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537 results on '"Shadrin, Alexey"'

1. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease

Author

Bergstedt, Jacob, Pasman, Joëlle A., Ma, Ziyan, Harder, Arvid, Yao, Shuyang, Parker, Nadine, Treur, Jorien L., Smit, Dirk J. A., Frei, Oleksandr, Shadrin, Alexey A., Meijsen, Joeri J., Shen, Qing, Hägg, Sara, Tornvall, Per, Buil, Alfonso, Werge, Thomas, Hjerling-Leffler, Jens, Als, Thomas D., Børglum, Anders D., Lewis, Cathryn M., McIntosh, Andrew M., Valdimarsdóttir, Unnur A., Andreassen, Ole A., Sullivan, Patrick F., Lu, Yi, and Fang, Fang

Published
2024
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4. An evolutionary timeline of the oxytocin signaling pathway

Author

Sartorius, Alina M., Rokicki, Jaroslav, Birkeland, Siri, Bettella, Francesco, Barth, Claudia, de Lange, Ann-Marie G., Haram, Marit, Shadrin, Alexey, Winterton, Adriano, Steen, Nils Eiel, Schwarz, Emanuel, Stein, Dan J., Andreassen, Ole A., van der Meer, Dennis, Westlye, Lars T., Theofanopoulou, Constantina, and Quintana, Daniel S.

Published
2024
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8. Generalization of cortical MOSTest genome-wide associations within and across samples

Author

Loughnan, Robert J, Shadrin, Alexey A, Frei, Oleksandr, van der Meer, Dennis, Zhao, Weiqi, Palmer, Clare E, Thompson, Wesley K, Makowski, Carolina, Jernigan, Terry L, Andreassen, Ole A, Fan, Chun Chieh, and Dale, Anders M

Subjects
Epidemiology, Health Sciences, Genetics, Biomedical Imaging, Human Genome, Neurosciences, 2.1 Biological and endogenous factors, Neurological, Humans, Genome-Wide Association Study, Phenotype, Cognition, Brain, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

Published
2022

10. Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy

Author

Hindley, Guy, Shadrin, Alexey A., van der Meer, Dennis, Parker, Nadine, Cheng, Weiqiu, O’Connell, Kevin S., Bahrami, Shahram, Lin, Aihua, Karadag, Naz, Holen, Børge, Bjella, Thomas, Deary, Ian J., Davies, Gail, Hill, W. David, Bressler, Jan, Seshadri, Sudha, Fan, Chun Chieh, Ueland, Torill, Djurovic, Srdjan, Smeland, Olav B., Frei, Oleksandr, Dale, Anders M., and Andreassen, Ole A.

Published
2023
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14. Genome-wide analysis of anorexia nervosa and major psychiatric disorders and related traits reveals genetic overlap and identifies novel risk loci for anorexia nervosa

Author

Bang, Lasse, Bahrami, Shahram, Hindley, Guy, Smeland, Olav B., Rødevand, Linn, Jaholkowski, Piotr P., Shadrin, Alexey, Connell, Kevin S. O’, Frei, Oleksandr, Lin, Aihua, Rahman, Zillur, Cheng, Weiqiu, Parker, Nadine, Fan, Chun C., Dale, Anders M., Djurovic, Srdjan, Bulik, Cynthia M., and Andreassen, Ole A.

Published
2023
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17. Polygenic resilience scores capture protective genetic effects for Alzheimer’s disease

Author

Hou, Jiahui, Hess, Jonathan L, Armstrong, Nicola, Bis, Joshua C, Grenier-Boley, Benjamin, Karlsson, Ida K, Leonenko, Ganna, Numbers, Katya, O’Brien, Eleanor K, Shadrin, Alexey, Thalamuthu, Anbupalam, Yang, Qiong, Andreassen, Ole A, Brodaty, Henry, Gatz, Margaret, Kochan, Nicole A, Lambert, Jean-Charles, Laws, Simon M, Masters, Colin L, Mather, Karen A, Pedersen, Nancy L, Posthuma, Danielle, Sachdev, Perminder S, Williams, Julie, Fan, Chun Chieh, Faraone, Stephen V, Fennema-Notestine, Christine, Lin, Shu-Ju, Escott-Price, Valentina, Holmans, Peter, Seshadri, Sudha, Tsuang, Ming T, Kremen, William S, and Glatt, Stephen J

Subjects
Genetics, Alzheimer's Disease, Brain Disorders, Human Genome, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Genetic Testing, Neurodegenerative, Prevention, Dementia, Aging, Acquired Cognitive Impairment, Aetiology, 2.1 Biological and endogenous factors, Alzheimer Disease, Apolipoprotein E4, Apolipoproteins E, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Risk Factors, Alzheimer’s Disease Neuroimaging Initiative, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

Polygenic risk scores (PRSs) can boost risk prediction in late-onset Alzheimer's disease (LOAD) beyond apolipoprotein E (APOE) but have not been leveraged to identify genetic resilience factors. Here, we sought to identify resilience-conferring common genetic variants in (1) unaffected individuals having high PRSs for LOAD, and (2) unaffected APOE-ε4 carriers also having high PRSs for LOAD. We used genome-wide association study (GWAS) to contrast "resilient" unaffected individuals at the highest genetic risk for LOAD with LOAD cases at comparable risk. From GWAS results, we constructed polygenic resilience scores to aggregate the addictive contributions of risk-orthogonal common variants that promote resilience to LOAD. Replication of resilience scores was undertaken in eight independent studies. We successfully replicated two polygenic resilience scores that reduce genetic risk penetrance for LOAD. We also showed that polygenic resilience scores positively correlate with polygenic risk scores in unaffected individuals, perhaps aiding in staving off disease. Our findings align with the hypothesis that a combination of risk-independent common variants mediates resilience to LOAD by moderating genetic disease risk.

