Luciano, Michelle, Maillard, Pauline, Marquand, Andre F., Martin, Nicholas G., Martinot, Jean-Luc, Mather, Karen A., Mattay, Venkata S., McMahon, Katie L., Mecocci, Patrizia, Melle, Ingrid, Meyer-Lindenberg, Andreas, Mirza-Schreiber, Nazanin, Milaneschi, Yuri, Mosley, Thomas H., Mühleisen, Thomas W., Müller-Myhsok, Bertram, Maniega, Susana Muñoz, Nauck, Matthias, Nho, Kwangsik, Niessen, Wiro J., Nöthen, Markus M., Nyquist, Paul A., Oosterlaan, Jaap, Pandolfo, Massimo, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda W. J. H., Pike, G. Bruce, Psaty, Bruce M., Pütz, Benno, Reppermund, Simone, Rietschel, Marcella D., Risacher, Shannon L., Romanczuk-Seiferth, Nina, Romero-Garcia, Rafael, Roshchupkin, Gennady V., Rotter, Jerome I., Sachdev, Perminder S., Sämann, Philipp G., Saremi, Arvin, Sargurupremraj, Muralidharan, Saykin, Andrew J., Schmaal, Lianne, Schmidt, Helena, Schmidt, Reinhold, Schofield, Peter R., Scholz, Markus, Schumann, Gunter, Schwarz, Emanuel, Shen, Li, Shin, Jean, Sisodiya, Sanjay M., Smith, Albert V., Smoller, Jordan W., Soininen, Hilkka S., Steen, Vidar M., Stein, Dan J., Stein, Jason L., Thomopoulos, Sophia I., Toga, Arthur W., Tordesillas-Gutiérrez, Diana, Trollor, Julian N., Valdes-Hernandez, Maria C., van ′t Ent, Dennis, van Bokhoven, Hans, van der Meer, Dennis, van der Wee, Nic J. A., Vázquez-Bourgon, Javier, Veltman, Dick J., Vernooij, Meike W., Villringer, Arno, Vinke, Louis N., Völzke, Henry, Walter, Henrik, Wardlaw, Joanna M., Weinberger, Daniel R., Weiner, Michael W., Wen, Wei, Westlye, Lars T., Westman, Eric, White, Tonya, Witte, A. Veronica, Wolf, Christiane, Yang, Jingyun, Zwiers, Marcel P., Ikram, M. Arfan, Seshadri, Sudha, Thompson, Paul M., Satizabal, Claudia L., Medland, Sarah E., and Rentería, Miguel E.

Published
2024
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2. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers.

Author

Boen, Rune, Kaufmann, Tobias, van der Meer, Dennis, Frei, Oleksandr, Agartz, Ingrid, Ames, David, Andersson, Micael, Armstrong, Nicola, Artiges, Eric, Atkins, Joshua, Bauer, Jochen, Benedetti, Francesco, Boomsma, Dorret, Brodaty, Henry, Brosch, Katharina, Buckner, Randy, Cairns, Murray, Calhoun, Vince, Caspers, Svenja, Cichon, Sven, Corvin, Aiden, Crespo-Facorro, Benedicto, Dannlowski, Udo, David, Friederike, de Geus, Eco, de Zubicaray, Greig, Desrivières, Sylvane, Doherty, Joanne, Donohoe, Gary, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fisher, Simon, Forstner, Andreas, Fortaner-Uyà, Lidia, Frouin, Vincent, Fukunaga, Masaki, Ge, Tian, Glahn, David, Goltermann, Janik, Grabe, Hans, Green, Melissa, Groenewold, Nynke, Grotegerd, Dominik, Grøntvedt, Gøril, Hahn, Tim, Hashimoto, Ryota, Hehir-Kwa, Jayne, Henskens, Frans, Holmes, Avram, Håberg, Asta, Haavik, Jan, Jacquemont, Sebastien, Jansen, Andreas, Jockwitz, Christiane, Jönsson, Erik, Kikuchi, Masataka, Kircher, Tilo, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David, Liu, Jingyu, Loughnan, Robert, Mather, Karen, McMahon, Katie, McRae, Allan, Medland, Sarah, Meinert, Susanne, Moreau, Clara, Morris, Derek, Mowry, Bryan, Mühleisen, Thomas, Nenadić, Igor, Nöthen, Markus, Nyberg, Lars, Ophoff, Roel, Owen, Michael, Pantelis, Christos, Paolini, Marco, Paus, Tomas, Pausova, Zdenka, Persson, Karin, Quidé, Yann, Marques, Tiago, Sachdev, Perminder, Sando, Sigrid, Schall, Ulrich, Scott, Rodney, Selbæk, Geir, Shumskaya, Elena, Silva, Ana, Sisodiya, Sanjay, Stein, Frederike, Stein, Dan, Straube, Benjamin, Streit, Fabian, Strike, Lachlan, Teumer, Alexander, and Teutenberg, Lea

Subjects
15q11.2 BP1-BP2, 1q21.1 distal, Brain structure, Copy number variants, Intraindividual variability, Magnetic resonance imaging, Humans, Chromosome Deletion, Brain, Magnetic Resonance Imaging, Abnormalities, Multiple, Chromosomes, Human, Pair 15, DNA Copy Number Variations
Abstract

BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individuals regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.

Published
2024

3. Image Memorability Prediction with Vision Transformers

Author

Hagen, Thomas and Espeseth, Thomas

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Behavioral studies have shown that the memorability of images is similar across groups of people, suggesting that memorability is a function of the intrinsic properties of images, and is unrelated to people's individual experiences and traits. Deep learning networks can be trained on such properties and be used to predict memorability in new data sets. Convolutional neural networks (CNN) have pioneered image memorability prediction, but more recently developed vision transformer (ViT) models may have the potential to yield even better predictions. In this paper, we present the ViTMem, a new memorability model based on ViT, and evaluate memorability predictions obtained by it with state-of-the-art CNN-derived models. Results showed that ViTMem performed equal to or better than state-of-the-art models on all data sets. Additional semantic level analyses revealed that ViTMem is particularly sensitive to the semantic content that drives memorability in images. We conclude that ViTMem provides a new step forward, and propose that ViT-derived models can replace CNNs for computational prediction of image memorability. Researchers, educators, advertisers, visual designers and other interested parties can leverage the model to improve the memorability of their image material.

Published
2023

8. Effects of copy number variations on brain structure and risk for psychiatric illness: Large‐scale studies from the ENIGMA working groups on CNVs

Author

Sønderby, Ida E, Ching, Christopher RK, Thomopoulos, Sophia I, van der Meer, Dennis, Sun, Daqiang, Villalon‐Reina, Julio E, Agartz, Ingrid, Amunts, Katrin, Arango, Celso, Armstrong, Nicola J, Ayesa‐Arriola, Rosa, Bakker, Geor, Bassett, Anne S, Boomsma, Dorret I, Bülow, Robin, Butcher, Nancy J, Calhoun, Vince D, Caspers, Svenja, Chow, Eva WC, Cichon, Sven, Ciufolini, Simone, Craig, Michael C, Crespo‐Facorro, Benedicto, Cunningham, Adam C, Dale, Anders M, Dazzan, Paola, de Zubicaray, Greig I, Djurovic, Srdjan, Doherty, Joanne L, Donohoe, Gary, Draganski, Bogdan, Durdle, Courtney A, Ehrlich, Stefan, Emanuel, Beverly S, Espeseth, Thomas, Fisher, Simon E, Ge, Tian, Glahn, David C, Grabe, Hans J, Gur, Raquel E, Gutman, Boris A, Haavik, Jan, Håberg, Asta K, Hansen, Laura A, Hashimoto, Ryota, Hibar, Derrek P, Holmes, Avram J, Hottenga, Jouke‐Jan, Pol, Hilleke E Hulshoff, Jalbrzikowski, Maria, Knowles, Emma EM, Kushan, Leila, Linden, David EJ, Liu, Jingyu, Lundervold, Astri J, Martin‐Brevet, Sandra, Martínez, Kenia, Mather, Karen A, Mathias, Samuel R, McDonald‐McGinn, Donna M, McRae, Allan F, Medland, Sarah E, Moberget, Torgeir, Modenato, Claudia, Sánchez, Jennifer Monereo, Moreau, Clara A, Mühleisen, Thomas W, Paus, Tomas, Pausova, Zdenka, Prieto, Carlos, Ragothaman, Anjanibhargavi, Reinbold, Céline S, Marques, Tiago Reis, Repetto, Gabriela M, Reymond, Alexandre, Roalf, David R, Rodriguez‐Herreros, Borja, Rucker, James J, Sachdev, Perminder S, Schmitt, James E, Schofield, Peter R, Silva, Ana I, Stefansson, Hreinn, Stein, Dan J, Tamnes, Christian K, Tordesillas‐Gutiérrez, Diana, Ulfarsson, Magnus O, Vajdi, Ariana, van 't Ent, Dennis, van den Bree, Marianne BM, Vassos, Evangelos, Vázquez‐Bourgon, Javier, Vila‐Rodriguez, Fidel, Walters, G Bragi, Wen, Wei, Westlye, Lars T, Wittfeld, Katharina, Zackai, Elaine H, Stefánsson, Kári, and Jacquemont, Sebastien

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Mental Health, Clinical Research, Pediatric, Genetics, Basic Behavioral and Social Science, Human Genome, Prevention, Brain Disorders, Behavioral and Social Science, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Mental health, Neurological, Brain, DNA Copy Number Variations, Humans, Magnetic Resonance Imaging, Mental Disorders, Multicenter Studies as Topic, Neurodevelopmental Disorders, Neuroimaging, brain structural imaging, copy number variant, diffusion tensor imaging, evolution, genetics-first approach, neurodevelopmental disorders, psychiatric disorders, ENIGMA-CNV Working Group, ENIGMA 22q11.2 Deletion Syndrome Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.

