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4. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

5. Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

Author

Sterenborg, Rosalie B. T. M., Steinbrenner, Inga, Li, Yong, Bujnis, Melissa N., Naito, Tatsuhiko, Marouli, Eirini, Galesloot, Tessel E., Babajide, Oladapo, Andreasen, Laura, Astrup, Arne, Åsvold, Bjørn Olav, Bandinelli, Stefania, Beekman, Marian, Beilby, John P., Bork-Jensen, Jette, Boutin, Thibaud, Brody, Jennifer A., Brown, Suzanne J., Brumpton, Ben, Campbell, Purdey J., Cappola, Anne R., Ceresini, Graziano, Chaker, Layal, Chasman, Daniel I., Concas, Maria Pina, Coutinho de Almeida, Rodrigo, Cross, Simone M., Cucca, Francesco, Deary, Ian J., Kjaergaard, Alisa Devedzic, Echouffo Tcheugui, Justin B., Ellervik, Christina, Eriksson, Johan G., Ferrucci, Luigi, Freudenberg, Jan, Fuchsberger, Christian, Gieger, Christian, Giulianini, Franco, Gögele, Martin, Graham, Sarah E., Grarup, Niels, Gunjača, Ivana, Hansen, Torben, Harding, Barbara N., Harris, Sarah E., Haunsø, Stig, Hayward, Caroline, Hui, Jennie, Ittermann, Till, Jukema, J. Wouter, Kajantie, Eero, Kanters, Jørgen K., Kårhus, Line L., Kiemeney, Lambertus A. L. M., Kloppenburg, Margreet, Kühnel, Brigitte, Lahti, Jari, Langenberg, Claudia, Lapauw, Bruno, Leese, Graham, Li, Shuo, Liewald, David C. M., Linneberg, Allan, Lominchar, Jesus V. T., Luan, Jian’an, Martin, Nicholas G., Matana, Antonela, Meima, Marcel E., Meitinger, Thomas, Meulenbelt, Ingrid, Mitchell, Braxton D., Møllehave, Line T., Mora, Samia, Naitza, Silvia, Nauck, Matthias, Netea-Maier, Romana T., Noordam, Raymond, Nursyifa, Casia, Okada, Yukinori, Onano, Stefano, Papadopoulou, Areti, Palmer, Colin N. A., Pattaro, Cristian, Pedersen, Oluf, Peters, Annette, Pietzner, Maik, Polašek, Ozren, Pramstaller, Peter P., Psaty, Bruce M., Punda, Ante, Ray, Debashree, Redmond, Paul, Richards, J. Brent, Ridker, Paul M., Russ, Tom C., Ryan, Kathleen A., Olesen, Morten Salling, Schultheiss, Ulla T., Selvin, Elizabeth, Siddiqui, Moneeza K., Sidore, Carlo, Slagboom, P. Eline, Sørensen, Thorkild I. A., Soto-Pedre, Enrique, Spector, Tim D., Spedicati, Beatrice, Srinivasan, Sundararajan, Starr, John M., Stott, David J., Tanaka, Toshiko, Torlak, Vesela, Trompet, Stella, Tuhkanen, Johanna, Uitterlinden, André G., van den Akker, Erik B., van den Eynde, Tibbert, van der Klauw, Melanie M., van Heemst, Diana, Verroken, Charlotte, Visser, W. Edward, Vojinovic, Dina, Völzke, Henry, Waldenberger, Melanie, Walsh, John P., Wareham, Nicholas J., Weiss, Stefan, Willer, Cristen J., Wilson, Scott G., Wolffenbuttel, Bruce H. R., Wouters, Hanneke J. C. M., Wright, Margaret J., Yang, Qiong, Zemunik, Tatijana, Zhou, Wei, Zhu, Gu, Zöllner, Sebastian, Smit, Johannes W. A., Peeters, Robin P., Köttgen, Anna, Teumer, Alexander, and Medici, Marco

Published
2024
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6. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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9. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

10. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Author

Howe, Laurence J, Nivard, Michel G, Morris, Tim T, Hansen, Ailin F, Rasheed, Humaira, Cho, Yoonsu, Chittoor, Geetha, Ahlskog, Rafael, Lind, Penelope A, Palviainen, Teemu, van der Zee, Matthijs D, Cheesman, Rosa, Mangino, Massimo, Wang, Yunzhang, Li, Shuai, Klaric, Lucija, Ratliff, Scott M, Bielak, Lawrence F, Nygaard, Marianne, Giannelis, Alexandros, Willoughby, Emily A, Reynolds, Chandra A, Balbona, Jared V, Andreassen, Ole A, Ask, Helga, Baras, Aris, Bauer, Christopher R, Boomsma, Dorret I, Campbell, Archie, Campbell, Harry, Chen, Zhengming, Christofidou, Paraskevi, Corfield, Elizabeth, Dahm, Christina C, Dokuru, Deepika R, Evans, Luke M, de Geus, Eco JC, Giddaluru, Sudheer, Gordon, Scott D, Harden, K Paige, Hill, W David, Hughes, Amanda, Kerr, Shona M, Kim, Yongkang, Kweon, Hyeokmoon, Latvala, Antti, Lawlor, Deborah A, Li, Liming, Lin, Kuang, Magnus, Per, Magnusson, Patrik KE, Mallard, Travis T, Martikainen, Pekka, Mills, Melinda C, Njølstad, Pål Rasmus, Overton, John D, Pedersen, Nancy L, Porteous, David J, Reid, Jeffrey, Silventoinen, Karri, Southey, Melissa C, Stoltenberg, Camilla, Tucker-Drob, Elliot M, Wright, Margaret J, Hewitt, John K, Keller, Matthew C, Stallings, Michael C, Lee, James J, Christensen, Kaare, Kardia, Sharon LR, Peyser, Patricia A, Smith, Jennifer A, Wilson, James F, Hopper, John L, Hägg, Sara, Spector, Tim D, Pingault, Jean-Baptiste, Plomin, Robert, Havdahl, Alexandra, Bartels, Meike, Martin, Nicholas G, Oskarsson, Sven, Justice, Anne E, Millwood, Iona Y, Hveem, Kristian, Naess, Øyvind, Willer, Cristen J, Åsvold, Bjørn Olav, Koellinger, Philipp D, Kaprio, Jaakko, Medland, Sarah E, Walters, Robin G, Benjamin, Daniel J, Turley, Patrick, Evans, David M, Davey Smith, George, Hayward, Caroline, Brumpton, Ben, Hemani, Gibran, and Davies, Neil M

Subjects
Human Genome, Genetics, Pediatric, 2.1 Biological and endogenous factors, Aetiology, Mental health, Generic health relevance, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Social Science Genetic Association Consortium, Within Family Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Published
2022

12. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

Author

Agartz, Ingrid, Asherson, Philip, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Baumeister, Sarah, Bertolino, Alessandro, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Buitelaar, Jan K, Cannon, Dara M, Cervenka, Simon, Conrod, Patricia J, Crespo-Facorro, Benedicto, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Frodl, Thomas, Gruner, Patricia, Gur, Raquel E, Gur, Ruben C, Harrison, Ben J, Hatton, Sean N, Hickie, Ian, Howells, Fleur M, Huyser, Chaim, Jernigan, Terry L, Jiang, Jiyang, Joska, John A, Kahn, René S, Kalnin, Andrew J, Kochan, Nicole A, Koops, Sanne, Kuntsi, Jonna, Lagopoulos, Jim, Lazaro, Luisa, Lebedeva, Irina S, Lochner, Christine, Martin, Nicholas G, Mazoyer, Bernard, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Medland, Sarah, Modabbernia, Amirhossein, Mwangi, Benson, Nakao, Tomohiro, Nyberg, Lars, Piras, Fabrizio, Portella, Maria J, Qiu, Jiang, Roffman, Joshua L, Sachdev, Perminder S, Sanford, Nicole, Satterthwaite, Theodore D, Saykin, Andrew J, Sellgren, Carl M, Sim, Kang, Smoller, Jordan W, Soares, Jair C, Sommer, Iris E, Spalletta, Gianfranco, Stein, Dan J, Thomopoulos, Sophia I, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, Trollor, Julian N, van 't Ent, Dennis, van den Heuvel, Odile A, van Erp, Theo GM, van Haren, Neeltje EM, Vecchio, Daniela, Veltman, Dick J, Wang, Yang, Weber, Bernd, Wei, Dongtao, Wen, Wei, Westlye, Lars T, Williams, Steven CR, Wright, Margaret J, Wu, Mon-Ju, Yu, Kevin, Ge, Ruiyang, Yu, Yuetong, Qi, Yi Xuan, Fan, Yu-nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila S, New, Faye, Boomsma, Dorret I, Brodaty, Henry, Brouwer, Rachel M, Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline A, Erk, Susanne, Fisher, Simon E, Franke, Barbara, Glahn, David C, Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke E, Schumann, Gunter, Tamnes, Christian K, Walter, Henrik, Wierenga, Lara M, Jahanshad, Neda, Thompson, Paul M, and Frangou, Sophia