Published
2022

20. The genetic architecture of human cortical folding

Author

van der Meer, Dennis, Kaufmann, Tobias, Shadrin, Alexey A, Makowski, Carolina, Frei, Oleksandr, Roelfs, Daniel, Monereo-Sánchez, Jennifer, Linden, David EJ, Rokicki, Jaroslav, Alnæs, Dag, de Leeuw, Christiaan, Thompson, Wesley K, Loughnan, Robert, Fan, Chun Chieh, Westlye, Lars T, Andreassen, Ole A, and Dale, Anders M

Subjects
Genetics, Brain Disorders, Human Genome, Neurosciences
Abstract

The folding of the human cerebral cortex is a highly genetically regulated process that allows for a much larger surface area to fit into the cranial vault and optimizes functional organization. Sulcal depth is a robust yet understudied measure of localized folding, previously associated with multiple neurodevelopmental disorders. Here, we report the first genome-wide association study of sulcal depth. Through the multivariate omnibus statistical test (MOSTest) applied to vertex-wise measures from 33,748 U.K. Biobank participants (mean age, 64.3 years; 52.0% female), we identified 856 genome-wide significant loci (P < 5 × 10−8). Comparisons with cortical thickness and surface area indicated that sulcal depth has higher locus yield, heritability, and effective sample size. There was a large amount of genetic overlap between these traits, with gene-based analyses indicating strong associations with neurodevelopmental processes. Our findings demonstrate sulcal depth is a promising neuroimaging phenotype that may enhance our understanding of cortical morphology.

Published
2021

21. Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology

Author

Shadrin, Alexey A, Kaufmann, Tobias, van der Meer, Dennis, Palmer, Clare E, Makowski, Carolina, Loughnan, Robert, Jernigan, Terry L, Seibert, Tyler M, Hagler, Donald J, Smeland, Olav B, Motazedi, Ehsan, Chu, Yunhan, Lin, Aihua, Cheng, Weiqiu, Hindley, Guy, Thompson, Wesley K, Fan, Chun C, Holland, Dominic, Westlye, Lars T, Frei, Oleksandr, Andreassen, Ole A, and Dale, Anders M

Subjects
Epidemiology, Health Sciences, Pediatric, Genetics, Neurosciences, Biomedical Imaging, Human Genome, Brain Disorders, 2.1 Biological and endogenous factors, Aged, Cerebral Cortex, Child, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Multifactorial Inheritance, Neuroimaging, United Kingdom, Multivariate vertex-wise analysis, Cortical surface area, Cortical thickness, Genome-wide association study, Distributed polygenic architecture, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences
Abstract

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Published
2021

22. Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders

Author

Hindley, Guy, Drange, Ole Kristian, Lin, Aihua, Kutrolli, Gleda, Shadrin, Alexey A., Parker, Nadine, O’Connell, Kevin S., Rødevand, Linn, Cheng, Weiqiu, Bahrami, Shahram, Karadag, Naz, Holen, Børge, Jaholkowski, Piotr, Woldeyohannes, Markos Tesfaye, Djurovic, Srdjan, Dale, Anders M., Frei, Oleksandr, Ueland, Thor, Smeland, Olav B., and Andreassen, Ole A.

Published
2023
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23. Genome-wide Association Analysis of Parkinson’s Disease and Schizophrenia Reveals Shared Genetic Architecture and Identifies Novel Risk Loci

Author

Smeland, Olav B, Shadrin, Alexey, Bahrami, Shahram, Broce, Iris, Tesli, Martin, Frei, Oleksandr, Wirgenes, Katrine V, O'Connell, Kevin S, Krull, Florian, Bettella, Francesco, Steen, Nils Eiel, Sugrue, Leo, Wang, Yunpeng, Svenningsson, Per, Sharma, Manu, Pihlstrøm, Lasse, Toft, Mathias, O'Donovan, Michael, Djurovic, Srdjan, Desikan, Rahul, Dale, Anders M, and Andreassen, Ole A

Subjects
Genetics, Biotechnology, Aging, Parkinson's Disease, Neurodegenerative, Serious Mental Illness, Human Genome, Neurosciences, Prevention, Mental Health, Schizophrenia, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Neurological, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Parkinson Disease, Polymorphism, Single Nucleotide, GWAS, Genetic overlap, Parkinson’s disease, RERE, ZDHHC2, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundParkinson's disease (PD) and schizophrenia (SCZ) are heritable brain disorders that involve dysregulation of the dopaminergic system. Epidemiological studies have reported potential comorbidity between the disorders, and movement disturbances are common in patients with SCZ before treatment with antipsychotic drugs. Despite this, little is known about shared genetic etiology between the disorders.MethodsWe analyzed recent large genome-wide association studies on patients with SCZ (N = 77,096) and PD (N = 417,508) using a conditional/conjunctional false discovery rate (FDR) approach to evaluate overlap in common genetic variants and improve statistical power for genetic discovery. Using a variety of biological resources, we functionally characterized the identified genomic loci.ResultsWe observed genetic enrichment in PD conditional on associations with SCZ and vice versa, indicating polygenic overlap. We then leveraged this cross-trait enrichment using conditional FDR analysis and identified 9 novel PD risk loci and 1 novel SCZ locus at conditional FDR < .01. Furthermore, we identified 9 genomic loci jointly associated with PD and SCZ at conjunctional FDR < .05. There was an even distribution of antagonistic and agonistic effect directions among the shared loci, in line with the insignificant genetic correlation between the disorders. Of 67 genes mapped to the shared loci, 65 are expressed in the human brain and show cell type-specific expression profiles.ConclusionsAltogether, the study increases understanding of the genetic architectures underlying SCZ and PD, indicating that common molecular genetic mechanisms may contribute to overlapping pathophysiological and clinical features between the disorders.