Published
2022

9. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Author

Dima, Danai, Modabbernia, Amirhossein, Papachristou, Efstathios, Doucet, Gaelle E, Agartz, Ingrid, Aghajani, Moji, Akudjedu, Theophilus N, Albajes-Eizagirre, Anton, Alnaes, Dag, Alpert, Kathryn I, Andersson, Micael, Andreasen, Nancy C, Andreassen, Ole A, Asherson, Philip, Banaschewski, Tobias, Bargallo, Nuria, Baumeister, Sarah, Baur-Streubel, Ramona, Bertolino, Alessandro, Bonvino, Aurora, Boomsma, Dorret I, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Brodaty, Henry, Brouwer, Rachel M, Buitelaar, Jan K, Busatto, Geraldo F, Buckner, Randy L, Calhoun, Vincent, Canales-Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Castellanos, Francisco X, Cervenka, Simon, Chaim-Avancini, Tiffany M, Ching, Christopher RK, Chubar, Victoria, Clark, Vincent P, Conrod, Patricia, Conzelmann, Annette, Crespo-Facorro, Benedicto, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Dale, Anders M, Davey, Christopher, de Geus, Eco JC, de Haan, Lieuwe, de Zubicaray, Greig I, den Braber, Anouk, Dickie, Erin W, Di Giorgio, Annabella, Doan, Nhat Trung, Dørum, Erlend S, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fatouros-Bergman, Helena, Fisher, Simon E, Fouche, Jean-Paul, Franke, Barbara, Frodl, Thomas, Fuentes-Claramonte, Paola, Glahn, David C, Gotlib, Ian H, Grabe, Hans-Jörgen, Grimm, Oliver, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Gur, Rachel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hartman, Catharine A, Hatton, Sean N, Heinz, Andreas, Heslenfeld, Dirk J, Hibar, Derrek P, Hickie, Ian B, Ho, Beng-Choon, Hoekstra, Pieter J, Hohmann, Sarah, Holmes, Avram J, Hoogman, Martine, Hosten, Norbert, Howells, Fleur M, Hulshoff Pol, Hilleke E, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Jernigan, Terry L, Jiang, Jiyang, Jönsson, Erik G, Joska, John A, Kahn, Rene, and Kalnin, Andrew

Subjects
Karolinska Schizophrenia Project, Amygdala, Hippocampus, Thalamus, Corpus Striatum, Humans, Human Development, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Female, Male, Young Adult, Neuroimaging, ENIGMA, brain morphometry, longitudinal trajectories, multisite, Neurosciences, Aging, Aetiology, 2.1 Biological and endogenous factors, Mental health, Cognitive Sciences, Experimental Psychology
Abstract

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.

Published
2022

10. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3–90 years

Author

Frangou, Sophia, Modabbernia, Amirhossein, Williams, Steven CR, Papachristou, Efstathios, Doucet, Gaelle E, Agartz, Ingrid, Aghajani, Moji, Akudjedu, Theophilus N, Albajes‐Eizagirre, Anton, Alnæs, Dag, Alpert, Kathryn I, Andersson, Micael, Andreasen, Nancy C, Andreassen, Ole A, Asherson, Philip, Banaschewski, Tobias, Bargallo, Nuria, Baumeister, Sarah, Baur‐Streubel, Ramona, Bertolino, Alessandro, Bonvino, Aurora, Boomsma, Dorret I, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Brodaty, Henry, Brouwer, Rachel M, Buitelaar, Jan K, Busatto, Geraldo F, Buckner, Randy L, Calhoun, Vincent, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Castellanos, Francisco X, Cervenka, Simon, Chaim‐Avancini, Tiffany M, Ching, Christopher RK, Chubar, Victoria, Clark, Vincent P, Conrod, Patricia, Conzelmann, Annette, Crespo‐Facorro, Benedicto, Crivello, Fabrice, Crone, Eveline A, Dale, Anders M, Dannlowski, Udo, Davey, Christopher, Geus, Eco JC, Haan, Lieuwe, Zubicaray, Greig I, Braber, Anouk, Dickie, Erin W, Di Giorgio, Annabella, Doan, Nhat Trung, Dørum, Erlend S, Ehrlich, Stefan, Erk, Susanne, Espeseth, Thomas, Fatouros‐Bergman, Helena, Fisher, Simon E, Fouche, Jean‐Paul, Franke, Barbara, Frodl, Thomas, Fuentes‐Claramonte, Paola, Glahn, David C, Gotlib, Ian H, Grabe, Hans‐Jörgen, Grimm, Oliver, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Gur, Rachel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hartman, Catharine A, Hatton, Sean N, Heinz, Andreas, Heslenfeld, Dirk J, Hibar, Derrek P, Hickie, Ian B, Ho, Beng‐Choon, Hoekstra, Pieter J, Hohmann, Sarah, Holmes, Avram J, Hoogman, Martine, Hosten, Norbert, Howells, Fleur M, Pol, Hilleke E Hulshoff, Huyser, Chaim, Jahanshad, Neda, James, Anthony, Jernigan, Terry L, Jiang, Jiyang, Jönsson, Erik G, Joska, John A, and Kahn, Rene

Subjects
Behavioral and Social Science, Neurosciences, Aging, Adolescent, Adult, Aged, Aged, 80 and over, Cerebral Cortex, Child, Child, Preschool, Cross-Sectional Studies, Female, Human Development, Humans, Male, Middle Aged, Neuroimaging, Young Adult, aging, cortical thickness, development, trajectories, Karolinska Schizophrenia Project, Cognitive Sciences, Experimental Psychology
Abstract

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.

Published
2022

12. Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

Author

Lam, Max, Chen, Chia-Yen, Ge, Tian, Xia, Yan, Hill, David W, Trampush, Joey W, Yu, Jin, Knowles, Emma, Davies, Gail, Stahl, Eli A, Huckins, Laura, Liewald, David C, Djurovic, Srdjan, Melle, Ingrid, Christoforou, Andrea, Reinvang, Ivar, DeRosse, Pamela, Lundervold, Astri J, Steen, Vidar M, Espeseth, Thomas, Räikkönen, Katri, Widen, Elisabeth, Palotie, Aarno, Eriksson, Johan G, Giegling, Ina, Konte, Bettina, Hartmann, Annette M, Roussos, Panos, Giakoumaki, Stella, Burdick, Katherine E, Payton, Antony, Ollier, William, Chiba-Falek, Ornit, Koltai, Deborah C, Need, Anna C, Cirulli, Elizabeth T, Voineskos, Aristotle N, Stefanis, Nikos C, Avramopoulos, Dimitrios, Hatzimanolis, Alex, Smyrnis, Nikolaos, Bilder, Robert M, Freimer, Nelson B, Cannon, Tyrone D, London, Edythe, Poldrack, Russell A, Sabb, Fred W, Congdon, Eliza, Conley, Emily Drabant, Scult, Matthew A, Dickinson, Dwight, Straub, Richard E, Donohoe, Gary, Morris, Derek, Corvin, Aiden, Gill, Michael, Hariri, Ahmad R, Weinberger, Daniel R, Pendleton, Neil, Bitsios, Panos, Rujescu, Dan, Lahti, Jari, Le Hellard, Stephanie, Keller, Matthew C, Andreassen, Ole A, Deary, Ian J, Glahn, David C, Huang, Hailiang, Liu, Chunyu, Malhotra, Anil K, and Lencz, Todd

Subjects
Humans, Nootropic Agents, Cognition, Schizophrenia, Genome-Wide Association Study, Transcriptome, Psychiatry, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Published
2021

13. Large-scale detection and categorization of oil spills from SAR images with deep learning

Author

Bianchi, Filippo Maria, Espeseth, Martine M., and Borch, Njål

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. By means of a carefully designed neural network model for image segmentation trained on an extensive dataset, we are able to obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories pertaining to its shape and texture characteristics. The classification results provide valuable insights for improving the design of oil spill services by world-leading providers. As the last contribution, we present our operational pipeline and a visualization tool for large-scale data, which allows to detect and analyze the historical presence of oil spills worldwide.

Published
2020

14. 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Author

Sønderby, Ida E, van der Meer, Dennis, Moreau, Clara, Kaufmann, Tobias, Walters, G Bragi, Ellegaard, Maria, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Micael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas B, Blangero, John, Boomsma, Dorret I, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Bøen, Rune, Cahn, Wiepke, Calhoun, Vince D, Caspers, Svenja, Ching, Christopher RK, Cichon, Sven, Ciufolini, Simone, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Desrivieres, Sylvane, Doherty, Joanne L, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Eising, Else, Espeseth, Thomas, Fejgin, Kim, Fisher, Simon E, Fladby, Tormod, Frei, Oleksandr, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Ge, Tian, Glahn, David C, Grabe, Hans J, Groenewold, Nynke A, Gústafsson, Ómar, Haavik, Jan, Haberg, Asta K, Hall, Jeremy, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Hibar, Derrek P, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Holmes, Avram J, Homuth, Georg, Hottenga, Jouke-Jan, Hulshoff Pol, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jockwitz, Christiane, Johansson, Stefan, Jönsson, Erik G, Jørgensen, Niklas R, Kikuchi, Masataka, Knowles, Emma EM, Kumar, Kuldeep, Le Hellard, Stephanie, Leu, Costin, Linden, David EJ, Liu, Jingyu, Lundervold, Arvid, Lundervold, Astri Johansen, Maillard, Anne M, Martin, Nicholas G, Martin-Brevet, Sandra, Mather, Karen A, Mathias, Samuel R, McMahon, Katie L, McRae, Allan F, Medland, Sarah E, Meyer-Lindenberg, Andreas, Moberget, Torgeir, Modenato, Claudia, Sánchez, Jennifer Monereo, Morris, Derek W, Mühleisen, Thomas W, Murray, Robin M, Nielsen, Jacob, Nordvik, Jan E, Nyberg, Lars, Loohuis, Loes M Olde, and Ophoff, Roel A

Subjects
ENIGMA-CNV working group, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Published
2021

17. Baseline Pupil Size Seems Unrelated to Fluid Intelligence, Working Memory Capacity, and Attentional Control

Author

Veera Ruuskanen, Thomas Hagen, Thomas Espeseth, and Sebastiaan Mathôt

Subjects
baseline pupil size, fluid intelligence, working memory capacity, attentional control, Consciousness. Cognition, BF309-499
Abstract

Over the past few years, several studies have explored the relationship between resting-state baseline pupil size and cognitive abilities, including fluid intelligence, working memory capacity, and attentional control. However, the results have been inconsistent. Here we present the findings from two experiments designed to replicate and expand previous research, with the aim of clarifying previous mixed findings. In both experiments, we measured baseline pupil size while participants were not engaged in any tasks, and assessed fluid intelligence using a matrix task. In one experiment we also measured working memory capacity (letter-number-sequencing task) and attentional control (attentional-capture task). We controlled for several personal and demographic variables known to influence pupil size, such as age and nicotine consumption. Our analyses revealed no relationship between resting-state pupil size (average or variability) and any of the measured constructs, neither before nor after controlling for confounding variables. Taken together, our results suggest that any relationship between resting-state pupil size and cognitive abilities is likely to be weak or non-existent.