Published
2024
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13. Interactions between the lipidome and genetic and environmental factors in autism

Author

Yap, Chloe X., Henders, Anjali K., Alvares, Gail A., Giles, Corey, Huynh, Kevin, Nguyen, Anh, Wallace, Leanne, McLaren, Tiana, Yang, Yuanhao, Hernandez, Leanna M., Gandal, Michael J., Hansell, Narelle K., Cleary, Dominique, Grove, Rachel, Hafekost, Claire, Harun, Alexis, Holdsworth, Helen, Jellett, Rachel, Khan, Feroza, Lawson, Lauren P., Leslie, Jodie, Levis Frenk, Mira, Masi, Anne, Mathew, Nisha E., Muniandy, Melanie, Nothard, Michaela, Miller, Jessica L., Nunn, Lorelle, Strike, Lachlan T., Cadby, Gemma, Moses, Eric K., de Zubicaray, Greig I., Thompson, Paul M., McMahon, Katie L., Wright, Margaret J., Visscher, Peter M., Dawson, Paul A., Dissanayake, Cheryl, Eapen, Valsamma, Heussler, Helen S., Whitehouse, Andrew J. O., Meikle, Peter J., Wray, Naomi R., and Gratten, Jacob

Published
2023
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14. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease

Author

Duperron, Marie-Gabrielle, Knol, Maria J., Le Grand, Quentin, Evans, Tavia E., Mishra, Aniket, Tsuchida, Ami, Roshchupkin, Gennady, Konuma, Takahiro, Trégouët, David-Alexandre, Romero, Jose Rafael, Frenzel, Stefan, Luciano, Michelle, Hofer, Edith, Bourgey, Mathieu, Dueker, Nicole D., Delgado, Pilar, Hilal, Saima, Tankard, Rick M., Dubost, Florian, Shin, Jean, Saba, Yasaman, Armstrong, Nicola J., Bordes, Constance, Bastin, Mark E., Beiser, Alexa, Brodaty, Henry, Bülow, Robin, Carrera, Caty, Chen, Christopher, Cheng, Ching-Yu, Deary, Ian J., Gampawar, Piyush G., Himali, Jayandra J., Jiang, Jiyang, Kawaguchi, Takahisa, Li, Shuo, Macalli, Melissa, Marquis, Pascale, Morris, Zoe, Muñoz Maniega, Susana, Miyamoto, Susumu, Okawa, Masakazu, Paradise, Matthew, Parva, Pedram, Rundek, Tatjana, Sargurupremraj, Muralidharan, Schilling, Sabrina, Setoh, Kazuya, Soukarieh, Omar, Tabara, Yasuharu, Teumer, Alexander, Thalamuthu, Anbupalam, Trollor, Julian N., Valdés Hernández, Maria C., Vernooij, Meike W., Völker, Uwe, Wittfeld, Katharina, Wong, Tien Yin, Wright, Margaret J., Zhang, Junyi, Zhao, Wanting, Zhu, Yi-Cheng, Schmidt, Helena, Sachdev, Perminder S., Wen, Wei, Yoshida, Kazumichi, Joutel, Anne, Satizabal, Claudia L., Sacco, Ralph L., Bourque, Guillaume, Lathrop, Mark, Paus, Tomas, Fernandez-Cadenas, Israel, Yang, Qiong, Mazoyer, Bernard, Boutinaud, Philippe, Okada, Yukinori, Grabe, Hans J., Mather, Karen A., Schmidt, Reinhold, Joliot, Marc, Ikram, M. Arfan, Matsuda, Fumihiko, Tzourio, Christophe, Wardlaw, Joanna M., Seshadri, Sudha, Adams, Hieab H. H., and Debette, Stéphanie

Published
2023
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17. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author

Han, Laura KM, Dinga, Richard, Hahn, Tim, Ching, Christopher RK, Eyler, Lisa T, Aftanas, Lyubomir, Aghajani, Moji, Aleman, André, Baune, Bernhard T, Berger, Klaus, Brak, Ivan, Filho, Geraldo Busatto, Carballedo, Angela, Connolly, Colm G, Couvy-Duchesne, Baptiste, Cullen, Kathryn R, Dannlowski, Udo, Davey, Christopher G, Dima, Danai, Duran, Fabio LS, Enneking, Verena, Filimonova, Elena, Frenzel, Stefan, Frodl, Thomas, Fu, Cynthia HY, Godlewska, Beata R, Gotlib, Ian H, Grabe, Hans J, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Hall, Geoffrey B, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Hosten, Norbert, Jansen, Andreas, Kähler, Claas, Kircher, Tilo, Klimes-Dougan, Bonnie, Krämer, Bernd, Krug, Axel, Lagopoulos, Jim, Leenings, Ramona, MacMaster, Frank P, MacQueen, Glenda, McIntosh, Andrew, McLellan, Quinn, McMahon, Katie L, Medland, Sarah E, Mueller, Bryon A, Mwangi, Benson, Osipov, Evgeny, Portella, Maria J, Pozzi, Elena, Reneman, Liesbeth, Repple, Jonathan, Rosa, Pedro GP, Sacchet, Matthew D, Sämann, Philipp G, Schnell, Knut, Schrantee, Anouk, Simulionyte, Egle, Soares, Jair C, Sommer, Jens, Stein, Dan J, Steinsträter, Olaf, Strike, Lachlan T, Thomopoulos, Sophia I, van Tol, Marie-José, Veer, Ilya M, Vermeiren, Robert RJM, Walter, Henrik, van der Wee, Nic JA, van der Werff, Steven JA, Whalley, Heather, Winter, Nils R, Wittfeld, Katharina, Wright, Margaret J, Wu, Mon-Ju, Völzke, Henry, Yang, Tony T, Zannias, Vasileios, de Zubicaray, Greig I, Zunta-Soares, Giovana B, Abé, Christoph, Alda, Martin, Andreassen, Ole A, Bøen, Erlend, Bonnin, Caterina M, Canales-Rodriguez, Erick J, Cannon, Dara, Caseras, Xavier, Chaim-Avancini, Tiffany M, Elvsåshagen, Torbjørn, Favre, Pauline, Foley, Sonya F, and Fullerton, Janice M

Subjects
Depression, Aging, Brain Disorders, Biomedical Imaging, Serious Mental Illness, Mental Health, Major Depressive Disorder, Behavioral and Social Science, Clinical Research, Neurosciences, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Adolescent, Adult, Aged, Brain, Depressive Disorder, Major, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Published
2021

18. Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

Author

Jia, Tianye, Chu, Congying, Liu, Yun, van Dongen, Jenny, Papastergios, Evangelos, Armstrong, Nicola J, Bastin, Mark E, Carrillo-Roa, Tania, den Braber, Anouk, Harris, Mathew, Jansen, Rick, Liu, Jingyu, Luciano, Michelle, Ori, Anil PS, Roiz Santiañez, Roberto, Ruggeri, Barbara, Sarkisyan, Daniil, Shin, Jean, Sungeun, Kim, Tordesillas Gutiérrez, Diana, van’t Ent, Dennis, Ames, David, Artiges, Eric, Bakalkin, Georgy, Banaschewski, Tobias, Bokde, Arun LW, Brodaty, Henry, Bromberg, Uli, Brouwer, Rachel, Büchel, Christian, Burke Quinlan, Erin, Cahn, Wiepke, de Zubicaray, Greig I, Ehrlich, Stefan, Ekström, Tomas J, Flor, Herta, Fröhner, Juliane H, Frouin, Vincent, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Hoare, Jacqueline, Ittermann, Bernd, Jahanshad, Neda, Jiang, Jiyang, Kwok, John B, Martin, Nicholas G, Martinot, Jean-Luc, Mather, Karen A, McMahon, Katie L, McRae, Allan F, Nees, Frauke, Papadopoulos Orfanos, Dimitri, Paus, Tomáš, Poustka, Luise, Sämann, Philipp G, Schofield, Peter R, Smolka, Michael N, Stein, Dan J, Strike, Lachlan T, Teeuw, Jalmar, Thalamuthu, Anbupalam, Trollor, Julian, Walter, Henrik, Wardlaw, Joanna M, Wen, Wei, Whelan, Robert, Apostolova, Liana G, Binder, Elisabeth B, Boomsma, Dorret I, Calhoun, Vince, Crespo-Facorro, Benedicto, Deary, Ian J, Hulshoff Pol, Hilleke, Ophoff, Roel A, Pausova, Zdenka, Sachdev, Perminder S, Saykin, Andrew, Wright, Margaret J, Thompson, Paul M, Schumann, Gunter, and Desrivières, Sylvane