Published
2021

26. Shared genetic architecture between schizophrenia and subcortical brain volumes implicates early neurodevelopmental processes and brain development in childhood

Author

Cheng, Weiqiu, van der Meer, Dennis, Parker, Nadine, Hindley, Guy, O’Connell, Kevin S., Wang, Yunpeng, Shadrin, Alexey A., Alnæs, Dag, Bahrami, Shahram, Lin, Aihua, Karadag, Naz, Holen, Børge, Fernandez-Cabello, Sara, Fan, Chun-Chieh, Dale, Anders M., Djurovic, Srdjan, Westlye, Lars T., Frei, Oleksandr, Smeland, Olav B., and Andreassen, Ole A.

Published
2022
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27. Genome-wide meta-analysis for Alzheimer’s disease cerebrospinal fluid biomarkers

Author

Jansen, Iris E., van der Lee, Sven J., Gomez-Fonseca, Duber, de Rojas, Itziar, Dalmasso, Maria Carolina, Grenier-Boley, Benjamin, Zettergren, Anna, Mishra, Aniket, Ali, Muhammad, Andrade, Victor, Bellenguez, Céline, Kleineidam, Luca, Küçükali, Fahri, Sung, Yun Ju, Tesí, Niccolo, Vromen, Ellen M., Wightman, Douglas P., Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Amouyel, Philippe, Athanasiu, Lavinia, Bahrami, Shahram, Bailly, Henri, Belbin, Olivia, Bergh, Sverre, Bertram, Lars, Biessels, Geert Jan, Blennow, Kaj, Blesa, Rafael, Boada, Mercè, Boland, Anne, Buerger, Katharina, Carracedo, Ángel, Cervera-Carles, Laura, Chene, Geneviève, Claassen, Jurgen A. H. R., Debette, Stephanie, Deleuze, Jean-Francois, de Deyn, Peter Paul, Diehl-Schmid, Janine, Djurovic, Srdjan, Dols-Icardo, Oriol, Dufouil, Carole, Duron, Emmanuelle, Düzel, Emrah, Fladby, Tormod, Fortea, Juan, Frölich, Lutz, García-González, Pablo, Garcia-Martinez, Maria, Giegling, Ina, Goldhardt, Oliver, Gobom, Johan, Grimmer, Timo, Haapasalo, Annakaisa, Hampel, Harald, Hanon, Olivier, Hausner, Lucrezia, Heilmann-Heimbach, Stefanie, Helisalmi, Seppo, Heneka, Michael T., Hernández, Isabel, Herukka, Sanna-Kaisa, Holstege, Henne, Jarholm, Jonas, Kern, Silke, Knapskog, Anne-Brita, Koivisto, Anne M., Kornhuber, Johannes, Kuulasmaa, Teemu, Lage, Carmen, Laske, Christoph, Leinonen, Ville, Lewczuk, Piotr, Lleó, Alberto, de Munain, Adolfo López, Lopez-Garcia, Sara, Maier, Wolfgang, Marquié, Marta, Mol, Merel O., Montrreal, Laura, Moreno, Fermin, Moreno-Grau, Sonia, Nicolas, Gael, Nöthen, Markus M., Orellana, Adelina, Pålhaugen, Lene, Papma, Janne M., Pasquier, Florence, Perneczky, Robert, Peters, Oliver, Pijnenburg, Yolande A. L., Popp, Julius, Posthuma, Danielle, Pozueta, Ana, Priller, Josef, Puerta, Raquel, Quintela, Inés, Ramakers, Inez, Rodriguez-Rodriguez, Eloy, Rujescu, Dan, Saltvedt, Ingvild, Sanchez-Juan, Pascual, Scheltens, Philip, Scherbaum, Norbert, Schmid, Matthias, Schneider, Anja, Selbæk, Geir, Selnes, Per, Shadrin, Alexey, Skoog, Ingmar, Soininen, Hilkka, Tárraga, Lluís, Teipel, Stefan, Tijms, Betty, Tsolaki, Magda, Van Broeckhoven, Christine, Van Dongen, Jasper, van Swieten, John C., Vandenberghe, Rik, Vidal, Jean-Sébastien, Visser, Pieter J., Vogelgsang, Jonathan, Waern, Margda, Wagner, Michael, Wiltfang, Jens, Wittens, Mandy M. J., Zetterberg, Henrik, Zulaica, Miren, van Duijn, Cornelia M., Bjerke, Maria, Engelborghs, Sebastiaan, Jessen, Frank, Teunissen, Charlotte E., Pastor, Pau, Hiltunen, Mikko, Ingelsson, Martin, Andreassen, Ole A., Clarimón, Jordi, Sleegers, Kristel, Ruiz, Agustín, Ramirez, Alfredo, Cruchaga, Carlos, Lambert, Jean-Charles, and van der Flier, Wiesje