Published
2024
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18. Considerations on brain age predictions from repeatedly sampled data across time

Author

Max Korbmacher, Meng‐Yun Wang, Rune Eikeland, Ralph Buchert, Ole A. Andreassen, Thomas Espeseth, Esten Leonardsen, Lars T. Westlye, Ivan I. Maximov, and Karsten Specht

Subjects
brain age, densely sampled MRI, field strength, magnetic resonance imaging, scan quality, T1‐weighted, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Introduction Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age‐matched healthy controls. Methods We used densely sampled T1‐weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross‐sectional datasets. Brain age was predicted by a pretrained deep learning model. Results We found small within‐subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross‐sectional validation data and inconclusive effects of scan quality. Conclusion The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.

Published
2023
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20. Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia

Author

Sønderby, Ida E, Gústafsson, Ómar, Doan, Nhat Trung, Hibar, Derrek P, Martin-Brevet, Sandra, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Michael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas, Blangero, John, Boomsma, Dorret I, Bralten, Janita, Brattbak, Hans-Richard, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Calhoun, Vince, Caspers, Svenja, Cavalleri, Gianpiero, Chen, Chi-Hua, Cichon, Sven, Ciufolini, Simone, Corvin, Aiden, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Delanty, Norman, den Braber, Anouk, Desrivières, Sylvane, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Espeseth, Thomas, Fisher, Simon E, Franke, Barbara, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Glahn, David C, Grabe, Hans, Groenewold, Nynke A, Haavik, Jan, Håberg, Asta, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Heinz, Andreas, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Hottenga, Jouke-Jan, Hulshoff, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jernigan, Terry, Jockwitz, Christiane, Johansson, Stefan, Jonsdottir, Gudrun A, Jönsson, Erik G, Kahn, Rene, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Knowles, Emma EM, Kolskår, Knut K, Kwok, John B, Hellard, Stephanie Le, Leu, Costin, Liu, Jingyu, Lundervold, Astri J, Lundervold, Arvid, Martin, Nicholas G, Mather, Karen, Mathias, Samuel R, McCormack, Mark, McMahon, Katie L, McRae, Allan, Milaneschi, Yuri, Moreau, Clara, Morris, Derek, Mothersill, David, Mühleisen, Thomas W, Murray, Robin, Nordvik, Jan E, Nyberg, Lars, Olde Loohuis, Loes M, Ophoff, Roel, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda, Peralta, Juan M, Pike, Bruce, and Prieto, Carlos

Subjects
Intellectual and Developmental Disabilities (IDD), Neurosciences, Brain Disorders, Autism, Biomedical Imaging, Mental Health, Mental health, Adult, Autism Spectrum Disorder, Autistic Disorder, Basal Ganglia, Brain, Chromosome Deletion, Chromosome Disorders, Chromosome Duplication, Chromosomes, Human, Pair 16, DNA Copy Number Variations, Databases, Factual, Female, Globus Pallidus, Humans, Image Processing, Computer-Assisted, Intellectual Disability, Magnetic Resonance Imaging, Male, Middle Aged, Neurodevelopmental Disorders, Organ Size, Putamen, Schizophrenia, 16p11.2 European Consortium, for the ENIGMA-CNV working group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P

Published
2020

21. Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Author

Sønderby, Ida E, Gústafsson, Ómar, Doan, Nhat Trung, Hibar, Derrek P, Martin-Brevet, Sandra, Abdellaoui, Abdel, Ames, David, Amunts, Katrin, Andersson, Michael, Armstrong, Nicola J, Bernard, Manon, Blackburn, Nicholas, Blangero, John, Boomsma, Dorret I, Bralten, Janita, Brattbak, Hans-Richard, Brodaty, Henry, Brouwer, Rachel M, Bülow, Robin, Calhoun, Vince, Caspers, Svenja, Cavalleri, Gianpiero, Chen, Chi-Hua, Cichon, Sven, Ciufolini, Simone, Corvin, Aiden, Crespo-Facorro, Benedicto, Curran, Joanne E, Dale, Anders M, Dalvie, Shareefa, Dazzan, Paola, de Geus, Eco JC, de Zubicaray, Greig I, de Zwarte, Sonja MC, Delanty, Norman, den Braber, Anouk, Desrivières, Sylvane, Donohoe, Gary, Draganski, Bogdan, Ehrlich, Stefan, Espeseth, Thomas, Fisher, Simon E, Franke, Barbara, Frouin, Vincent, Fukunaga, Masaki, Gareau, Thomas, Glahn, David C, Grabe, Hans, Groenewold, Nynke A, Haavik, Jan, Håberg, Asta, Hashimoto, Ryota, Hehir-Kwa, Jayne Y, Heinz, Andreas, Hillegers, Manon HJ, Hoffmann, Per, Holleran, Laurena, Hottenga, Jouke-Jan, Hulshoff, Hilleke E, Ikeda, Masashi, Jahanshad, Neda, Jernigan, Terry, Jockwitz, Christiane, Johansson, Stefan, Jonsdottir, Gudrun A, Jönsson, Erik G, Kahn, Rene, Kaufmann, Tobias, Kelly, Sinead, Kikuchi, Masataka, Knowles, Emma EM, Kolskår, Knut K, Kwok, John B, Hellard, Stephanie Le, Leu, Costin, Liu, Jingyu, Lundervold, Astri J, Lundervold, Arvid, Martin, Nicholas G, Mather, Karen, Mathias, Samuel R, McCormack, Mark, McMahon, Katie L, McRae, Allan, Milaneschi, Yuri, Moreau, Clara, Morris, Derek, Mothersill, David, Mühleisen, Thomas W, Murray, Robin, Nordvik, Jan E, Nyberg, Lars, Olde Loohuis, Loes M, Ophoff, Roel, Paus, Tomas, Pausova, Zdenka, Penninx, Brenda, Peralta, Juan M, Pike, Bruce, and Prieto, Carlos

Subjects
16p11.2 European Consortium, for the ENIGMA-CNV working group, Neurosciences, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Prior to and following the publication of this article the authors noted that the complete list of authors was not included in the main article and was only present in Supplementary Table 1. The author list in the original article has now been updated to include all authors, and Supplementary Table 1 has been removed. All other supplementary files have now been updated accordingly. Furthermore, in Table 1 of this Article, the replication cohort for the row Close relative in data set, n (%) was incorrect. All values have now been corrected to 0(0%). The publishers would like to apologise for this error and the inconvenience it may have caused.

Published
2020

22. The Relationship Between Polygenic Risk Scores and Cognition in Schizophrenia

Author

Richards, Alexander L, Pardiñas, Antonio F, Frizzati, Aura, Tansey, Katherine E, Lynham, Amy J, Holmans, Peter, Legge, Sophie E, Savage, Jeanne E, Agartz, Ingrid, Andreassen, Ole A, Blokland, Gabriella AM, Corvin, Aiden, Cosgrove, Donna, Degenhardt, Franziska, Djurovic, Srdjan, Espeseth, Thomas, Ferraro, Laura, Gayer-Anderson, Charlotte, Giegling, Ina, van Haren, Neeltje E, Hartmann, Annette M, Hubert, John J, Jönsson, Erik G, Konte, Bettina, Lennertz, Leonhard, Olde Loohuis, Loes M, Melle, Ingrid, Morgan, Craig, Morris, Derek W, Murray, Robin M, Nyman, Håkan, Ophoff, Roel A, van Os, Jim, Petryshen, Tracey L, Quattrone, Diego, Rietschel, Marcella, Rujescu, Dan, Rutten, Bart PF, Streit, Fabian, Strohmaier, Jana, Sullivan, Patrick F, Sundet, Kjetil, Wagner, Michael, Escott-Price, Valentina, Owen, Michael J, Donohoe, Gary, O’Donovan, Michael C, and Walters, James TR

Subjects
Genetics, Prevention, Schizophrenia, Depression, Behavioral and Social Science, Mental Health, Brain Disorders, Serious Mental Illness, Human Genome, 2.1 Biological and endogenous factors, Aetiology, 2.3 Psychological, social and economic factors, Mental health, Bipolar Disorder, Datasets as Topic, Depressive Disorder, Major, Educational Status, Genome-Wide Association Study, Humans, Intelligence, Multifactorial Inheritance, Psychotic Disorders, psychiatry, genomics, intelligence, bioinformatics, GROUP Investigators, EUGEI WP2 Group, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundCognitive impairment is a clinically important feature of schizophrenia. Polygenic risk score (PRS) methods have demonstrated genetic overlap between schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), educational attainment (EA), and IQ, but very few studies have examined associations between these PRS and cognitive phenotypes within schizophrenia cases.MethodsWe combined genetic and cognitive data in 3034 schizophrenia cases from 11 samples using the general intelligence factor g as the primary measure of cognition. We used linear regression to examine the association between cognition and PRS for EA, IQ, schizophrenia, BD, and MDD. The results were then meta-analyzed across all samples. A genome-wide association studies (GWAS) of cognition was conducted in schizophrenia cases.ResultsPRS for both population IQ (P = 4.39 × 10-28) and EA (P = 1.27 × 10-26) were positively correlated with cognition in those with schizophrenia. In contrast, there was no association between cognition in schizophrenia cases and PRS for schizophrenia (P = .39), BD (P = .51), or MDD (P = .49). No individual variant approached genome-wide significance in the GWAS.ConclusionsCognition in schizophrenia cases is more strongly associated with PRS that index cognitive traits in the general population than PRS for neuropsychiatric disorders. This suggests the mechanisms of cognitive variation within schizophrenia are at least partly independent from those that predispose to schizophrenia diagnosis itself. Our findings indicate that this cognitive variation arises at least in part due to genetic factors shared with cognitive performance in populations and is not solely due to illness or treatment-related factors, although our findings are consistent with important contributions from these factors.