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Mental Health, Neurosciences, Genetics, Human Genome, Diabetes, Neurodegenerative, 2.1 Biological and endogenous factors, Aetiology, Neurological, Mental health, CpG Islands, DNA Methylation, Epigenesis, Genetic, Epigenome, Genome-Wide Association Study, Humans, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)-three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

Published
2021

19. High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

Author

Revelas, Mary, Thalamuthu, Anbupalam, Zettergren, Anna, Oldmeadow, Christopher, Najar, Jenna, Seidu, Nazib M., Armstrong, Nicola J., Riveros, Carlos, Kwok, John B., Schofield, Peter R., Trollor, Julian N., Waern, Margda, Wright, Margaret J., Zetterberg, Henrik, Ames, David, Belnnow, Kaj, Brodaty, Henry, Scott, Rodney J., Skoog, Ingmar, Attia, John R., Sachdev, Perminder S., and Mather, Karen A.

Published
2023
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20. Cerebral small vessel disease genomics and its implications across the lifespan.

Author

Sargurupremraj, Muralidharan, Suzuki, Hideaki, Jian, Xueqiu, Sarnowski, Chloé, Evans, Tavia E, Bis, Joshua C, Eiriksdottir, Gudny, Sakaue, Saori, Terzikhan, Natalie, Habes, Mohamad, Zhao, Wei, Armstrong, Nicola J, Hofer, Edith, Yanek, Lisa R, Hagenaars, Saskia P, Kumar, Rajan B, van den Akker, Erik B, McWhirter, Rebekah E, Trompet, Stella, Mishra, Aniket, Saba, Yasaman, Satizabal, Claudia L, Beaudet, Gregory, Petit, Laurent, Tsuchida, Ami, Zago, Laure, Schilling, Sabrina, Sigurdsson, Sigurdur, Gottesman, Rebecca F, Lewis, Cora E, Aggarwal, Neelum T, Lopez, Oscar L, Smith, Jennifer A, Valdés Hernández, Maria C, van der Grond, Jeroen, Wright, Margaret J, Knol, Maria J, Dörr, Marcus, Thomson, Russell J, Bordes, Constance, Le Grand, Quentin, Duperron, Marie-Gabrielle, Smith, Albert V, Knopman, David S, Schreiner, Pamela J, Evans, Denis A, Rotter, Jerome I, Beiser, Alexa S, Maniega, Susana Muñoz, Beekman, Marian, Trollor, Julian, Stott, David J, Vernooij, Meike W, Wittfeld, Katharina, Niessen, Wiro J, Soumaré, Aicha, Boerwinkle, Eric, Sidney, Stephen, Turner, Stephen T, Davies, Gail, Thalamuthu, Anbupalam, Völker, Uwe, van Buchem, Mark A, Bryan, R Nick, Dupuis, Josée, Bastin, Mark E, Ames, David, Teumer, Alexander, Amouyel, Philippe, Kwok, John B, Bülow, Robin, Deary, Ian J, Schofield, Peter R, Brodaty, Henry, Jiang, Jiyang, Tabara, Yasuharu, Setoh, Kazuya, Miyamoto, Susumu, Yoshida, Kazumichi, Nagata, Manabu, Kamatani, Yoichiro, Matsuda, Fumihiko, Psaty, Bruce M, Bennett, David A, De Jager, Philip L, Mosley, Thomas H, Sachdev, Perminder S, Schmidt, Reinhold, Warren, Helen R, Evangelou, Evangelos, Trégouët, David-Alexandre, International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium (IHGC), Ikram, Mohammad A, Wen, Wei, DeCarli, Charles, Srikanth, Velandai K, Jukema, J Wouter, Slagboom, Eline P, and Kardia, Sharon LR

Subjects
International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium, Humans, Alzheimer Disease, Hypertension, Medical History Taking, Risk Assessment, Risk Factors, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Stroke, Genome-Wide Association Study, Young Adult, Diffusion Tensor Imaging, Genetic Loci, Mendelian Randomization Analysis, Cerebral Small Vessel Diseases, White Matter, and over
Abstract

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Published
2020

21. A large-scale genome-wide association study meta-analysis of cannabis use disorder.

Author

Johnson, Emma C, Demontis, Ditte, Thorgeirsson, Thorgeir E, Walters, Raymond K, Polimanti, Renato, Hatoum, Alexander S, Sanchez-Roige, Sandra, Paul, Sarah E, Wendt, Frank R, Clarke, Toni-Kim, Lai, Dongbing, Reginsson, Gunnar W, Zhou, Hang, He, June, Baranger, David AA, Gudbjartsson, Daniel F, Wedow, Robbee, Adkins, Daniel E, Adkins, Amy E, Alexander, Jeffry, Bacanu, Silviu-Alin, Bigdeli, Tim B, Boden, Joseph, Brown, Sandra A, Bucholz, Kathleen K, Bybjerg-Grauholm, Jonas, Corley, Robin P, Degenhardt, Louisa, Dick, Danielle M, Domingue, Benjamin W, Fox, Louis, Goate, Alison M, Gordon, Scott D, Hack, Laura M, Hancock, Dana B, Hartz, Sarah M, Hickie, Ian B, Hougaard, David M, Krauter, Kenneth, Lind, Penelope A, McClintick, Jeanette N, McQueen, Matthew B, Meyers, Jacquelyn L, Montgomery, Grant W, Mors, Ole, Mortensen, Preben B, Nordentoft, Merete, Pearson, John F, Peterson, Roseann E, Reynolds, Maureen D, Rice, John P, Runarsdottir, Valgerdur, Saccone, Nancy L, Sherva, Richard, Silberg, Judy L, Tarter, Ralph E, Tyrfingsson, Thorarinn, Wall, Tamara L, Webb, Bradley T, Werge, Thomas, Wetherill, Leah, Wright, Margaret J, Zellers, Stephanie, Adams, Mark J, Bierut, Laura J, Boardman, Jason D, Copeland, William E, Farrer, Lindsay A, Foroud, Tatiana M, Gillespie, Nathan A, Grucza, Richard A, Harris, Kathleen Mullan, Heath, Andrew C, Hesselbrock, Victor, Hewitt, John K, Hopfer, Christian J, Horwood, John, Iacono, William G, Johnson, Eric O, Kendler, Kenneth S, Kennedy, Martin A, Kranzler, Henry R, Madden, Pamela AF, Maes, Hermine H, Maher, Brion S, Martin, Nicholas G, McGue, Matthew, McIntosh, Andrew M, Medland, Sarah E, Nelson, Elliot C, Porjesz, Bernice, Riley, Brien P, Stallings, Michael C, Vanyukov, Michael M, Vrieze, Scott, Psychiatric Genomics Consortium Substance Use Disorders Workgroup, Davis, Lea K, Bogdan, Ryan, Gelernter, Joel, and Edenberg, Howard J

Subjects
Psychiatric Genomics Consortium Substance Use Disorders Workgroup, Humans, Marijuana Abuse, Risk, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

BackgroundVariation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder.MethodsTo conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations.FindingsWe identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia.InterpretationThese findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.FundingNational Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Published
2020

22. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults.