Published
2022
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29. The relationship between cannabis use, schizophrenia, and bipolar disorder: a genetically informed study

Author

Cheng, Weiqiu, Parker, Nadine, Karadag, Naz, Koch, Elise, Hindley, Guy, Icick, Romain, Shadrin, Alexey, O’Connell, Kevin S, Bjella, Thomas, Bahrami, Shahram, Rahman, Zillur, Tesfaye, Markos, Jaholkowski, Piotr, Rødevand, Linn, Holen, Børge, Lagerberg, Trine Vik, Steen, Nils Eiel, Djurovic, Srdjan, Dale, Anders M, Frei, Oleksandr, Smeland, Olav B, and Andreassen, Ole A

Published
2023
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30. Author Correction: Understanding the genetic determinants of the brain with MOSTest.

Author

van der Meer, Dennis, Frei, Oleksandr, Kaufmann, Tobias, Shadrin, Alexey A, Devor, Anna, Smeland, Olav B, Thompson, Wesley K, Fan, Chun Chieh, Holland, Dominic, Westlye, Lars T, Andreassen, Ole A, and Dale, Anders M

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2020

31. Understanding the genetic determinants of the brain with MOSTest.

Author

van der Meer, Dennis, Frei, Oleksandr, Kaufmann, Tobias, Shadrin, Alexey A, Devor, Anna, Smeland, Olav B, Thompson, Wesley K, Fan, Chun Chieh, Holland, Dominic, Westlye, Lars T, Andreassen, Ole A, and Dale, Anders M

Subjects
Nervous System, Brain, Humans, Genetic Predisposition to Disease, Multivariate Analysis, Female, Genome-Wide Association Study
Abstract

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10-8, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.

Published
2020

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

Author

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

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

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

Published
2019

33. Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology

Author

Karadag, Naz, primary, Hagen, Espen, additional, Shadrin, Alexey A., additional, van der Meer, Dennis, additional, O'Connell, Kevin S., additional, Rahman, Zillur, additional, Kutrolli, Gleda, additional, Parker, Nadine, additional, Bahrami, Shahram, additional, Fominykh, Vera, additional, Heuser, Kjell, additional, Taubøll, Erik, additional, Steen, Nils Eiel, additional, Djurovic, Srdjan, additional, Dale, Anders M., additional, Frei, Oleksandr, additional, Andreassen, Ole A., additional, and Smeland, Olav B., additional

Published
2024
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35. 400. Polygenic Architecture of Metabolic Markers Reveals Distinct Patterns of Overlap With Psychiatric Disorders

Author

van der Meer, Dennis, primary, Rahman, Zillur, additional, Ottas, Aigar, additional, Parekh, Pravesh, additional, Kutrolli, Gleda, additional, Sønderby, Ida, additional, Parker, Nadine, additional, Tesfaye, Markos, additional, Jaholkowski, Piotr, additional, Hindley, Guy, additional, Koch, Elise, additional, Steen, Nils Eiel, additional, Djurovic, Srdjan, additional, O'Connell, Kevin, additional, Smeland, Olav, additional, Frei, Oleksandr, additional, Lehto, Kelli, additional, Alver, Maris, additional, Dale, Anders, additional, Milani, Lili, additional, Shadrin, Alexey, additional, and Andreassen, Ole, additional

Published
2024
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37. Shared genetic loci between clozapine metabolism and granulocyte counts

Author

Koch, Elise, primary, Parker, Nadine, additional, Lock, Siobhan K., additional, Smith, Robert L., additional, Shadrin, Alexey A., additional, Frei, Oleksandr, additional, Dale, Anders M., additional, Djurovic, Srdjan, additional, Molden, Espen, additional, O´Connell, Kevin S., additional, and Andreassen, Ole A., additional

Published
2024
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38. Corrigendum to “Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders” [Psychoneuroendocrinology 157 (2023) 106368]

Author

Hindley, Guy, primary, Drange, Ole Kristian, additional, Lin, Aihua, additional, Kutrolli, Gleda, additional, Shadrin, Alexey A., additional, Parker, Nadine, additional, O’Connell, Kevin S., additional, Rødevand, Linn, additional, Cheng, Weiqiu, additional, Bahrami, Shahram, additional, Karadag, Naz, additional, Holen, Børge, additional, Jaholkowski, Piotr, additional, Woldeyohannes, Markos Tesfaye, additional, Djurovic, Srdjan, additional, Dale, Anders M., additional, Frei, Oleksandr, additional, Ueland, Thor, additional, Smeland, Olav B., additional, and Andreassen, Ole A., additional

Published
2024
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39. New insights into the genetic etiology of Alzheimer’s disease and related dementias