Published
2020

23. Genetic architecture of subcortical brain structures in 38,851 individuals

Author

Satizabal, Claudia L, Adams, Hieab HH, Hibar, Derrek P, White, Charles C, Knol, Maria J, Stein, Jason L, Scholz, Markus, Sargurupremraj, Muralidharan, Jahanshad, Neda, Roshchupkin, Gennady V, Smith, Albert V, Bis, Joshua C, Jian, Xueqiu, Luciano, Michelle, Hofer, Edith, Teumer, Alexander, van der Lee, Sven J, Yang, Jingyun, Yanek, Lisa R, Lee, Tom V, Li, Shuo, Hu, Yanhui, Koh, Jia Yu, Eicher, John D, Desrivières, Sylvane, Arias-Vasquez, Alejandro, Chauhan, Ganesh, Athanasiu, Lavinia, Rentería, Miguel E, Kim, Sungeun, Hoehn, David, Armstrong, Nicola J, Chen, Qiang, Holmes, Avram J, den Braber, Anouk, Kloszewska, Iwona, Andersson, Micael, Espeseth, Thomas, Grimm, Oliver, Abramovic, Lucija, Alhusaini, Saud, Milaneschi, Yuri, Papmeyer, Martina, Axelsson, Tomas, Ehrlich, Stefan, Roiz-Santiañez, Roberto, Kraemer, Bernd, Håberg, Asta K, Jones, Hannah J, Pike, G Bruce, Stein, Dan J, Stevens, Allison, Bralten, Janita, Vernooij, Meike W, Harris, Tamara B, Filippi, Irina, Witte, A Veronica, Guadalupe, Tulio, Wittfeld, Katharina, Mosley, Thomas H, Becker, James T, Doan, Nhat Trung, Hagenaars, Saskia P, Saba, Yasaman, Cuellar-Partida, Gabriel, Amin, Najaf, Hilal, Saima, Nho, Kwangsik, Mirza-Schreiber, Nazanin, Arfanakis, Konstantinos, Becker, Diane M, Ames, David, Goldman, Aaron L, Lee, Phil H, Boomsma, Dorret I, Lovestone, Simon, Giddaluru, Sudheer, Le Hellard, Stephanie, Mattheisen, Manuel, Bohlken, Marc M, Kasperaviciute, Dalia, Schmaal, Lianne, Lawrie, Stephen M, Agartz, Ingrid, Walton, Esther, Tordesillas-Gutierrez, Diana, Davies, Gareth E, Shin, Jean, Ipser, Jonathan C, Vinke, Louis N, Hoogman, Martine, Jia, Tianye, Burkhardt, Ralph, Klein, Marieke, Crivello, Fabrice, Janowitz, Deborah, Carmichael, Owen, Haukvik, Unn K, Aribisala, Benjamin S, and Schmidt, Helena

Subjects
Biological Sciences, Genetics, Brain Disorders, Mental Health, Neurosciences, Human Genome, 2.1 Biological and endogenous factors, Neurological, Adult, Aged, Animals, Brain, Cohort Studies, Drosophila melanogaster, Genetic Variation, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Middle Aged, Neurodevelopmental Disorders, Organ Size, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Published
2019

24. Common brain disorders are associated with heritable patterns of apparent aging of the brain

Author

Kaufmann, Tobias, van der Meer, Dennis, Doan, Nhat Trung, Schwarz, Emanuel, Lund, Martina J, Agartz, Ingrid, Alnæs, Dag, Barch, Deanna M, Baur-Streubel, Ramona, Bertolino, Alessandro, Bettella, Francesco, Beyer, Mona K, Bøen, Erlend, Borgwardt, Stefan, Brandt, Christine L, Buitelaar, Jan, Celius, Elisabeth G, Cervenka, Simon, Conzelmann, Annette, Córdova-Palomera, Aldo, Dale, Anders M, de Quervain, Dominique JF, Di Carlo, Pasquale, Djurovic, Srdjan, Dørum, Erlend S, Eisenacher, Sarah, Elvsåshagen, Torbjørn, Espeseth, Thomas, Fatouros-Bergman, Helena, Flyckt, Lena, Franke, Barbara, Frei, Oleksandr, Haatveit, Beathe, Håberg, Asta K, Harbo, Hanne F, Hartman, Catharina A, Heslenfeld, Dirk, Hoekstra, Pieter J, Høgestøl, Einar A, Jernigan, Terry L, Jonassen, Rune, Jönsson, Erik G, Kirsch, Peter, Kłoszewska, Iwona, Kolskår, Knut K, Landrø, Nils Inge, Le Hellard, Stephanie, Lesch, Klaus-Peter, Lovestone, Simon, Lundervold, Arvid, Lundervold, Astri J, Maglanoc, Luigi A, Malt, Ulrik F, Mecocci, Patrizia, Melle, Ingrid, Meyer-Lindenberg, Andreas, Moberget, Torgeir, Norbom, Linn B, Nordvik, Jan Egil, Nyberg, Lars, Oosterlaan, Jaap, Papalino, Marco, Papassotiropoulos, Andreas, Pauli, Paul, Pergola, Giulio, Persson, Karin, Richard, Geneviève, Rokicki, Jaroslav, Sanders, Anne-Marthe, Selbæk, Geir, Shadrin, Alexey A, Smeland, Olav B, Soininen, Hilkka, Sowa, Piotr, Steen, Vidar M, Tsolaki, Magda, Ulrichsen, Kristine M, Vellas, Bruno, Wang, Lei, Westman, Eric, Ziegler, Georg C, Zink, Mathias, Andreassen, Ole A, and Westlye, Lars T

Subjects
Biological Psychology, Psychology, Rare Diseases, Mental Health, Neurosciences, Brain Disorders, Biomedical Imaging, Aging, Aetiology, 2.3 Psychological, social and economic factors, 1.1 Normal biological development and functioning, Underpinning research, Mental health, Neurological, Adolescent, Adult, Aged, Aged, 80 and over, Algorithms, Brain, Brain Diseases, Child, Child, Preschool, Female, Genome-Wide Association Study, Humans, Infant, Magnetic Resonance Imaging, Male, Mental Disorders, Middle Aged, Neuropsychological Tests, Schizophrenia, Sex Characteristics, Young Adult, Karolinska Schizophrenia Project, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract

Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.

Published
2019

25. Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways.

Author

Lam, Max, Hill, W David, Trampush, Joey W, Yu, Jin, Knowles, Emma, Davies, Gail, Stahl, Eli, Huckins, Laura, Liewald, David C, Djurovic, Srdjan, Melle, Ingrid, Sundet, Kjetil, Christoforou, Andrea, Reinvang, Ivar, DeRosse, Pamela, Lundervold, Astri J, Steen, Vidar M, Espeseth, Thomas, Räikkönen, Katri, Widen, Elisabeth, Palotie, Aarno, Eriksson, Johan G, Giegling, Ina, Konte, Bettina, Hartmann, Annette M, Roussos, Panos, Giakoumaki, Stella, Burdick, Katherine E, Payton, Antony, Ollier, William, Chiba-Falek, Ornit, Attix, Deborah K, Need, Anna C, Cirulli, Elizabeth T, Voineskos, Aristotle N, Stefanis, Nikos C, Avramopoulos, Dimitrios, Hatzimanolis, Alex, Arking, Dan E, Smyrnis, Nikolaos, Bilder, Robert M, Freimer, Nelson A, Cannon, Tyrone D, London, Edythe, Poldrack, Russell A, Sabb, Fred W, Congdon, Eliza, Conley, Emily Drabant, Scult, Matthew A, Dickinson, Dwight, Straub, Richard E, Donohoe, Gary, Morris, Derek, Corvin, Aiden, Gill, Michael, Hariri, Ahmad R, Weinberger, Daniel R, Pendleton, Neil, Bitsios, Panos, Rujescu, Dan, Lahti, Jari, Le Hellard, Stephanie, Keller, Matthew C, Andreassen, Ole A, Deary, Ian J, Glahn, David C, Malhotra, Anil K, and Lencz, Todd

Subjects
Humans, Genetic Predisposition to Disease, Cognition, Cognition Disorders, Schizophrenia, Synaptic Transmission, Polymorphism, Single Nucleotide, Adult, Educational Status, Genome-Wide Association Study, Neurodevelopmental Disorders, GWAS, cognitive ability, educational attainment, genetic correlation, pathways, pleiotropy, schizophrenia, Serious Mental Illness, Neurosciences, Biotechnology, Mental Health, Genetics, Brain Disorders, Prevention, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Mental health, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Susceptibility to schizophrenia is inversely correlated with general cognitive ability at both the phenotypic and the genetic level. Paradoxically, a modest but consistent positive genetic correlation has been reported between schizophrenia and educational attainment, despite the strong positive genetic correlation between cognitive ability and educational attainment. Here we leverage published genome-wide association studies (GWASs) in cognitive ability, education, and schizophrenia to parse biological mechanisms underlying these results. Association analysis based on subsets (ASSET), a pleiotropic meta-analytic technique, allowed jointly associated loci to be identified and characterized. Specifically, we identified subsets of variants associated in the expected ("concordant") direction across all three phenotypes (i.e., greater risk for schizophrenia, lower cognitive ability, and lower educational attainment); these were contrasted with variants that demonstrated the counterintuitive ("discordant") relationship between education and schizophrenia (i.e., greater risk for schizophrenia and higher educational attainment). ASSET analysis revealed 235 independent loci associated with cognitive ability, education, and/or schizophrenia at p < 5 × 10-8. Pleiotropic analysis successfully identified more than 100 loci that were not significant in the input GWASs. Many of these have been validated by larger, more recent single-phenotype GWASs. Leveraging the joint genetic correlations of cognitive ability, education, and schizophrenia, we were able to dissociate two distinct biological mechanisms-early neurodevelopmental pathways that characterize concordant allelic variation and adulthood synaptic pruning pathways-that were linked to the paradoxical positive genetic association between education and schizophrenia. Furthermore, genetic correlation analyses revealed that these mechanisms contribute not only to the etiopathogenesis of schizophrenia but also to the broader biological dimensions implicated in both general health outcomes and psychiatric illness.