Author

Hofer, Edith, Roshchupkin, Gennady V, Adams, Hieab HH, Knol, Maria J, Lin, Honghuang, Li, Shuo, Zare, Habil, Ahmad, Shahzad, Armstrong, Nicola J, Satizabal, Claudia L, Bernard, Manon, Bis, Joshua C, Gillespie, Nathan A, Luciano, Michelle, Mishra, Aniket, Scholz, Markus, Teumer, Alexander, Xia, Rui, Jian, Xueqiu, Mosley, Thomas H, Saba, Yasaman, Pirpamer, Lukas, Seiler, Stephan, Becker, James T, Carmichael, Owen, Rotter, Jerome I, Psaty, Bruce M, Lopez, Oscar L, Amin, Najaf, van der Lee, Sven J, Yang, Qiong, Himali, Jayandra J, Maillard, Pauline, Beiser, Alexa S, DeCarli, Charles, Karama, Sherif, Lewis, Lindsay, Harris, Mat, Bastin, Mark E, Deary, Ian J, Veronica Witte, A, Beyer, Frauke, Loeffler, Markus, Mather, Karen A, Schofield, Peter R, Thalamuthu, Anbupalam, Kwok, John B, Wright, Margaret J, Ames, David, Trollor, Julian, Jiang, Jiyang, Brodaty, Henry, Wen, Wei, Vernooij, Meike W, Hofman, Albert, Uitterlinden, André G, Niessen, Wiro J, Wittfeld, Katharina, Bülow, Robin, Völker, Uwe, Pausova, Zdenka, Bruce Pike, G, Maingault, Sophie, Crivello, Fabrice, Tzourio, Christophe, Amouyel, Philippe, Mazoyer, Bernard, Neale, Michael C, Franz, Carol E, Lyons, Michael J, Panizzon, Matthew S, Andreassen, Ole A, Dale, Anders M, Logue, Mark, Grasby, Katrina L, Jahanshad, Neda, Painter, Jodie N, Colodro-Conde, Lucía, Bralten, Janita, Hibar, Derrek P, Lind, Penelope A, Pizzagalli, Fabrizio, Stein, Jason L, Thompson, Paul M, Medland, Sarah E, ENIGMA consortium, Sachdev, Perminder S, Kremen, William S, Wardlaw, Joanna M, Villringer, Arno, van Duijn, Cornelia M, Grabe, Hans J, Longstreth, William T, Fornage, Myriam, Paus, Tomas, Debette, Stephanie, Ikram, M Arfan, Schmidt, Helena, Schmidt, Reinhold, and Seshadri, Sudha

Subjects
ENIGMA consortium, Brain, Chromosome Structures, Humans, Neurodegenerative Diseases, Cognition, Mental Disorders, Genomics, Aging, Phenotype, Polymorphism, Single Nucleotide, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Genome-Wide Association Study, Genetics, Neurosciences, Human Genome, Brain Disorders, Biotechnology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Neurological
Abstract

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

Published
2020

23. Reducing Antibiotic Prescribing in Primary Care for Respiratory Illness.

Author

Kronman, Matthew P, Gerber, Jeffrey S, Grundmeier, Robert W, Zhou, Chuan, Robinson, Jeffrey D, Heritage, John, Stout, James, Burges, Dennis, Hedrick, Benjamin, Warren, Louise, Shalowitz, Madeleine, Shone, Laura P, Steffes, Jennifer, Wright, Margaret, Fiks, Alexander G, and Mangione-Smith, Rita

Subjects
Humans, Streptococcal Infections, Respiratory Tract Infections, Pharyngitis, Bronchitis, Sinusitis, Otitis Media, Acute Disease, Anti-Bacterial Agents, Confidence Intervals, Logistic Models, Odds Ratio, Communication, Pediatric Nursing, Education, Distance, Child, Child, Preschool, Infant, Outpatients, Program Development, Primary Health Care, Chicago, Female, Male, Intention to Treat Analysis, Inappropriate Prescribing, Quality Improvement, Pediatricians, Pediatrics, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

BackgroundOne-third of outpatient antibiotic prescriptions for pediatric acute respiratory tract infections (ARTIs) are inappropriate. We evaluated a distance learning program's effectiveness for reducing outpatient antibiotic prescribing for ARTI visits.MethodsIn this stepped-wedge clinical trial run from November 2015 to June 2018, we randomly assigned 19 pediatric practices belonging to the Pediatric Research in Office Settings Network or the NorthShore University HealthSystem to 4 wedges. Visits for acute otitis media, bronchitis, pharyngitis, sinusitis, and upper respiratory infection for children 6 months to

Published
2020

24. Reducing paediatric overweight and obesity through motivational interviewing: study protocol for a randomised controlled trial in the AAP PROS research network.

Author

Wright, Margaret, Delacroix, Emerson, Sonneville, Kendrin, Considine, Shannon, Proctor, Tim, Steffes, Jennifer, Harris, Donna, Shone, Laura, Woo, Heide, Vaughan, Roger, Grundmeier, Robert, Fiks, Alexander, Stockwell, Melissa, and Resnicow, Ken

Subjects
nutrition & dietetics, paediatrics, primary care, public health, Body Mass Index, Child, Child, Preschool, Clinical Protocols, Female, Humans, Male, Motivational Interviewing, Obesity, Overweight, Pediatrics, Primary Health Care
Abstract

INTRODUCTION: Primary care remains an underused venue for prevention and management of paediatric overweight and obesity. A prior trial demonstrated a significant impact of paediatrician/nurse practitioner (Ped/NP)-and registered dietitian (RD)-delivered motivational interviewing (MI) on child body mass index (BMI). The study described here will test the effectiveness of an enhanced version of this primary care-based MI counselling intervention on child BMI. METHODS AND ANALYSIS: This cluster randomised effectiveness trial includes 24 Ped/NPs from 18 paediatric primary care practices that belong to the American Academy of Pediatrics (AAP) national Pediatric Research in Office Settings (PROS) practice-based research network. To date, practices have been randomised (nine to intervention and nine to usual care). Intervention Ped/NPs have been trained in MI, behavioural therapy, billing/coding for weight management and study procedures. Usual care Ped/NPs received training in billing/coding and study procedures only. Children 3- 11 years old with BMI >the 85th percentile were identified via electronic health records (EHRs). Parents from intervention practices have been recruited and enrolled. Over about 2 years, these parents are offered approximately 10 MI-based counselling sessions (about four in person sessions with their childs Ped/NP and up to six telephonic sessions with a trained RD). The primary outcome is change in child BMI (defined as per cent from median BMI for age and sex) over the study period. The primary comparison is between eligible children in intervention practices whose parents enrol in the study and all eligible children in usual care practices. Data sources will include EHRs, billing records, surveys and counselling call notes. ETHICS AND DISSEMINATION: Institutional Review Board approval was obtained from the AAP. All Ped/NPs provided written informed consent, and intervention group parents provided consent and Health Insurance Portability and Accountability Act (HIPAA) authorisation. Findings will be disseminated through peer-reviewed publications, conference presentations and appropriate AAP channels. TRIAL REGISTRATION NUMBER: NCT03177148; Pre-results.

Published
2020

25. Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Author

Armstrong, Nicola J, Mather, Karen A, Sargurupremraj, Muralidharan, Knol, Maria J, Malik, Rainer, Satizabal, Claudia L, Yanek, Lisa R, Wen, Wei, Gudnason, Vilmundur G, Dueker, Nicole D, Elliott, Lloyd T, Hofer, Edith, Bis, Joshua, Jahanshad, Neda, Li, Shuo, Logue, Mark A, Luciano, Michelle, Scholz, Markus, Smith, Albert V, Trompet, Stella, Vojinovic, Dina, Xia, Rui, Alfaro-Almagro, Fidel, Ames, David, Amin, Najaf, Amouyel, Philippe, Beiser, Alexa S, Brodaty, Henry, Deary, Ian J, Fennema-Notestine, Christine, Gampawar, Piyush G, Gottesman, Rebecca, Griffanti, Ludovica, Jack, Clifford R, Jenkinson, Mark, Jiang, Jiyang, Kral, Brian G, Kwok, John B, Lampe, Leonie, C.M. Liewald, David, Maillard, Pauline, Marchini, Jonathan, Bastin, Mark E, Mazoyer, Bernard, Pirpamer, Lukas, Rafael Romero, José, Roshchupkin, Gennady V, Schofield, Peter R, Schroeter, Matthias L, Stott, David J, Thalamuthu, Anbupalam, Trollor, Julian, Tzourio, Christophe, van der Grond, Jeroen, Vernooij, Meike W, Witte, Veronica A, Wright, Margaret J, Yang, Qiong, Morris, Zoe, Siggurdsson, Siggi, Psaty, Bruce, Villringer, Arno, Schmidt, Helena, Haberg, Asta K, van Duijn, Cornelia M, Jukema, J Wouter, Dichgans, Martin, Sacco, Ralph L, Wright, Clinton B, Kremen, William S, Becker, Lewis C, Thompson, Paul M, Mosley, Thomas H, Wardlaw, Joanna M, Ikram, M Arfan, Adams, Hieab HH, Seshadri, Sudha, Sachdev, Perminder S, Smith, Stephen M, Launer, Lenore, Longstreth, William, DeCarli, Charles, Schmidt, Reinhold, Fornage, Myriam, Debette, Stephanie, and Nyquist, Paul A