Author

Bellenguez, Céline, Küçükali, Fahri, Jansen, Iris E., Kleineidam, Luca, Moreno-Grau, Sonia, Amin, Najaf, Naj, Adam C., Campos-Martin, Rafael, Grenier-Boley, Benjamin, Andrade, Victor, Holmans, Peter A., Boland, Anne, Damotte, Vincent, van der Lee, Sven J., Costa, Marcos R., Kuulasmaa, Teemu, Yang, Qiong, de Rojas, Itziar, Bis, Joshua C., Yaqub, Amber, Prokic, Ivana, Chapuis, Julien, Ahmad, Shahzad, Giedraitis, Vilmantas, Aarsland, Dag, Garcia-Gonzalez, Pablo, Abdelnour, Carla, Alarcón-Martín, Emilio, Alcolea, Daniel, Alegret, Montserrat, Alvarez, Ignacio, Álvarez, Victoria, Armstrong, Nicola J., Tsolaki, Anthoula, Antúnez, Carmen, Appollonio, Ildebrando, Arcaro, Marina, Archetti, Silvana, Pastor, Alfonso Arias, Arosio, Beatrice, Athanasiu, Lavinia, Bailly, Henri, Banaj, Nerisa, Baquero, Miquel, Barral, Sandra, Beiser, Alexa, Pastor, Ana Belén, Below, Jennifer E., Benchek, Penelope, Benussi, Luisa, Berr, Claudine, Besse, Céline, Bessi, Valentina, Binetti, Giuliano, Bizarro, Alessandra, Blesa, Rafael, Boada, Mercè, Boerwinkle, Eric, Borroni, Barbara, Boschi, Silvia, Bossù, Paola, Bråthen, Geir, Bressler, Jan, Bresner, Catherine, Brodaty, Henry, Brookes, Keeley J., Brusco, Luis Ignacio, Buiza-Rueda, Dolores, Bûrger, Katharina, Burholt, Vanessa, Bush, William S., Calero, Miguel, Cantwell, Laura B., Chene, Geneviève, Chung, Jaeyoon, Cuccaro, Michael L., Carracedo, Ángel, Cecchetti, Roberta, Cervera-Carles, Laura, Charbonnier, Camille, Chen, Hung-Hsin, Chillotti, Caterina, Ciccone, Simona, Claassen, Jurgen A. H. R., Clark, Christopher, Conti, Elisa, Corma-Gómez, Anaïs, Costantini, Emanuele, Custodero, Carlo, Daian, Delphine, Dalmasso, Maria Carolina, Daniele, Antonio, Dardiotis, Efthimios, Dartigues, Jean-François, de Deyn, Peter Paul, de Paiva Lopes, Katia, de Witte, Lot D., Debette, Stéphanie, Deckert, Jürgen, del Ser, Teodoro, Denning, Nicola, DeStefano, Anita, Dichgans, Martin, Diehl-Schmid, Janine, Diez-Fairen, Mónica, Rossi, Paolo Dionigi, Djurovic, Srdjan, Duron, Emmanuelle, Düzel, Emrah, Dufouil, Carole, Eiriksdottir, Gudny, Engelborghs, Sebastiaan, Escott-Price, Valentina, Espinosa, Ana, Ewers, Michael, Faber, Kelley M., Fabrizio, Tagliavini, Nielsen, Sune Fallgaard, Fardo, David W., Farotti, Lucia, Fenoglio, Chiara, Fernández-Fuertes, Marta, Ferrari, Raffaele, Ferreira, Catarina B., Ferri, Evelyn, Fin, Bertrand, Fischer, Peter, Fladby, Tormod, Fließbach, Klaus, Fongang, Bernard, Fornage, Myriam, Fortea, Juan, Foroud, Tatiana M., Fostinelli, Silvia, Fox, Nick C., Franco-Macías, Emlio, Bullido, María J., Frank-García, Ana, Froelich, Lutz, Fulton-Howard, Brian, Galimberti, Daniela, García-Alberca, Jose Maria, García-González, Pablo, Garcia-Madrona, Sebastian, Garcia-Ribas, Guillermo, Ghidoni, Roberta, Giegling, Ina, Giorgio, Giaccone, Goate, Alison M., Goldhardt, Oliver, Gomez-Fonseca, Duber, González-Pérez, Antonio, Graff, Caroline, Grande, Giulia, Green, Emma, Grimmer, Timo, Grünblatt, Edna, Grunin, Michelle, Gudnason, Vilmundur, Guetta-Baranes, Tamar, Haapasalo, Annakaisa, Hadjigeorgiou, Georgios, Haines, Jonathan L., Hamilton-Nelson, Kara L., Hampel, Harald, Hanon, Olivier, Hardy, John, Hartmann, Annette M., Hausner, Lucrezia, Harwood, Janet, Heilmann-Heimbach, Stefanie, Helisalmi, Seppo, Heneka, Michael T., Hernández, Isabel, Herrmann, Martin J., Hoffmann, Per, Holmes, Clive, Holstege, Henne, Vilas, Raquel Huerto, Hulsman, Marc, Humphrey, Jack, Biessels, Geert Jan, Jian, Xueqiu, Johansson, Charlotte, Jun, Gyungah R., Kastumata, Yuriko, Kauwe, John, Kehoe, Patrick G., Kilander, Lena, Ståhlbom, Anne Kinhult, Kivipelto, Miia, Koivisto, Anne, Kornhuber, Johannes, Kosmidis, Mary H., Kukull, Walter A., Kuksa, Pavel P., Kunkle, Brian W., Kuzma, Amanda