Published
2019

26. Author Correction: Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

Author

Davies, Gail, Lam, Max, Harris, Sarah E, Trampush, Joey W, Luciano, Michelle, Hill, W David, Hagenaars, Saskia P, Ritchie, Stuart J, Marioni, Riccardo E, Fawns-Ritchie, Chloe, Liewald, David CM, Okely, Judith A, Ahola-Olli, Ari V, Barnes, Catriona LK, Bertram, Lars, Bis, Joshua C, Burdick, Katherine E, Christoforou, Andrea, DeRosse, Pamela, Djurovic, Srdjan, Espeseth, Thomas, Giakoumaki, Stella, Giddaluru, Sudheer, Gustavson, Daniel E, Hayward, Caroline, Hofer, Edith, Ikram, M Arfan, Karlsson, Robert, Knowles, Emma, Lahti, Jari, Leber, Markus, Li, Shuo, Mather, Karen A, Melle, Ingrid, Morris, Derek, Oldmeadow, Christopher, Palviainen, Teemu, Payton, Antony, Pazoki, Raha, Petrovic, Katja, Reynolds, Chandra A, Sargurupremraj, Muralidharan, Scholz, Markus, Smith, Jennifer A, Smith, Albert V, Terzikhan, Natalie, Thalamuthu, Anbupalam, Trompet, Stella, van der Lee, Sven J, Ware, Erin B, Windham, B Gwen, Wright, Margaret J, Yang, Jingyun, Yu, Jin, Ames, David, Amin, Najaf, Amouyel, Philippe, Andreassen, Ole A, Armstrong, Nicola J, Assareh, Amelia A, Attia, John R, Attix, Deborah, Avramopoulos, Dimitrios, Bennett, David A, Böhmer, Anne C, Boyle, Patricia A, Brodaty, Henry, Campbell, Harry, Cannon, Tyrone D, Cirulli, Elizabeth T, Congdon, Eliza, Conley, Emily Drabant, Corley, Janie, Cox, Simon R, Dale, Anders M, Dehghan, Abbas, Dick, Danielle, Dickinson, Dwight, Eriksson, Johan G, Evangelou, Evangelos, Faul, Jessica D, Ford, Ian, Freimer, Nelson A, Gao, He, Giegling, Ina, Gillespie, Nathan A, Gordon, Scott D, Gottesman, Rebecca F, Griswold, Michael E, Gudnason, Vilmundur, Harris, Tamara B, Hartmann, Annette M, Hatzimanolis, Alex, Heiss, Gerardo, Holliday, Elizabeth G, Joshi, Peter K, Kähönen, Mika, Kardia, Sharon LR, Karlsson, Ida, and Kleineidam, Luca

Abstract

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

Published
2019

27. Deep neural networks learn general and clinically relevant representations of the ageing brain

Author

Leonardsen, Esten H., Peng, Han, Kaufmann, Tobias, Agartz, Ingrid, Andreassen, Ole A., Celius, Elisabeth Gulowsen, Espeseth, Thomas, Harbo, Hanne F., Høgestøl, Einar A., Lange, Ann-Marie de, Marquand, Andre F., Vidal-Piñeiro, Didac, Roe, James M., Selbæk, Geir, Sørensen, Øystein, Smith, Stephen M., Westlye, Lars T., Wolfers, Thomas, and Wang, Yunpeng

Published
2022
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28. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author

Brouwer, Rachel M., Klein, Marieke, Grasby, Katrina L., Schnack, Hugo G., Jahanshad, Neda, Teeuw, Jalmar, Thomopoulos, Sophia I., Sprooten, Emma, Franz, Carol E., Gogtay, Nitin, Kremen, William S., Panizzon, Matthew S., Olde Loohuis, Loes M., Whelan, Christopher D., Aghajani, Moji, Alloza, Clara, Alnæs, Dag, Artiges, Eric, Ayesa-Arriola, Rosa, Barker, Gareth J., Bastin, Mark E., Blok, Elisabet, Bøen, Erlend, Breukelaar, Isabella A., Bright, Joanna K., Buimer, Elizabeth E. L., Bülow, Robin, Cannon, Dara M., Ciufolini, Simone, Crossley, Nicolas A., Damatac, Christienne G., Dazzan, Paola, de Mol, Casper L., de Zwarte, Sonja M. C., Desrivières, Sylvane, Díaz-Caneja, Covadonga M., Doan, Nhat Trung, Dohm, Katharina, Fröhner, Juliane H., Goltermann, Janik, Grigis, Antoine, Grotegerd, Dominik, Han, Laura K. M., Harris, Mathew A., Hartman, Catharina A., Heany, Sarah J., Heindel, Walter, Heslenfeld, Dirk J., Hohmann, Sarah, Ittermann, Bernd, Jansen, Philip R., Janssen, Joost, Jia, Tianye, Jiang, Jiyang, Jockwitz, Christiane, Karali, Temmuz, Keeser, Daniel, Koevoets, Martijn G. J. C., Lenroot, Rhoshel K., Malchow, Berend, Mandl, René C. W., Medel, Vicente, Meinert, Susanne, Morgan, Catherine A., Mühleisen, Thomas W., Nabulsi, Leila, Opel, Nils, de la Foz, Víctor Ortiz-García, Overs, Bronwyn J., Paillère Martinot, Marie-Laure, Redlich, Ronny, Marques, Tiago Reis, Repple, Jonathan, Roberts, Gloria, Roshchupkin, Gennady V., Setiaman, Nikita, Shumskaya, Elena, Stein, Frederike, Sudre, Gustavo, Takahashi, Shun, Thalamuthu, Anbupalam, Tordesillas-Gutiérrez, Diana, van der Lugt, Aad, van Haren, Neeltje E. M., Wardlaw, Joanna M., Wen, Wei, Westeneng, Henk-Jan, Wittfeld, Katharina, Zhu, Alyssa H., Zugman, Andre, Armstrong, Nicola J., Bonfiglio, Gaia, Bralten, Janita, Dalvie, Shareefa, Davies, Gail, Di Forti, Marta, Ding, Linda, Donohoe, Gary, Forstner, Andreas J., Gonzalez-Peñas, Javier, Guimaraes, Joao P. O. F. T., Homuth, Georg, Hottenga, Jouke-Jan, Knol, Maria J., Kwok, John B. J., Le Hellard, Stephanie, Mather, Karen A., Milaneschi, Yuri, Morris, Derek W., Nöthen, Markus M., Papiol, Sergi, Rietschel, Marcella, Santoro, Marcos L., Steen, Vidar M., Stein, Jason L., Streit, Fabian, Tankard, Rick M., Teumer, Alexander, van ‘t Ent, Dennis, van der Meer, Dennis, van Eijk, Kristel R., Vassos, Evangelos, Vázquez-Bourgon, Javier, Witt, Stephanie H., Adams, Hieab H. H., Agartz, Ingrid, Ames, David, Amunts, Katrin, Andreassen, Ole A., Arango, Celso, Banaschewski, Tobias, Baune, Bernhard T., Belangero, Sintia I., Bokde, Arun L. W., Boomsma, Dorret I., Bressan, Rodrigo A., Brodaty, Henry, Buitelaar, Jan K., Cahn, Wiepke, Caspers, Svenja, Cichon, Sven, Crespo-Facorro, Benedicto, Cox, Simon R., Dannlowski, Udo, Elvsåshagen, Torbjørn, Espeseth, Thomas, Falkai, Peter G., Fisher, Simon E., Flor, Herta, Fullerton, Janice M., Garavan, Hugh, Gowland, Penny A., Grabe, Hans J., Hahn, Tim, Heinz, Andreas, Hillegers, Manon, Hoare, Jacqueline, Hoekstra, Pieter J., Ikram, Mohammad A., Jackowski, Andrea P., Jansen, Andreas, Jönsson, Erik G., Kahn, Rene S., Kircher, Tilo, Korgaonkar, Mayuresh S., Krug, Axel, Lemaitre, Herve, Malt, Ulrik F., Martinot, Jean-Luc, McDonald, Colm, Mitchell, Philip B., Muetzel, Ryan L., Murray, Robin M., Nees, Frauke, Nenadić, Igor, Oosterlaan, Jaap, Ophoff, Roel A., Pan, Pedro M., Penninx, Brenda W. J. H., Poustka, Luise, Sachdev, Perminder S., Salum, Giovanni A., Schofield, Peter R., Schumann, Gunter, Shaw, Philip, Sim, Kang, Smolka, Michael N., Stein, Dan J., Trollor, Julian N., van den Berg, Leonard H., Veldink, Jan H., Walter, Henrik, Westlye, Lars T., Whelan, Robert, White, Tonya, Wright, Margaret J., Medland, Sarah E., Franke, Barbara, Thompson, Paul M., and Hulshoff Pol, Hilleke E.

Published
2022
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30. A Novel Rotavirus Reverse Genetics Platform Supports Flexible Insertion of Exogenous Genes and Enables Rapid Development of a High-Throughput Neutralization Assay

Author

Jiajie Wei, Scott Radcliffe, Amanda Pirrone, Meiqing Lu, Yuan Li, Jason Cassaday, William Newhard, Gwendolyn J. Heidecker, William A. Rose II, Xi He, Daniel Freed, Michael Citron, Amy Espeseth, and Dai Wang

Subjects
rotavirus, high-throughput screening, microneutralization assay, neutralizing antibody, pre-existing immunity, Microbiology, QR1-502
Abstract

Despite the success of rotavirus vaccines, rotaviruses remain one of the leading causes of diarrheal diseases, resulting in significant childhood morbidity and mortality, especially in low- and middle-income countries. The reverse genetics system enables the manipulation of the rotavirus genome and opens the possibility of using rotavirus as an expression vector for heterologous proteins, such as vaccine antigens and therapeutic payloads. Here, we demonstrate that three positions in rotavirus genome—the C terminus of NSP1, NSP3 and NSP5—can tolerate the insertion of reporter genes. By using rotavirus expressing GFP, we develop a high-throughput neutralization assay and reveal the pre-existing immunity against rotavirus in humans and other animal species. Our work shows the plasticity of the rotavirus genome and establishes a high-throughput assay for interrogating humoral immune responses, benefiting the design of next-generation rotavirus vaccines and the development of rotavirus-based expression platforms.