Subjects
Epidemiology, Health Sciences, Brain Disorders, Biotechnology, Neurosciences, Cerebrovascular, Stroke, Neurodegenerative, Dementia, Vascular Cognitive Impairment/Dementia, Alzheimer's Disease Related Dementias (ADRD), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Human Genome, Aging, Genetics, Acquired Cognitive Impairment, 2.1 Biological and endogenous factors, Aged, Brain, Cerebral Small Vessel Diseases, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, White Matter, brain, genome-wide association study, neuroimaging, risk factors, white matter, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Neurology & Neurosurgery, Clinical sciences, Allied health and rehabilitation science
Abstract

Background and purposePeriventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings.MethodsParticipants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC.ResultsIn the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 (NBEAL), 10q23.1 (TSPAN14/FAM231A), and 10q24.33 (SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 (NOS3) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype.ConclusionsOur study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

Published
2020

26. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

Author

Thompson, Paul M, Jahanshad, Neda, Ching, Christopher RK, Salminen, Lauren E, Thomopoulos, Sophia I, Bright, Joanna, Baune, Bernhard T, Bertolín, Sara, Bralten, Janita, Bruin, Willem B, Bülow, Robin, Chen, Jian, Chye, Yann, Dannlowski, Udo, de Kovel, Carolien GF, Donohoe, Gary, Eyler, Lisa T, Faraone, Stephen V, Favre, Pauline, Filippi, Courtney A, Frodl, Thomas, Garijo, Daniel, Gil, Yolanda, Grabe, Hans J, Grasby, Katrina L, Hajek, Tomas, Han, Laura KM, Hatton, Sean N, Hilbert, Kevin, Ho, Tiffany C, Holleran, Laurena, Homuth, Georg, Hosten, Norbert, Houenou, Josselin, Ivanov, Iliyan, Jia, Tianye, Kelly, Sinead, Klein, Marieke, Kwon, Jun Soo, Laansma, Max A, Leerssen, Jeanne, Lueken, Ulrike, Nunes, Abraham, Neill, Joseph O', Opel, Nils, Piras, Fabrizio, Piras, Federica, Postema, Merel C, Pozzi, Elena, Shatokhina, Natalia, Soriano-Mas, Carles, Spalletta, Gianfranco, Sun, Daqiang, Teumer, Alexander, Tilot, Amanda K, Tozzi, Leonardo, van der Merwe, Celia, Van Someren, Eus JW, van Wingen, Guido A, Völzke, Henry, Walton, Esther, Wang, Lei, Winkler, Anderson M, Wittfeld, Katharina, Wright, Margaret J, Yun, Je-Yeon, Zhang, Guohao, Zhang-James, Yanli, Adhikari, Bhim M, Agartz, Ingrid, Aghajani, Moji, Aleman, André, Althoff, Robert R, Altmann, Andre, Andreassen, Ole A, Baron, David A, Bartnik-Olson, Brenda L, Marie Bas-Hoogendam, Janna, Baskin-Sommers, Arielle R, Bearden, Carrie E, Berner, Laura A, Boedhoe, Premika SW, Brouwer, Rachel M, Buitelaar, Jan K, Caeyenberghs, Karen, Cecil, Charlotte AM, Cohen, Ronald A, Cole, James H, Conrod, Patricia J, De Brito, Stephane A, de Zwarte, Sonja MC, Dennis, Emily L, Desrivieres, Sylvane, Dima, Danai, Ehrlich, Stefan, Esopenko, Carrie, Fairchild, Graeme, Fisher, Simon E, Fouche, Jean-Paul, and Francks, Clyde

Subjects
ENIGMA Consortium, Brain, Humans, Magnetic Resonance Imaging, Reproducibility of Results, Depressive Disorder, Major, Neuroimaging, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Behavioral and Social Science, Genetics, Basic Behavioral and Social Science, Prevention, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Mental health, Neurological, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Published
2020

28. Discovery of 42 genome-wide significant loci associated with dyslexia

Author

Doust, Catherine, Fontanillas, Pierre, Eising, Else, Gordon, Scott D., Wang, Zhengjun, Alagöz, Gökberk, Molz, Barbara, Pourcain, Beate St, Francks, Clyde, Marioni, Riccardo E., Zhao, Jingjing, Paracchini, Silvia, Talcott, Joel B., Monaco, Anthony P., Stein, John F., Gruen, Jeffrey R., Olson, Richard K., Willcutt, Erik G., DeFries, John C., Pennington, Bruce F., Smith, Shelley D., Wright, Margaret J., Martin, Nicholas G., Auton, Adam, Bates, Timothy C., Fisher, Simon E., and Luciano, Michelle

Published
2022
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29. Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Author

Lahti, Jari, Tuominen, Samuli, Yang, Qiong, Pergola, Giulio, Ahmad, Shahzad, Amin, Najaf, Armstrong, Nicola J., Beiser, Alexa, Bey, Katharina, Bis, Joshua C., Boerwinkle, Eric, Bressler, Jan, Campbell, Archie, Campbell, Harry, Chen, Qiang, Corley, Janie, Cox, Simon R., Davies, Gail, De Jager, Philip L., Derks, Eske M., Faul, Jessica D., Fitzpatrick, Annette L., Fohner, Alison E., Ford, Ian, Fornage, Myriam, Gerring, Zachary, Grabe, Hans J., Grodstein, Francine, Gudnason, Vilmundur, Simonsick, Eleanor, Holliday, Elizabeth G., Joshi, Peter K., Kajantie, Eero, Kaprio, Jaakko, Karell, Pauliina, Kleineidam, Luca, Knol, Maria J., Kochan, Nicole A., Kwok, John B., Leber, Markus, Lam, Max, Lee, Teresa, Li, Shuo, Loukola, Anu, Luck, Tobias, Marioni, Riccardo E., Mather, Karen A., Medland, Sarah, Mirza, Saira S., Nalls, Mike A., Nho, Kwangsik, O’Donnell, Adrienne, Oldmeadow, Christopher, Painter, Jodie, Pattie, Alison, Reppermund, Simone, Risacher, Shannon L., Rose, Richard J., Sadashivaiah, Vijay, Scholz, Markus, Satizabal, Claudia L., Schofield, Peter W., Schraut, Katharina E., Scott, Rodney J., Simino, Jeannette, Smith, Albert V., Smith, Jennifer A., Stott, David J., Surakka, Ida, Teumer, Alexander, Thalamuthu, Anbupalam, Trompet, Stella, Turner, Stephen T., van der Lee, Sven J., Villringer, Arno, Völker, Uwe, Wilson, Robert S., Wittfeld, Katharina, Vuoksimaa, Eero, Xia, Rui, Yaffe, Kristine, Yu, Lei, Zare, Habil, Zhao, Wei, Ames, David, Attia, John, Bennett, David A., Brodaty, Henry, Chasman, Daniel I., Goldman, Aaron L., Hayward, Caroline, Ikram, M. Arfan, Jukema, J. Wouter, Kardia, Sharon L. R., Lencz, Todd, Loeffler, Markus, Mattay, Venkata S., Palotie, Aarno, Psaty, Bruce M., Ramirez, Alfredo, Ridker, Paul M., Riedel-Heller, Steffi G., Sachdev, Perminder S., Saykin, Andrew J., Scherer, Martin, Schofield, Peter R., Sidney, Stephen, Starr, John M., Trollor, Julian, Ulrich, William, Wagner, Michael, Weir, David R., Wilson, James F., Wright, Margaret J., Weinberger, Daniel R., Debette, Stephanie, Eriksson, Johan G., Mosley, Jr., Thomas H., Launer, Lenore J., van Duijn, Cornelia M., Deary, Ian J., Seshadri, Sudha, and Räikkönen, Katri

Published
2022
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30. Performance Feedback for Human Papillomavirus Vaccination: A Randomized Trial From the American Academy of Pediatrics Pediatric Research in Office Settings Research Network

Author

Fiks, Alexander G., Stephens-Shields, Alisa J., Kelly, Mary Kate, Localio, Russell, Hannan, Chloe, Grundmeier, Robert W., Shone, Laura P., Steffes, Jennifer, Wright, Margaret, Breck, Abigail, Rand, Cynthia M., Albertin, Christina, Humiston, Sharon G., McFarland, Greta, Abney, Dianna E., and Szilagyi, Peter G.