B., Lage, Carmen, Laukka, Erika J., Launer, Lenore, Lauria, Alessandra, Lee, Chien-Yueh, Lehtisalo, Jenni, Lerch, Ondrej, Lleó, Alberto, Longstreth, Jr, William, Lopez, Oscar, de Munain, Adolfo Lopez, Love, Seth, Löwemark, Malin, Luckcuck, Lauren, Lunetta, Kathryn L., Ma, Yiyi, Macías, Juan, MacLeod, Catherine A., Maier, Wolfgang, Mangialasche, Francesca, Spallazzi, Marco, Marquié, Marta, Marshall, Rachel, Martin, Eden R., Montes, Angel Martín, Rodríguez, Carmen Martínez, Masullo, Carlo, Mayeux, Richard, Mead, Simon, Mecocci, Patrizia, Medina, Miguel, Meggy, Alun, Mehrabian, Shima, Mendoza, Silvia, Menéndez-González, Manuel, Mir, Pablo, Moebus, Susanne, Mol, Merel, Molina-Porcel, Laura, Montrreal, Laura, Morelli, Laura, Moreno, Fermin, Morgan, Kevin, Mosley, Thomas, Nöthen, Markus M., Muchnik, Carolina, Mukherjee, Shubhabrata, Nacmias, Benedetta, Ngandu, Tiia, Nicolas, Gael, Nordestgaard, Børge G., Olaso, Robert, Orellana, Adelina, Orsini, Michela, Ortega, Gemma, Padovani, Alessandro, Paolo, Caffarra, Papenberg, Goran, Parnetti, Lucilla, Pasquier, Florence, Pastor, Pau, Peloso, Gina, Pérez-Cordón, Alba, Pérez-Tur, Jordi, Pericard, Pierre, Peters, Oliver, Pijnenburg, Yolande A. L., Pineda, Juan A., Piñol-Ripoll, Gerard, Pisanu, Claudia, Polak, Thomas, Popp, Julius, Posthuma, Danielle, Priller, Josef, Puerta, Raquel, Quenez, Olivier, Quintela, Inés, Thomassen, Jesper Qvist, Rábano, Alberto, Rainero, Innocenzo, Rajabli, Farid, Ramakers, Inez, Real, Luis M., Reinders, Marcel J. T., Reitz, Christiane, Reyes-Dumeyer, Dolly, Ridge, Perry, Riedel-Heller, Steffi, Riederer, Peter, Roberto, Natalia, Rodriguez-Rodriguez, Eloy, Rongve, Arvid, Allende, Irene Rosas, Rosende-Roca, Maitée, Royo, Jose Luis, Rubino, Elisa, Rujescu, Dan, Sáez, María Eugenia, Sakka, Paraskevi, Saltvedt, Ingvild, Sanabria, Ángela, Sánchez-Arjona, María Bernal, Sanchez-Garcia, Florentino, Juan, Pascual Sánchez, Sánchez-Valle, Raquel, Sando, Sigrid B., Sarnowski, Chloé, Satizabal, Claudia L., Scamosci, Michela, Scarmeas, Nikolaos, Scarpini, Elio, Scheltens, Philip, Scherbaum, Norbert, Scherer, Martin, Schmid, Matthias, Schneider, Anja, Schott, Jonathan M., Selbæk, Geir, Seripa, Davide, Serrano, Manuel, Sha, Jin, Shadrin, Alexey A., Skrobot, Olivia, Slifer, Susan, Snijders, Gijsje J. L., Soininen, Hilkka, Solfrizzi, Vincenzo, Solomon, Alina, Song, Yeunjoo, Sorbi, Sandro, Sotolongo-Grau, Oscar, Spalletta, Gianfranco, Spottke, Annika, Squassina, Alessio, Stordal, Eystein, Tartan, Juan Pablo, Tárraga, Lluís, Tesí, Niccolo, Thalamuthu, Anbupalam, Thomas, Tegos, Tosto, Giuseppe, Traykov, Latchezar, Tremolizzo, Lucio, Tybjærg-Hansen, Anne, Uitterlinden, Andre, Ullgren, Abbe, Ulstein, Ingun, Valero, Sergi, Valladares, Otto, Broeckhoven, Christine Van, Vance, Jeffery, Vardarajan, Badri N., van der Lugt, Aad, Dongen, Jasper Van, van Rooij, Jeroen, van Swieten, John, Vandenberghe, Rik, Verhey, Frans, Vidal, Jean-Sébastien, Vogelgsang, Jonathan, Vyhnalek, Martin, Wagner, Michael, Wallon, David, Wang, Li-San, Wang, Ruiqi, Weinhold, Leonie, Wiltfang, Jens, Windle, Gill, Woods, Bob, Yannakoulia, Mary, Zare, Habil, Zhao, Yi, Zhang, Xiaoling, Zhu, Congcong, Zulaica, Miren, Farrer, Lindsay A., Psaty, Bruce M., Ghanbari, Mohsen, Raj, Towfique, Sachdev, Perminder, Mather, Karen, Jessen, Frank, Ikram, M. Arfan, de Mendonça, Alexandre, Hort, Jakub, Tsolaki, Magda, Pericak-Vance, Margaret A., Amouyel, Philippe, Williams, Julie, Frikke-Schmidt, Ruth, Clarimon, Jordi, Deleuze, Jean-François, Rossi, Giacomina, Seshadri, Sudha, Andreassen, Ole A., Ingelsson, Martin, Hiltunen, Mikko, Sleegers, Kristel, Schellenberg, Gerard D., van Duijn, Cornelia M., Sims, Rebecca, van der Flier, Wiesje M., Ruiz, Agustín, Ramirez, Alfredo, and Lambert, Jean-Charles