Published
2023
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31. Multi-Trait Analysis of GWAS and Biological Insights Into Cognition: A Response to Hill (2018)

Author

Lam, Max, Trampush, Joey W, Yu, Jin, Knowles, Emma, Djurovic, Srdjan, Melle, Ingrid, Sundet, Kjetil, Christoforou, Andrea, Reinvang, Ivar, DeRosse, Pamela, Lundervold, Astri J, Steen, Vidar M, Espeseth, Thomas, Räikkönen, Katri, Widen, Elisabeth, Palotie, Aarno, Eriksson, Johan G, Giegling, Ina, Konte, Bettina, Roussos, Panos, Giakoumaki, Stella, Burdick, Katherine E, Payton, Antony, Ollier, William, Chiba-Falek, Ornit, Attix, Deborah K, Need, Anna C, Cirulli, Elizabeth T, Voineskos, Aristotle N, Stefanis, Nikos C, Avramopoulos, Dimitrios, Hatzimanolis, Alex, Arking, Dan E, Smyrnis, Nikolaos, Bilder, Robert M, Freimer, Nelson A, Cannon, Tyrone D, London, Edythe, Poldrack, Russell A, Sabb, Fred W, Congdon, Eliza, Conley, Emily Drabant, Scult, Matthew A, Dickinson, Dwight, Straub, Richard E, Donohoe, Gary, Morris, Derek, Corvin, Aiden, Gill, Michael, Hariri, Ahmad R, Weinberger, Daniel R, Pendleton, Neil, Bitsios, Panos, Rujescu, Dan, Lahti, Jari, Le Hellard, Stephanie, Keller, Matthew C, Andreassen, Ole A, Glahn, David C, Malhotra, Anil K, and Lencz, Todd

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Clinical Sciences, Human Genome, Genetics, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Cognition, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Nootropic Agents, Polymorphism, Single Nucleotide, GWAS, general cognitive ability, nootropics, gene expression, neurodevelopment, synapse, calcium channel, potassium channel, cerebellum, Paediatrics and Reproductive Medicine, Cognitive Sciences, Genetics & Heredity, Clinical sciences, Reproductive medicine
Abstract

Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88) presented a critique of our recently published paper in Cell Reports entitled 'Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets' (Lam et al., Cell Reports, Vol. 21, 2017, 2597-2613). Specifically, Hill offered several interrelated comments suggesting potential problems with our use of a new analytic method called Multi-Trait Analysis of GWAS (MTAG) (Turley et al., Nature Genetics, Vol. 50, 2018, 229-237). In this brief article, we respond to each of these concerns. Using empirical data, we conclude that our MTAG results do not suffer from 'inflation in the FDR [false discovery rate]', as suggested by Hill (Twin Research and Human Genetics, Vol. 21, 2018, 84-88), and are not 'more relevant to the genetic contributions to education than they are to the genetic contributions to intelligence'.

Published
2018

32. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence

Author

Savage, Jeanne E, Jansen, Philip R, Stringer, Sven, Watanabe, Kyoko, Bryois, Julien, de Leeuw, Christiaan A, Nagel, Mats, Awasthi, Swapnil, Barr, Peter B, Coleman, Jonathan RI, Grasby, Katrina L, Hammerschlag, Anke R, Kaminski, Jakob A, Karlsson, Robert, Krapohl, Eva, Lam, Max, Nygaard, Marianne, Reynolds, Chandra A, Trampush, Joey W, Young, Hannah, Zabaneh, Delilah, Hägg, Sara, Hansell, Narelle K, Karlsson, Ida K, Linnarsson, Sten, Montgomery, Grant W, Muñoz-Manchado, Ana B, Quinlan, Erin B, Schumann, Gunter, Skene, Nathan G, Webb, Bradley T, White, Tonya, Arking, Dan E, Avramopoulos, Dimitrios, Bilder, Robert M, Bitsios, Panos, Burdick, Katherine E, Cannon, Tyrone D, Chiba-Falek, Ornit, Christoforou, Andrea, Cirulli, Elizabeth T, Congdon, Eliza, Corvin, Aiden, Davies, Gail, Deary, Ian J, DeRosse, Pamela, Dickinson, Dwight, Djurovic, Srdjan, Donohoe, Gary, Conley, Emily Drabant, Eriksson, Johan G, Espeseth, Thomas, Freimer, Nelson A, Giakoumaki, Stella, Giegling, Ina, Gill, Michael, Glahn, David C, Hariri, Ahmad R, Hatzimanolis, Alex, Keller, Matthew C, Knowles, Emma, Koltai, Deborah, Konte, Bettina, Lahti, Jari, Le Hellard, Stephanie, Lencz, Todd, Liewald, David C, London, Edythe, Lundervold, Astri J, Malhotra, Anil K, Melle, Ingrid, Morris, Derek, Need, Anna C, Ollier, William, Palotie, Aarno, Payton, Antony, Pendleton, Neil, Poldrack, Russell A, Räikkönen, Katri, Reinvang, Ivar, Roussos, Panos, Rujescu, Dan, Sabb, Fred W, Scult, Matthew A, Smeland, Olav B, Smyrnis, Nikolaos, Starr, John M, Steen, Vidar M, Stefanis, Nikos C, Straub, Richard E, Sundet, Kjetil, Tiemeier, Henning, Voineskos, Aristotle N, Weinberger, Daniel R, Widen, Elisabeth, Yu, Jin, Abecasis, Goncalo, Andreassen, Ole A, Breen, Gerome, and Christiansen, Lene

Subjects
Biological Sciences, Genetics, Brain Disorders, Mental Health, Human Genome, Biotechnology, Neurosciences, Underpinning research, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Aetiology, Mental health, Neurological, Adolescent, Brain, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Intelligence, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

Published
2018

33. Williams Syndrome neuroanatomical score associates with GTF2IRD1 in large-scale magnetic resonance imaging cohorts: a proof of concept for multivariate endophenotypes.

Author

Fan, Chun Chieh, Schork, Andrew J, Brown, Timothy T, Spencer, Barbara E, Akshoomoff, Natacha, Chen, Chi-Hua, Kuperman, Joshua M, Hagler, Donald J, Steen, Vidar M, Le Hellard, Stephanie, Håberg, Asta Kristine, Espeseth, Thomas, Andreassen, Ole A, Dale, Anders M, Jernigan, Terry L, Halgren, Eric, and Pediatric Imaging, Neurocognition and Genetics Study, Alzheimer’s Disease Neuroimaging Initiative

Subjects
Pediatric Imaging, Neurocognition and Genetics Study, Alzheimer’s Disease Neuroimaging Initiative, Hippocampus, Humans, Williams Syndrome, Muscle Proteins, Trans-Activators, Nuclear Proteins, Magnetic Resonance Imaging, Multivariate Analysis, Case-Control Studies, Polymorphism, Single Nucleotide, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Child, Preschool, Europe, Female, Male, Young Adult, Endophenotypes, Proof of Concept Study, Pediatric Imaging, Neurocognition and Genetics Study, Alzheimer’s Disease Neuroimaging Initiative, Polymorphism, Single Nucleotide, and over, Preschool, Brain Disorders, Clinical Research, Biomedical Imaging, Neurosciences, Genetics, Pediatric, Mental Health, 2.1 Biological and endogenous factors, Psychology, Clinical Sciences, Public Health and Health Services
Abstract

Despite great interest in using magnetic resonance imaging (MRI) for studying the effects of genes on brain structure in humans, current approaches have focused almost entirely on predefined regions of interest and had limited success. Here, we used multivariate methods to define a single neuroanatomical score of how William's Syndrome (WS) brains deviate structurally from controls. The score is trained and validated on measures of T1 structural brain imaging in two WS cohorts (training, n = 38; validating, n = 60). We then associated this score with single nucleotide polymorphisms (SNPs) in the WS hemi-deleted region in five cohorts of neurologically and psychiatrically typical individuals (healthy European descendants, n = 1863). Among 110 SNPs within the 7q11.23 WS chromosomal region, we found one associated locus (p = 5e-5) located at GTF2IRD1, which has been implicated in animal models of WS. Furthermore, the genetic signals of neuroanatomical scores are highly enriched locally in the 7q11.23 compared with summary statistics based on regions of interest, such as hippocampal volumes (n = 12,596), and also globally (SNP-heritability = 0.82, se = 0.25, p = 5e-4). The role of genetic variability in GTF2IRD1 during neurodevelopment extends to healthy subjects. Our approach of learning MRI-derived phenotypes from clinical populations with well-established brain abnormalities characterized by known genetic lesions may be a powerful alternative to traditional region of interest-based studies for identifying genetic variants regulating typical brain development.

Published
2018

34. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function.

Author

Davies, Gail, Lam, Max, Harris, Sarah E, Trampush, Joey W, Luciano, Michelle, Hill, W David, Hagenaars, Saskia P, Ritchie, Stuart J, Marioni, Riccardo E, Fawns-Ritchie, Chloe, Liewald, David CM, Okely, Judith A, Ahola-Olli, Ari V, Barnes, Catriona LK, Bertram, Lars, Bis, Joshua C, Burdick, Katherine E, Christoforou, Andrea, DeRosse, Pamela, Djurovic, Srdjan, Espeseth, Thomas, Giakoumaki, Stella, Giddaluru, Sudheer, Gustavson, Daniel E, Hayward, Caroline, Hofer, Edith, Ikram, M Arfan, Karlsson, Robert, Knowles, Emma, Lahti, Jari, Leber, Markus, Li, Shuo, Mather, Karen A, Melle, Ingrid, Morris, Derek, Oldmeadow, Christopher, Palviainen, Teemu, Payton, Antony, Pazoki, Raha, Petrovic, Katja, Reynolds, Chandra A, Sargurupremraj, Muralidharan, Scholz, Markus, Smith, Jennifer A, Smith, Albert V, Terzikhan, Natalie, Thalamuthu, Anbupalam, Trompet, Stella, van der Lee, Sven J, Ware, Erin B, Windham, B Gwen, Wright, Margaret J, Yang, Jingyun, Yu, Jin, Ames, David, Amin, Najaf, Amouyel, Philippe, Andreassen, Ole A, Armstrong, Nicola J, Assareh, Amelia A, Attia, John R, Attix, Deborah, Avramopoulos, Dimitrios, Bennett, David A, Böhmer, Anne C, Boyle, Patricia A, Brodaty, Henry, Campbell, Harry, Cannon, Tyrone D, Cirulli, Elizabeth T, Congdon, Eliza, Conley, Emily Drabant, Corley, Janie, Cox, Simon R, Dale, Anders M, Dehghan, Abbas, Dick, Danielle, Dickinson, Dwight, Eriksson, Johan G, Evangelou, Evangelos, Faul, Jessica D, Ford, Ian, Freimer, Nelson A, Gao, He, Giegling, Ina, Gillespie, Nathan A, Gordon, Scott D, Gottesman, Rebecca F, Griswold, Michael E, Gudnason, Vilmundur, Harris, Tamara B, Hartmann, Annette M, Hatzimanolis, Alex, Heiss, Gerardo, Holliday, Elizabeth G, Joshi, Peter K, Kähönen, Mika, Kardia, Sharon LR, Karlsson, Ida, and Kleineidam, Luca