Published
2023
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31. Accidents

Author

WRIGHT, MARGARET

Published
2021

32. POETRY

Author

Wright, Margaret

Published
2021

33. SCN1A overexpression, associated with a genomic region marked by a risk variant for a common epilepsy, raises seizure susceptibility

Author

Silvennoinen, Katri, Gawel, Kinga, Tsortouktzidis, Despina, Pitsch, Julika, Alhusaini, Saud, van Loo, Karen M. J., Picardo, Richard, Michalak, Zuzanna, Pagni, Susanna, Martins Custodio, Helena, Mills, James, Whelan, Christopher D., de Zubicaray, Greig I., McMahon, Katie L., van der Ent, Wietske, Kirstein-Smardzewska, Karolina J., Tiraboschi, Ettore, Mudge, Jonathan M., Frankish, Adam, Thom, Maria, Wright, Margaret J., Thompson, Paul M., Schoch, Susanne, Becker, Albert J., Esguerra, Camila V., and Sisodiya, Sanjay M.

Published
2022
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34. HPV vaccine recommendation profiles among a national network of pediatric practitioners: understanding contributors to parental vaccine hesitancy and acceptance

Author

Hopfer, Suellen, Wright, Margaret E, Pellman, Harry, Wasserman, Richard, and Fiks, Alexander G

Subjects
Paediatrics, Biomedical and Clinical Sciences, Prevention, Immunization, Pediatric, Adolescent Sexual Activity, Vaccine Related, Sexually Transmitted Infections, Clinical Research, Infectious Diseases, HPV and/or Cervical Cancer Vaccines, Cancer, Prevention of disease and conditions, and promotion of well-being, 3.4 Vaccines, Good Health and Well Being, Adult, Aged, Decision Making, Female, Health Knowledge, Attitudes, Practice, Humans, Male, Middle Aged, Papillomavirus Infections, Papillomavirus Vaccines, Parents, Patient Acceptance of Health Care, Pediatricians, Practice Patterns, Physicians', Surveys and Questionnaires, Vaccination, Vaccination Refusal, HPV vaccine, latent class analysis, vaccine hesitancy, clinician communication, Immunology, Medical Microbiology, Pharmacology and Pharmaceutical Sciences, Virology, Medical biotechnology, Medical microbiology
Abstract

Background: Practitioner communication is one of the most important influences and predictors of HPV vaccination uptake. The objective of this study was to conduct a latent class analysis characterizing pediatric practitioner HPV recommendation patterns. Methods: Pediatric practitioners of the American Academy of Pediatrics' (AAP) Pediatric Research in Office Settings (PROS) national network completed an online survey where they were presented with 5 hypothetical vignettes of well child visits and responded to questions. Questions asked about their use of communication strategies, assessments about the adolescent patient becoming sexually active in the next 2 years for decision-making about HPV vaccine recommendation, and peer norms. Latent class analysis characterized practitioner subgroups based on their response patterns to 10 survey questions. Multinomial logistic regression examined practitioner characteristics associated with each profile. Results: Among 470 respondents, we identified three distinct practitioner HPV vaccine recommendation profiles: (1) Engagers (52%) followed national age-based guidelines, strongly recommended HPV vaccination, and perceived peers as strongly recommending; (2) Protocol Followers (20%) also strongly recommended HPV vaccination, but were less likely to engage families in a discussion about benefits; and (3) Ambivalent HPV Vaccine Recommenders (28%) delayed or did not recommend HPV vaccination and were more likely to use judgment about whether adolescents will become sexually active in the next two years. Practicing in a suburban setting was associated with twice the odds of being an Ambivalent Recommender relative to being an Engager (OR = 2.2; 95% CI:1.1-4.1). Conclusions: Findings underscore the importance of continued efforts to bolster practitioner adoption of evidence-based approaches to HPV vaccine recommendation especially among Ambivalent Recommenders.

Published
2019

35. 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

36. Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects.

Author

Mackey, Scott, Allgaier, Nicholas, Chaarani, Bader, Spechler, Philip, Orr, Catherine, Bunn, Janice, Allen, Nicholas B, Alia-Klein, Nelly, Batalla, Albert, Blaine, Sara, Brooks, Samantha, Caparelli, Elisabeth, Chye, Yann Ying, Cousijn, Janna, Dagher, Alain, Desrivieres, Sylvane, Feldstein-Ewing, Sarah, Foxe, John J, Goldstein, Rita Z, Goudriaan, Anna E, Heitzeg, Mary M, Hester, Robert, Hutchison, Kent, Korucuoglu, Ozlem, Li, Chiang-Shan R, London, Edythe, Lorenzetti, Valentina, Luijten, Maartje, Martin-Santos, Rocio, May, April, Momenan, Reza, Morales, Angelica, Paulus, Martin P, Pearlson, Godfrey, Rousseau, Marc-Etienne, Salmeron, Betty Jo, Schluter, Renée, Schmaal, Lianne, Schumann, Gunter, Sjoerds, Zsuzsika, Stein, Dan J, Stein, Elliot A, Sinha, Rajita, Solowij, Nadia, Tapert, Susan, Uhlmann, Anne, Veltman, Dick, van Holst, Ruth, Whittle, Sarah, Wright, Margaret J, Yücel, Murat, Zhang, Sheng, Yurgelun-Todd, Deborah, Hibar, Derrek P, Jahanshad, Neda, Evans, Alan, Thompson, Paul M, Glahn, David C, Conrod, Patricia, Garavan, Hugh, and ENIGMA Addiction Working Group

Subjects
ENIGMA Addiction Working Group, Brain, Cerebral Cortex, Humans, Substance-Related Disorders, Alcoholism, Amphetamine-Related Disorders, Cocaine-Related Disorders, Marijuana Abuse, Tobacco Use Disorder, Methamphetamine, Organ Size, Adult, Middle Aged, Female, Male, Young Adult, Gray Matter, Support Vector Machine, Mega-Analysis, Structural MRI, Neurosciences, Drug Abuse (NIDA only), Brain Disorders, Alcoholism, Alcohol Use and Health, Substance Misuse, Mental health, Good Health and Well Being, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

ObjectiveAlthough lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes.MethodBrain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings.ResultsLower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects.ConclusionsThe results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

Published
2019

37. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Author

Chauhan, Ganesh, Adams, Hieab HH, Satizabal, Claudia L, Bis, Joshua C, Teumer, Alexander, Sargurupremraj, Muralidharan, Hofer, Edith, Trompet, Stella, Hilal, Saima, Smith, Albert Vernon, Jian, Xueqiu, Malik, Rainer, Traylor, Matthew, Pulit, Sara L, Amouyel, Philippe, Mazoyer, Bernard, Zhu, Yi-Cheng, Kaffashian, Sara, Schilling, Sabrina, Beecham, Gary W, Montine, Thomas J, Schellenberg, Gerard D, Kjartansson, Olafur, Guðnason, Vilmundur, Knopman, David S, Griswold, Michael E, Windham, B Gwen, Gottesman, Rebecca F, Mosley, Thomas H, Schmidt, Reinhold, Saba, Yasaman, Schmidt, Helena, Takeuchi, Fumihiko, Yamaguchi, Shuhei, Nabika, Toru, Kato, Norihiro, Rajan, Kumar B, Aggarwal, Neelum T, De Jager, Philip L, Evans, Denis A, Psaty, Bruce M, Rotter, Jerome I, Rice, Kenneth, Lopez, Oscar L, Liao, Jiemin, Chen, Christopher, Cheng, Ching-Yu, Wong, Tien Y, Ikram, Mohammad K, van der Lee, Sven J, Amin, Najaf, Chouraki, Vincent, DeStefano, Anita L, Aparicio, Hugo J, Romero, Jose R, Maillard, Pauline, DeCarli, Charles, Wardlaw, Joanna M, del C. Valdés Hernández, Maria, Luciano, Michelle, Liewald, David, Deary, Ian J, Starr, John M, Bastin, Mark E, Maniega, Susana Muñoz, Slagboom, P Eline, Beekman, Marian, Deelen, Joris, Uh, Hae-Won, Lemmens, Robin, Brodaty, Henry, Wright, Margaret J, Ames, David, Boncoraglio, Giorgio B, Hopewell, Jemma C, Beecham, Ashley H, Blanton, Susan H, Wright, Clinton B, Sacco, Ralph L, Wen, Wei, Thalamuthu, Anbupalam, Armstrong, Nicola J, Chong, Elizabeth, Schofield, Peter R, Kwok, John B, van der Grond, Jeroen, Stott, David J, Ford, Ian, Jukema, J Wouter, Vernooij, Meike W, Hofman, Albert, Uitterlinden, André G, van der Lugt, Aad, Wittfeld, Katharina, Grabe, Hans J, Hosten, Norbert, von Sarnowski, Bettina, Völker, Uwe, Levi, Christopher, and Jimenez-Conde, Jordi