Published
2022
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41. Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation.

Author

Frei, Oleksandr, Holland, Dominic, Smeland, Olav B, Shadrin, Alexey A, Fan, Chun Chieh, Maeland, Steffen, O'Connell, Kevin S, Wang, Yunpeng, Djurovic, Srdjan, Thompson, Wesley K, Andreassen, Ole A, and Dale, Anders M

Subjects
Humans, Models, Statistical, Bipolar Disorder, Schizophrenia, Gene Frequency, Multifactorial Inheritance, Linkage Disequilibrium, Models, Genetic, Genome-Wide Association Study, Models, Genetic, Statistical
Abstract

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.

Published
2019

42. Genetic Overlap Between Alzheimer's Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes.

Author

Drange, Ole Kristian, Smeland, Olav Bjerkehagen, Shadrin, Alexey A, Finseth, Per Ivar, Witoelar, Aree, Frei, Oleksandr, Psychiatric Genomics Consortium Bipolar Disorder Working Group, Wang, Yunpeng, Hassani, Sahar, Djurovic, Srdjan, Dale, Anders M, and Andreassen, Ole A

Subjects
Psychiatric Genomics Consortium Bipolar Disorder Working Group, Alzheimer’s disease, GWAS, MARK2, VAC14, affective symptoms, bipolar disorder, cognitive symptoms, pleiotropy, Biotechnology, Neurosciences, Acquired Cognitive Impairment, Human Genome, Aging, Genetics, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Brain Disorders, Neurodegenerative, Alzheimer's Disease, Aetiology, 2.1 Biological and endogenous factors, Alzheimer's disease, Psychology, Cognitive Sciences
Abstract

Background: Alzheimer's disease (AD) and bipolar disorder (BIP) are complex traits influenced by numerous common genetic variants, most of which remain to be detected. Clinical and epidemiological evidence suggest that AD and BIP are related. However, it is not established if this relation is of genetic origin. Here, we applied statistical methods based on the conditional false discovery rate (FDR) framework to detect genetic overlap between AD and BIP and utilized this overlap to increase the power to identify common genetic variants associated with either or both traits. Methods: We obtained genome wide association studies data from the International Genomics of Alzheimer's Project part 1 (17,008 AD cases and 37,154 controls) and the Psychiatric Genetic Consortium Bipolar Disorder Working Group (20,352 BIP cases and 31,358 controls). We used conditional QQ-plots to assess overlap in common genetic variants between AD and BIP. We exploited the genetic overlap to re-rank test-statistics for AD and BIP and improve detection of genetic variants using the conditional FDR framework. Results: Conditional QQ-plots demonstrated a polygenic overlap between AD and BIP. Using conditional FDR, we identified one novel genomic locus associated with AD, and nine novel loci associated with BIP. Further, we identified two novel loci jointly associated with AD and BIP implicating the MARK2 gene (lead SNP rs10792421, conjunctional FDR = 0.030, same direction of effect) and the VAC14 gene (lead SNP rs11649476, conjunctional FDR = 0.022, opposite direction of effect). Conclusion: We found polygenic overlap between AD and BIP and identified novel loci for each trait and two jointly associated loci. Further studies should examine if the shared loci implicating the MARK2 and VAC14 genes could explain parts of the shared and distinct features of AD and BIP.

Published
2019

44. Immune marker levels in severe mental disorders: associations with polygenic risk scores of related mental phenotypes and psoriasis

Author

Werner, Maren Caroline Frogner, Wirgenes, Katrine Verena, Shadrin, Alexey, Lunding, Synve Hoffart, Rødevand, Linn, Hjell, Gabriela, Ormerod, Monica Bettina Elkjær Greenwood, Haram, Marit, Agartz, Ingrid, Djurovic, Srdjan, Melle, Ingrid, Aukrust, Pål, Ueland, Thor, Andreassen, Ole Andreas, and Steen, Nils Eiel

Published
2022
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45. The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition

Author

van der Meer, Dennis, Gurholt, Tiril P., Sønderby, Ida E., Shadrin, Alexey A., Hindley, Guy, Rahman, Zillur, de Lange, Ann-Marie G., Frei, Oleksandr, Leinhard, Olof D., Linge, Jennifer, Simon, Rozalyn, Beck, Dani, Westlye, Lars T., Halvorsen, Sigrun, Dale, Anders M., Karlsen, Tom H., Kaufmann, Tobias, and Andreassen, Ole A.

Published
2022
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47. Darwinian Genetic Drift

Author

Shadrin, Alexey A. and Parkhomchuk, Dmitri V.

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Genetic drift is stochastic fluctuations of alleles frequencies in a population due to sampling effects. We consider a model of drift in an equilibrium population, with high mutation rates: few functional mutations per generation. Such mutation rates are common in multicellular organisms including humans, however they are not explicitly considered in most population genetics models. Under these assumptions the drift shows properties distinct from the classical drift models, which ignore realistic mutation rates: i) All (non-lethal) variants of a site have a characteristic average frequencies, which are independent of population size, however the magnitude of fluctuations around these frequencies depends on population size. ii) There is no "mutational meltdown" due to "low efficiency of selection" for small population size. Population average fitness does not depend on population size. iii) Drift (and molecular clock) can be represented as wandering by compensatory mutations, postulate of neutral mutations is not necessary for explaining the high rate of mutation accumulation. Our results, which adjust the meaning of the neutral theory from the individual neutrality of the majority of mutations, to the collective neutrality of compensatory mutations, are applicable to investigations in phylogeny and coalescent and for GWAS design and analysis.