Subjects
Humans, Neurodegenerative Diseases, Genetic Predisposition to Disease, Cognition, Reaction Time, Mental Disorders, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Young Adult, Genetic Loci, Neurodevelopmental Disorders, Prevention, Behavioral and Social Science, Mental Health, Neurosciences, Biotechnology, Genetics, Human Genome, Neurological, Mental health
Abstract

General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P

Published
2018

39. Mental Effort in Elite and Nonelite Rowers.

Author

Herrebrøden, Henrik, Espeseth, Thomas, and Bishop, Laura

Subjects
*ELITE athletes, *EYE tracking, *TOUGHNESS (Personality trait), *SELECTIVITY (Psychology), *DUAL-task paradigm
Abstract

Mental effort (intensity of attention) in elite sports has remained a debated topic and a challenging phenomenon to measure. Thus, a quasi-ecological laboratory study was conducted to investigate mental effort in elite rowers as compared with a group of nonelites. Findings suggest that eye-tracking measures—specifically, blink rates and pupil size—can serve as valid indicators of mental effort in physically demanding sport tasks. Furthermore, findings contradict the notion that elite athletes spend less cognitive effort than their lower-level peers. Specifically, elites displayed similar levels of self-reported effort and performance decrement with increasing mental load and significantly more mental effort overall as measured by pupil-size increase (relative to baseline) during rowing trials as compared with the nonelites in the sample. Future studies on eye tracking in sports may include investigations of mental effort in addition to selective attention during physically demanding tasks. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Identification of pleiotropy at the gene level between psychiatric disorders and related traits

Author

Tatiana Polushina, Niladri Banerjee, Sudheer Giddaluru, Francesco Bettella, Thomas Espeseth, Astri J. Lundervold, Srdjan Djurovic, Sven Cichon, Per Hoffmann, Markus M. Nöthen, Vidar M. Steen, Ole A. Andreassen, and Stéphanie Le Hellard

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract Major mental disorders are highly prevalent and make a substantial contribution to the global disease burden. It is known that mental disorders share clinical characteristics, and genome-wide association studies (GWASs) have recently provided evidence for shared genetic factors as well. Genetic overlaps are usually identified at the single-marker level. Here, we aimed to identify genetic overlaps at the gene level between 7 mental disorders (schizophrenia, autism spectrum disorder, major depressive disorder, anorexia nervosa, ADHD, bipolar disorder and anxiety), 8 brain morphometric traits, 2 cognitive traits (educational attainment and general cognitive function) and 9 personality traits (subjective well-being, depressive symptoms, neuroticism, extraversion, openness to experience, agreeableness and conscientiousness, children’s aggressive behaviour, loneliness) based on publicly available GWASs. We performed systematic conditional regression analyses to identify independent signals and select loci associated with more than one trait. We identified 48 genes containing independent markers associated with several traits (pleiotropy at the gene level). We also report 9 genes with different markers that show independent associations with single traits (allelic heterogeneity). This study demonstrates that mental disorders and related traits do show pleiotropy at the gene level as well as the single-marker level. The identification of these genes might be important for prioritizing further deep genotyping, functional studies, or drug targeting.

Published
2021
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43. Erratum: GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium

Author

Trampush, JW, Yang, MLZ, Yu, J, Knowles, E, Davies, G, Liewald, DC, Starr, JM, Djurovic, S, Melle, I, Sundet, K, Christoforou, A, Reinvang, I, DeRosse, P, Lundervold, AJ, Steen, VM, Espeseth, T, Räikkönen, K, Widen, E, Palotie, A, Eriksson, JG, Giegling, I, Konte, B, Roussos, P, Giakoumaki, S, Burdick, KE, Payton, A, Ollier, W, Horan, M, Chiba-Falek, O, Attix, DK, Need, AC, Cirulli, ET, Voineskos, AN, Stefanis, NC, Avramopoulos, D, Hatzimanolis, A, Arking, DE, Smyrnis, N, Bilder, RM, Freimer, NA, Cannon, TD, London, E, Poldrack, RA, Sabb, FW, Congdon, E, Conley, ED, Scult, MA, Dickinson, D, Straub, RE, Donohoe, G, Morris, D, Corvin, A, Gill, M, Hariri, AR, Weinberger, DR, Pendleton, N, Bitsios, P, Rujescu, D, Lahti, J, Le Hellard, S, Keller, MC, Andreassen, OA, Deary, IJ, Glahn, DC, Malhotra, AK, and Lencz, T

Subjects
Biomedical and Clinical Sciences, Biological Psychology, Clinical and Health Psychology, Clinical Sciences, Psychology, Genetics, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

This corrects the article DOI: 10.1038/mp.2016.244.

Published
2017

44. Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets

Author

Lam, Max, Trampush, Joey W, Yu, Jin, Knowles, Emma, Davies, Gail, Liewald, David C, Starr, John M, Djurovic, Srdjan, Melle, Ingrid, Sundet, Kjetil, Christoforou, Andrea, Reinvang, Ivar, DeRosse, Pamela, Lundervold, Astri J, Steen, Vidar M, Espeseth, Thomas, Räikkönen, Katri, Widen, Elisabeth, Palotie, Aarno, Eriksson, Johan G, Giegling, Ina, Konte, Bettina, Roussos, Panos, Giakoumaki, Stella, Burdick, Katherine E, Payton, Antony, Ollier, William, Chiba-Falek, Ornit, Attix, Deborah K, Need, Anna C, Cirulli, Elizabeth T, Voineskos, Aristotle N, Stefanis, Nikos C, Avramopoulos, Dimitrios, Hatzimanolis, Alex, Arking, Dan E, Smyrnis, Nikolaos, Bilder, Robert M, Freimer, Nelson A, Cannon, Tyrone D, London, Edythe, Poldrack, Russell A, Sabb, Fred W, Congdon, Eliza, Conley, Emily Drabant, Scult, Matthew A, Dickinson, Dwight, Straub, Richard E, Donohoe, Gary, Morris, Derek, Corvin, Aiden, Gill, Michael, Hariri, Ahmad R, Weinberger, Daniel R, Pendleton, Neil, Bitsios, Panos, Rujescu, Dan, Lahti, Jari, Le Hellard, Stephanie, Keller, Matthew C, Andreassen, Ole A, Deary, Ian J, Glahn, David C, Malhotra, Anil K, and Lencz, Todd

Subjects
Biological Sciences, Genetics, Mental Health, Aging, Brain Disorders, Biotechnology, Autoimmune Disease, Neurosciences, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Underpinning research, 1.1 Normal biological development and functioning, Mental health, Neurological, Cefotaxime, Cognition, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Nootropic Agents, Polymorphism, Single Nucleotide, Synapses, GWAS, calcium channel, cerebellum, gene expression, general cognitive ability, neurodevelopment, nootropics, potassium channel, synapse, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Here, we present a large (n = 107,207) genome-wide association study (GWAS) of general cognitive ability ("g"), further enhanced by combining results with a large-scale GWAS of educational attainment. We identified 70 independent genomic loci associated with general cognitive ability. Results showed significant enrichment for genes causing Mendelian disorders with an intellectual disability phenotype. Competitive pathway analysis implicated the biological processes of neurogenesis and synaptic regulation, as well as the gene targets of two pharmacologic agents: cinnarizine, a T-type calcium channel blocker, and LY97241, a potassium channel inhibitor. Transcriptome-wide and epigenome-wide analysis revealed that the implicated loci were enriched for genes expressed across all brain regions (most strongly in the cerebellum). Enrichment was exclusive to genes expressed in neurons but not oligodendrocytes or astrocytes. Finally, we report genetic correlations between cognitive ability and disparate phenotypes including psychiatric disorders, several autoimmune disorders, longevity, and maternal age at first birth.

Published
2017

45. GWAS meta-analysis reveals novel loci and genetic correlates for general cognitive function: a report from the COGENT consortium.

Author

Trampush, JW, Yang, MLZ, Yu, J, Knowles, E, Davies, G, Liewald, DC, Starr, JM, Djurovic, S, Melle, I, Sundet, K, Christoforou, A, Reinvang, I, DeRosse, P, Lundervold, AJ, Steen, VM, Espeseth, T, Räikkönen, K, Widen, E, Palotie, A, Eriksson, JG, Giegling, I, Konte, B, Roussos, P, Giakoumaki, S, Burdick, KE, Payton, A, Ollier, W, Horan, M, Chiba-Falek, O, Attix, DK, Need, AC, Cirulli, ET, Voineskos, AN, Stefanis, NC, Avramopoulos, D, Hatzimanolis, A, Arking, DE, Smyrnis, N, Bilder, RM, Freimer, NA, Cannon, TD, London, E, Poldrack, RA, Sabb, FW, Congdon, E, Conley, ED, Scult, MA, Dickinson, D, Straub, RE, Donohoe, G, Morris, D, Corvin, A, Gill, M, Hariri, AR, Weinberger, DR, Pendleton, N, Bitsios, P, Rujescu, D, Lahti, J, Le Hellard, S, Keller, MC, Andreassen, OA, Deary, IJ, Glahn, DC, Malhotra, AK, and Lencz, T

Subjects
Psychiatry, Medical and Health Sciences, Biological Sciences, Psychology and Cognitive Sciences
Abstract

This corrects the article DOI: 10.1038/mp.2016.244.

Published
2017

46. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex.