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Prevention, Genetics, Stroke, Aging, Cardiovascular, Human Genome, Brain Disorders, Cerebrovascular, 2.1 Biological and endogenous factors, Good Health and Well Being, Stroke Genetics Network (SiGN), the International Stroke Genetics Consortium (ISGC), METASTROKE, Alzheimer's Disease Genetics Consortium (ADGC), and the Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveTo explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.MethodsWe performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.ResultsThe mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.ConclusionIn this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Published
2019

38. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder.

Author

Demontis, Ditte, Walters, Raymond K, Martin, Joanna, Mattheisen, Manuel, Als, Thomas D, Agerbo, Esben, Baldursson, Gísli, Belliveau, Rich, Bybjerg-Grauholm, Jonas, Bækvad-Hansen, Marie, Cerrato, Felecia, Chambert, Kimberly, Churchhouse, Claire, Dumont, Ashley, Eriksson, Nicholas, Gandal, Michael, Goldstein, Jacqueline I, Grasby, Katrina L, Grove, Jakob, Gudmundsson, Olafur O, Hansen, Christine S, Hauberg, Mads Engel, Hollegaard, Mads V, Howrigan, Daniel P, Huang, Hailiang, Maller, Julian B, Martin, Alicia R, Martin, Nicholas G, Moran, Jennifer, Pallesen, Jonatan, Palmer, Duncan S, Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Poterba, Timothy, Poulsen, Jesper Buchhave, Ripke, Stephan, Robinson, Elise B, Satterstrom, F Kyle, Stefansson, Hreinn, Stevens, Christine, Turley, Patrick, Walters, G Bragi, Won, Hyejung, Wright, Margaret J, ADHD Working Group of the Psychiatric Genomics Consortium (PGC), Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium, 23andMe Research Team, Andreassen, Ole A, Asherson, Philip, Burton, Christie L, Boomsma, Dorret I, Cormand, Bru, Dalsgaard, Søren, Franke, Barbara, Gelernter, Joel, Geschwind, Daniel, Hakonarson, Hakon, Haavik, Jan, Kranzler, Henry R, Kuntsi, Jonna, Langley, Kate, Lesch, Klaus-Peter, Middeldorp, Christel, Reif, Andreas, Rohde, Luis Augusto, Roussos, Panos, Schachar, Russell, Sklar, Pamela, Sonuga-Barke, Edmund JS, Sullivan, Patrick F, Thapar, Anita, Tung, Joyce Y, Waldman, Irwin D, Medland, Sarah E, Stefansson, Kari, Nordentoft, Merete, Hougaard, David M, Werge, Thomas, Mors, Ole, Mortensen, Preben Bo, Daly, Mark J, Faraone, Stephen V, Børglum, Anders D, and Neale, Benjamin M

Subjects
ADHD Working Group of the Psychiatric Genomics Consortium, Early Lifecourse & Genetic Epidemiology (EAGLE) Consortium, 23andMe Research Team, Brain, Humans, Genetic Predisposition to Disease, Risk, Cohort Studies, Attention Deficit Disorder with Hyperactivity, Gene Expression Regulation, Polymorphism, Single Nucleotide, Adolescent, Child, Child, Preschool, Female, Male, Genome-Wide Association Study, Genetic Loci, Clinical Research, Mental Health, Human Genome, Pediatric, Prevention, Genetics, Brain Disorders, Attention Deficit Hyperactivity Disorder (ADHD), Behavioral and Social Science, Aetiology, 2.1 Biological and endogenous factors, Mental health, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.

Published
2019

40. 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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42. Genome‐wide association meta‐analysis of age at first cannabis use

Author

Minică, Camelia C, Verweij, Karin JH, van der Most, Peter J, Mbarek, Hamdi, Bernard, Manon, van Eijk, Kristel R, Lind, Penelope A, Liu, Meng Zhen, Maciejewski, Dominique F, Palviainen, Teemu, Sánchez‐Mora, Cristina, Sherva, Richard, Taylor, Michelle, Walters, Raymond K, Abdellaoui, Abdel, Bigdeli, Timothy B, Branje, Susan JT, Brown, Sandra A, Casas, Miguel, Corley, Robin P, Davey‐Smith, George, Davies, Gareth E, Ehli, Erik A, Farrer, Lindsay, Fedko, Iryna O, Garcia‐Martínez, Iris, Gordon, Scott D, Hartman, Catharina A, Heath, Andrew C, Hickie, Ian B, Hickman, Matthew, Hopfer, Christian J, Hottenga, Jouke Jan, Kahn, René S, Kaprio, Jaakko, Korhonen, Tellervo, Kranzler, Henry R, Krauter, Ken, van Lier, Pol AC, Madden, Pamela AF, Medland, Sarah E, Neale, Michael C, Meeus, Wim HJ, Montgomery, Grant W, Nolte, Ilja M, Oldehinkel, Albertine J, Pausova, Zdenka, Ramos‐Quiroga, Josep A, Richarte, Vanesa, Rose, Richard J, Shin, Jean, Stallings, Michael C, Wall, Tamara L, Ware, Jennifer J, Wright, Margaret J, Zhao, Hongyu, Koot, Hans M, Paus, Tomas, Hewitt, John K, Ribasés, Marta, Loukola, Anu, Boks, Marco P, Snieder, Harold, Munafò, Marcus R, Gelernter, Joel, Boomsma, Dorret I, Martin, Nicholas G, Gillespie, Nathan A, Vink, Jacqueline M, and Derks, Eske M

Subjects
Biological Psychology, Epidemiology, Health Sciences, Psychology, Biotechnology, Brain Disorders, Substance Misuse, Genetics, Human Genome, Drug Abuse (NIDA only), Prevention, 2.1 Biological and endogenous factors, Aetiology, Mental health, Good Health and Well Being, Adolescent, Adult, Age of Onset, Calcium-Transporting ATPases, Female, Genome-Wide Association Study, Humans, Male, Marijuana Use, Middle Aged, Polymorphism, Single Nucleotide, Twins, Young Adult, Age at first use, ATP2C2, cannabis initiation, genome-wide association, heritability, substance use, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Public health, Clinical and health psychology
Abstract

Background and aimsCannabis is one of the most commonly used substances among adolescents and young adults. Earlier age at cannabis initiation is linked to adverse life outcomes, including multi-substance use and dependence. This study estimated the heritability of age at first cannabis use and identified associations with genetic variants.MethodsA twin-based heritability analysis using 8055 twins from three cohorts was performed. We then carried out a genome-wide association meta-analysis of age at first cannabis use in a discovery sample of 24 953 individuals from nine European, North American and Australian cohorts, and a replication sample of 3735 individuals.ResultsThe twin-based heritability for age at first cannabis use was 38% [95% confidence interval (CI) = 19-60%]. Shared and unique environmental factors explained 39% (95% CI = 20-56%) and 22% (95% CI = 16-29%). The genome-wide association meta-analysis identified five single nucleotide polymorphisms (SNPs) on chromosome 16 within the calcium-transporting ATPase gene (ATP2C2) at P  0.8), with the strongest association at the intronic variant rs1574587 (P = 4.09E-09). Gene-based tests of association identified the ATP2C2 gene on 16q24.1 (P = 1.33e-06). Although the five SNPs and ATP2C2 did not replicate, ATP2C2 has been associated with cocaine dependence in a previous study. ATP2B2, which is a member of the same calcium signalling pathway, has been associated previously with opioid dependence. SNP-based heritability for age at first cannabis use was non-significant.ConclusionAge at cannabis initiation appears to be moderately heritable in western countries, and individual differences in onset can be explained by separate but correlated genetic liabilities. The significant association between age of initiation and ATP2C2 is consistent with the role of calcium signalling mechanisms in substance use disorders.

Published
2018

43. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume.

Author

Vojinovic, Dina, Adams, Hieab H, Jian, Xueqiu, Yang, Qiong, Smith, Albert Vernon, Bis, Joshua C, Teumer, Alexander, Scholz, Markus, Armstrong, Nicola J, Hofer, Edith, Saba, Yasaman, Luciano, Michelle, Bernard, Manon, Trompet, Stella, Yang, Jingyun, Gillespie, Nathan A, van der Lee, Sven J, Neumann, Alexander, Ahmad, Shahzad, Andreassen, Ole A, Ames, David, Amin, Najaf, Arfanakis, Konstantinos, Bastin, Mark E, Becker, Diane M, Beiser, Alexa S, Beyer, Frauke, Brodaty, Henry, Bryan, R Nick, Bülow, Robin, Dale, Anders M, De Jager, Philip L, Deary, Ian J, DeCarli, Charles, Fleischman, Debra A, Gottesman, Rebecca F, van der Grond, Jeroen, Gudnason, Vilmundur, Harris, Tamara B, Homuth, Georg, Knopman, David S, Kwok, John B, Lewis, Cora E, Li, Shuo, Loeffler, Markus, Lopez, Oscar L, Maillard, Pauline, El Marroun, Hanan, Mather, Karen A, Mosley, Thomas H, Muetzel, Ryan L, Nauck, Matthias, Nyquist, Paul A, Panizzon, Matthew S, Pausova, Zdenka, Psaty, Bruce M, Rice, Ken, Rotter, Jerome I, Royle, Natalie, Satizabal, Claudia L, Schmidt, Reinhold, Schofield, Peter R, Schreiner, Pamela J, Sidney, Stephen, Stott, David J, Thalamuthu, Anbupalam, Uitterlinden, Andre G, Valdés Hernández, Maria C, Vernooij, Meike W, Wen, Wei, White, Tonya, Witte, A Veronica, Wittfeld, Katharina, Wright, Margaret J, Yanek, Lisa R, Tiemeier, Henning, Kremen, William S, Bennett, David A, Jukema, J Wouter, Paus, Tomas, Wardlaw, Joanna M, Schmidt, Helena, Sachdev, Perminder S, Villringer, Arno, Grabe, Hans Jörgen, Longstreth, WT, van Duijn, Cornelia M, Launer, Lenore J, Seshadri, Sudha, Ikram, M Arfan, and Fornage, Myriam

Subjects
Lateral Ventricles, Humans, Organ Size, Genome, Human, Aged, Middle Aged, Genome-Wide Association Study, Human Genome, Aging, Genetics, 2.1 Biological and endogenous factors, Neurological
Abstract

The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρgenetic = -0.59, p-value = 3.14 × 10-6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology.

Published
2018

44. Testing associations between cannabis use and subcortical volumes in two large population‐based samples

Author

Gillespie, Nathan A, Neale, Michael C, Bates, Timothy C, Eyler, Lisa T, Fennema‐Notestine, Christine, Vassileva, Jasmin, Lyons, Michael J, Prom‐Wormley, Elizabeth C, McMahon, Katie L, Thompson, Paul M, de Zubicaray, Greig, Hickie, Ian B, McGrath, John J, Strike, Lachlan T, Rentería, Miguel E, Panizzon, Matthew S, Martin, Nicholas G, Franz, Carol E, Kremen, William S, and Wright, Margaret J

Subjects
Biological Psychology, Psychology, Substance Misuse, Neurosciences, Brain Disorders, Drug Abuse (NIDA only), Mental health, Good Health and Well Being, Brain volume, cannabis use, grey matter, imaging, multi-substance use, subcortical, Medical and Health Sciences, Psychology and Cognitive Sciences, Substance Abuse, Public health, Clinical and health psychology
Abstract

Background and aimsDisentangling the putative impact of cannabis on brain morphology from other comorbid substance use is critical. After controlling for the effects of nicotine, alcohol and multi-substance use, this study aimed to determine whether frequent cannabis use is associated with significantly smaller subcortical grey matter volumes.DesignExploratory analyses using mixed linear models, one per region of interest (ROI), were performed whereby individual differences in volume (outcome) at seven subcortical ROIs were regressed onto cannabis and comorbid substance use (predictors).SettingTwo large population-based twin samples from the United States and Australia.ParticipantsA total of 622 young Australian adults [66% female; μage  = 25.9, standard deviation SD) = 3.6] and 474 middle-aged US males (μage  = 56.1SD = 2.6 ) of predominately Anglo-Saxon ancestry with complete substance use and imaging data. Subjects with a history of stroke or traumatic brain injury were excluded.MeasurementsMagnetic resonance imaging (MRI) and volumetric segmentation methods were used to estimate volume in seven subcortical ROIs: thalamus, caudate nucleus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens. Substance use measurements included maximum nicotine and alcohol use, total life-time multi-substance use, maximum cannabis use in the young adults and regular cannabis use in the middle-aged males.FindingsAfter correcting for multiple testing (P = 0.007), cannabis use was unrelated to any subcortical ROI. However, maximum nicotine use was associated with significantly smaller thalamus volumes in middle-aged males.ConclusionsIn exploratory analyses based on young adult and middle-aged samples, normal variation in cannabis use is unrelated statistically to individual differences in brain morphology as measured by subcortical volume.

Published
2018

45. 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

48. Autism-related dietary preferences mediate autism-gut microbiome associations

Author

Yap, Chloe X., Henders, Anjali K., Alvares, Gail A., Wood, David L.A., Krause, Lutz, Tyson, Gene W., Restuadi, Restuadi, Wallace, Leanne, McLaren, Tiana, Hansell, Narelle K., Cleary, Dominique, Grove, Rachel, Hafekost, Claire, Harun, Alexis, Holdsworth, Helen, Jellett, Rachel, Khan, Feroza, Lawson, Lauren P., Leslie, Jodie, Frenk, Mira Levis, Masi, Anne, Mathew, Nisha E., Muniandy, Melanie, Nothard, Michaela, Miller, Jessica L., Nunn, Lorelle, Holtmann, Gerald, Strike, Lachlan T., de Zubicaray, Greig I., Thompson, Paul M., McMahon, Katie L., Wright, Margaret J., Visscher, Peter M., Dawson, Paul A., Dissanayake, Cheryl, Eapen, Valsamma, Heussler, Helen S., McRae, Allan F., Whitehouse, Andrew J.O., Wray, Naomi R., and Gratten, Jacob

Published
2021
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50. Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts

Author

Campos, Adrian I., Thompson, Paul M., Veltman, Dick J., Pozzi, Elena, van Veltzen, Laura S., Jahanshad, Neda, Adams, Mark J., Baune, Bernhard T., Berger, Klaus, Brosch, Katharina, Bülow, Robin, Connolly, Colm G., Dannlowski, Udo, Davey, Christopher G., de Zubicaray, Greig I., Dima, Danai, Erwin-Grabner, Tracy, Evans, Jennifer W., Fu, Cynthia H.Y., Gotlib, Ian H., Goya-Maldonado, Roberto, Grabe, Hans J., Grotegerd, Dominik, Harris, Matthew A., Harrison, Ben J., Hatton, Sean N., Hermesdorf, Marco, Hickie, Ian B., Ho, Tiffany C., Kircher, Tilo, Krug, Axel, Lagopoulos, Jim, Lemke, Hannah, McMahon, Katie, MacMaster, Frank P., Martin, Nicholas G., McIntosh, Andrew M., Medland, Sarah E., Meinert, Susanne, Meller, Tina, Nenadic, Igor, Opel, Nils, Redlich, Ronny, Reneman, Liesbeth, Repple, Jonathan, Sacchet, Matthew D., Schmitt, Simon, Schrantee, Anouk, Sim, Kang, Singh, Aditya, Stein, Frederike, Strike, Lachlan T., van der Wee, Nic J.A., van der Werff, Steven J.A., Völzke, Henry, Waltemate, Lena, Whalley, Heather C., Wittfeld, Katharina, Wright, Margaret J., Yang, Tony T., Zarate, Carlos A., Schmaal, Lianne, and Rentería, Miguel E.

Published
2021
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