Author

Guadalupe, Tulio, Mathias, Samuel R, vanErp, Theo GM, Whelan, Christopher D, Zwiers, Marcel P, Abe, Yoshinari, Abramovic, Lucija, Agartz, Ingrid, Andreassen, Ole A, Arias-Vásquez, Alejandro, Aribisala, Benjamin S, Armstrong, Nicola J, Arolt, Volker, Artiges, Eric, Ayesa-Arriola, Rosa, Baboyan, Vatche G, Banaschewski, Tobias, Barker, Gareth, Bastin, Mark E, Baune, Bernhard T, Blangero, John, Bokde, Arun LW, Boedhoe, Premika SW, Bose, Anushree, Brem, Silvia, Brodaty, Henry, Bromberg, Uli, Brooks, Samantha, Büchel, Christian, Buitelaar, Jan, Calhoun, Vince D, Cannon, Dara M, Cattrell, Anna, Cheng, Yuqi, Conrod, Patricia J, Conzelmann, Annette, Corvin, Aiden, Crespo-Facorro, Benedicto, Crivello, Fabrice, Dannlowski, Udo, de Zubicaray, Greig I, de Zwarte, Sonja MC, Deary, Ian J, Desrivières, Sylvane, Doan, Nhat Trung, Donohoe, Gary, Dørum, Erlend S, Ehrlich, Stefan, Espeseth, Thomas, Fernández, Guillén, Flor, Herta, Fouche, Jean-Paul, Frouin, Vincent, Fukunaga, Masaki, Gallinat, Jürgen, Garavan, Hugh, Gill, Michael, Suarez, Andrea Gonzalez, Gowland, Penny, Grabe, Hans J, Grotegerd, Dominik, Gruber, Oliver, Hagenaars, Saskia, Hashimoto, Ryota, Hauser, Tobias U, Heinz, Andreas, Hibar, Derrek P, Hoekstra, Pieter J, Hoogman, Martine, Howells, Fleur M, Hu, Hao, Hulshoff Pol, Hilleke E, Huyser, Chaim, Ittermann, Bernd, Jahanshad, Neda, Jönsson, Erik G, Jurk, Sarah, Kahn, Rene S, Kelly, Sinead, Kraemer, Bernd, Kugel, Harald, Kwon, Jun Soo, Lemaitre, Herve, Lesch, Klaus-Peter, Lochner, Christine, Luciano, Michelle, Marquand, Andre F, Martin, Nicholas G, Martínez-Zalacaín, Ignacio, Martinot, Jean-Luc, Mataix-Cols, David, Mather, Karen, McDonald, Colm, McMahon, Katie L, Medland, Sarah E, Menchón, José M, Morris, Derek W, Mothersill, Omar, Maniega, Susana Munoz, and Mwangi, Benson

Subjects
Brain, Humans, Magnetic Resonance Imaging, Organ Size, Aging, Sex Characteristics, Quantitative Trait, Heritable, Adolescent, Adult, Aged, Middle Aged, Female, Male, Functional Laterality, Meta-Analysis as Topic, Young Adult, Age, Enigma, Handedness, Heritability, Meta-analysis, Sex, Subcortical brain asymmetry, Quantitative Trait, Heritable, Medical and Health Sciences, Psychology and Cognitive Sciences, Experimental Psychology
Abstract

The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.

Published
2017

47. Significant Locus and Metabolic Genetic Correlations Revealed in Genome-Wide Association Study of Anorexia Nervosa

Author

Duncan, Laramie, Yilmaz, Zeynep, Gaspar, Helena, Walters, Raymond, Goldstein, Jackie, Anttila, Verneri, Bulik-Sullivan, Brendan, Ripke, Stephan, Thornton, Laura, Hinney, Anke, Daly, Mark, Sullivan, Patrick F, Zeggini, Eleftheria, Breen, Gerome, Bulik, Cynthia M, Gaspar, Héléna, Adan, Roger, Alfredsson, Lars, Ando, Tetsuya, Andreassen, Ole, Aschauer, Harald, Baker, Jessica, Barrett, Jeffrey, Bencko, Vladimir, Bergen, Andrew, Berrettini, Wade, Birgegård, Andreas, Boni, Claudette, Perica, Vesna Boraska, Brandt, Harry, Burghardt, Roland, Carlberg, Laura, Cassina, Matteo, Cesta, Carolyn, Cichon, Sven, Clementi, Maurizio, Cohen-Woods, Sarah, Coleman, Joni, Cone, Roger, Courtet, Philippe, Crawford, Steven, Crow, Scott, Crowley, Jim, Danner, Unna, Davis, Oliver, de Zwaan, Martina, Dedoussis, George, Degortes, Daniela, DeSocio, Janiece, Dick, Danielle, Dikeos, Dimitris, Dina, Christian, Ding, Bo, Dmitrzak-Weglarz, Monika, Docampo, Elisa, Egberts, Karin, Ehrlich, Stefan, Escaramís, Geòrgia, Esko, Tõnu, Espeseth, Thomas, Estivill, Xavier, Favaro, Angela, Fernández-Aranda, Fernando, Fichter, Manfred, Finan, Chris, Fischer, Krista, Floyd, James, Föcker, Manuel, Foretova, Lenka, Forzan, Monica, Fox, Caroline, Franklin, Christopher, Gaborieau, Valerie, Gallinger, Steven, Gambaro, Giovanni, Giegling, Ina, Gonidakis, Fragiskos, Gorwood, Philip, Gratacos, Monica, Guillaume, Sébastien, Guo, Yiran, Hakonarson, Hakon, Halmi, Katherine, Harrison, Rebecca, Hatzikotoulas, Konstantinos, Hauser, Joanna, Hebebrand, Johannes, Helder, Sietske, Hendriks, Judith, Herms, Stefan, Herpertz-Dahlmann, Beate, Herzog, Wolfgang, and Hilliard, Christopher

Subjects
Serious Mental Illness, Mental Health, Anorexia, Eating Disorders, Brain Disorders, Nutrition, Human Genome, Genetics, Prevention, Pediatric, Mental health, Anorexia Nervosa, Case-Control Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Phenotype, Polymorphism, Single Nucleotide, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Diabetes, GWAS, Metabolism, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

ObjectiveThe authors conducted a genome-wide association study of anorexia nervosa and calculated genetic correlations with a series of psychiatric, educational, and metabolic phenotypes.MethodFollowing uniform quality control and imputation procedures using the 1000 Genomes Project (phase 3) in 12 case-control cohorts comprising 3,495 anorexia nervosa cases and 10,982 controls, the authors performed standard association analysis followed by a meta-analysis across cohorts. Linkage disequilibrium score regression was used to calculate genome-wide common variant heritability (single-nucleotide polymorphism [SNP]-based heritability [h2SNP]), partitioned heritability, and genetic correlations (rg) between anorexia nervosa and 159 other phenotypes.ResultsResults were obtained for 10,641,224 SNPs and insertion-deletion variants with minor allele frequencies >1% and imputation quality scores >0.6. The h2SNP of anorexia nervosa was 0.20 (SE=0.02), suggesting that a substantial fraction of the twin-based heritability arises from common genetic variation. The authors identified one genome-wide significant locus on chromosome 12 (rs4622308) in a region harboring a previously reported type 1 diabetes and autoimmune disorder locus. Significant positive genetic correlations were observed between anorexia nervosa and schizophrenia, neuroticism, educational attainment, and high-density lipoprotein cholesterol, and significant negative genetic correlations were observed between anorexia nervosa and body mass index, insulin, glucose, and lipid phenotypes.ConclusionsAnorexia nervosa is a complex heritable phenotype for which this study has uncovered the first genome-wide significant locus. Anorexia nervosa also has large and significant genetic correlations with both psychiatric phenotypes and metabolic traits. The study results encourage a reconceptualization of this frequently lethal disorder as one with both psychiatric and metabolic etiology.

Published
2017

48. Identification of Gene Loci That Overlap Between Schizophrenia and Educational Attainment.

Author

Le Hellard, Stéphanie, Wang, Yunpeng, Witoelar, Aree, Zuber, Verena, Bettella, Francesco, Hugdahl, Kenneth, Espeseth, Thomas, Steen, Vidar, Melle, Ingrid, Schork, Andrew, Djurovic, Srdjan, Andreassen, Ole, Dale, Anders, Thompson, Wesley, and Desikan, Rahul

Subjects
GWAS, conditional FDR, pleiotropy, Adult, Aptitude, Educational Status, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Schizophrenia
Abstract

There is evidence for genetic overlap between cognitive abilities and schizophrenia (SCZ), and genome-wide association studies (GWAS) demonstrate that both SCZ and general cognitive abilities have a strong polygenic component with many single-nucleotide polymorphisms (SNPs) each with a small effect. Here we investigated the shared genetic architecture between SCZ and educational attainment, which is regarded as a proxy phenotype for cognitive abilities, but may also reflect other traits. We applied a conditional false discovery rate (condFDR) method to GWAS of SCZ (n = 82 315), college completion (College, n = 95 427), and years of education (EduYears, n = 101 069). Variants associated with College or EduYears showed enrichment of association with SCZ, demonstrating polygenic overlap. This was confirmed by an increased replication rate in SCZ. By applying a condFDR threshold

Published
2017

49. Genetic control of variability in subcortical and intracranial volumes

Author

Córdova-Palomera, Aldo, van der Meer, Dennis, Kaufmann, Tobias, Bettella, Francesco, Wang, Yunpeng, Alnæs, Dag, Doan, Nhat Trung, Agartz, Ingrid, Bertolino, Alessandro, Buitelaar, Jan K., Coynel, David, Djurovic, Srdjan, Dørum, Erlend S., Espeseth, Thomas, Fazio, Leonardo, Franke, Barbara, Frei, Oleksandr, Håberg, Asta, Le Hellard, Stephanie, Jönsson, Erik G., Kolskår, Knut K., Lund, Martina J., Moberget, Torgeir, Nordvik, Jan E., Nyberg, Lars, Papassotiropoulos, Andreas, Pergola, Giulio, de Quervain, Dominique, Rampino, Antonio, Richard, Genevieve, Rokicki, Jaroslav, Sanders, Anne-Marthe, Schwarz, Emanuel, Smeland, Olav B., Steen, Vidar M., Starrfelt, Jostein, Sønderby, Ida E., Ulrichsen, Kristine M., Andreassen, Ole A., and Westlye, Lars T.

Published
2021
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