Published
2017

48. Genetic mechanisms for impaired synaptic plasticity in schizophrenia revealed by computational modeling.

Author

Mäki-Marttunen, Tuomo, Blackwell, Kim T., Akkouh, Ibrahim, Shadrin, Alexey, Valstad, Mathias, Elvsåshagen, Torbjørn, Linne, Marja-Leena, Djurovic, Srdjan, Einevoll, Gaute T., and Andreassen, Ole A.

Subjects
GENE expression, VISUAL evoked potentials, LONG-term potentiation, NEUROPLASTICITY, CINGULATE cortex
Abstract

Schizophrenia phenotypes are suggestive of impaired cortical plasticity in the disease, but the mechanisms of these deficits are unknown. Genomic association studies have implicated a large number of genes that regulate neuromodulation and plasticity, indicating that the plasticity deficits have a genetic origin. Here, we used biochemically detailed computational modeling of postsynaptic plasticity to investigate how schizophrenia-associated genes regulate long-term potentiation (LTP) and depression (LTD). We combined our model with data from postmortem RNA expression studies (CommonMind gene-expression datasets) to assess the consequences of altered expression of plasticity-regulating genes for the amplitude of LTP and LTD. Our results show that the expression alterations observed post mortem, especially those in the anterior cingulate cortex, lead to impaired protein kinase A (PKA)-pathwaymediated LTP in synapses containing GluR1 receptors. We validated these findings using a genotyped electroencephalogram (EEG) dataset where polygenic risk scores for synaptic and ion channel-encoding genes as well as modulation of visual evoked potentials were determined for 286 healthy controls. Our results provide a possible genetic mechanism for plasticity impairments in schizophrenia, which can lead to improved understanding and, ultimately, treatment of the disorder. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Finemap-MiXeR: A variational Bayesian approach for genetic finemapping.

Author

Akdeniz, Bayram Cevdet, Frei, Oleksandr, Shadrin, Alexey, Vetrov, Dmitry, Kropotov, Dmitry, Hovig, Eivind, Andreassen, Ole A., and Dale, Anders M.

Subjects
GENOME-wide association studies, GENETIC variation, MATRIX inversion, LINKAGE disequilibrium, BAYESIAN field theory
Abstract

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBO's gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping. Author summary: Genome-Wide Association Studies report the effect size of each genomic variant as summary statistics. Due to the correlated structure of the genomic variants, it may not be straightforward to determine the actual causal genomic variants from these summary statistics. Finemapping studies aim to identify these causal SNPs using different approaches. Here, we presented a novel finemapping method, called Finemap-MiXeR, to determine the actual causal variants using summary statistics data and weighted linkage disequilibrium matrix as input. Our method is based on Variational Bayesian inference on MiXeR model and Evidence Lower Bound of the model is determined to obtain a tractable optimization function. Afterwards, we determined the first derivatives of this Evidence Lower Bound, and finally, Adaptive Moment Estimation is applied to perform optimization. Our method has been validated on synthetic and real data, and similar or better performance than the existing finemapping tools has been observed. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Novel Loci Associated With Attention-Deficit/Hyperactivity Disorder Are Revealed by Leveraging Polygenic Overlap With Educational Attainment.

Author

Shadrin, Alexey A, Smeland, Olav B, Zayats, Tetyana, Schork, Andrew J, Frei, Oleksandr, Bettella, Francesco, Witoelar, Aree, Li, Wen, Eriksen, Jon A, Krull, Florian, Djurovic, Srdjan, Faraone, Stephen V, Reichborn-Kjennerud, Ted, Thompson, Wesley K, Johansson, Stefan, Haavik, Jan, Dale, Anders M, Wang, Yunpeng, and Andreassen, Ole A

Subjects
Humans, Genetic Predisposition to Disease, Attention Deficit Disorder with Hyperactivity, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Child, Educational Status, Female, Male, Genome-Wide Association Study, Jumonji Domain-Containing Histone Demethylases, attention-deficit/hyperactivity disorder, conditional/conjunctional false discovery rate, educational attainment, genetic overlap, Genetics, Brain Disorders, Pediatric, Attention Deficit Hyperactivity Disorder (ADHD), Mental Health, Human Genome, Biotechnology, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Mental health, Medical and Health Sciences, Psychology and Cognitive Sciences, Developmental & Child Psychology
Abstract

OBJECTIVE:Attention-deficit/hyperactivity disorder (ADHD) is a common and highly heritable psychiatric condition. By exploiting the reported relationship between ADHD and educational attainment (EA), we aimed to improve discovery of ADHD-associated genetic variants and to investigate genetic overlap between these phenotypes. METHOD:A conditional/conjunctional false discovery rate (condFDR/conjFDR) method was applied to genome-wide association study (GWAS) data on ADHD (2,064 trios, 896 cases, and 2,455 controls) and EA (n=328,917) to identify ADHD-associated loci and loci overlapping between ADHD and EA. Identified single nucleotide polymorphisms (SNPs) were tested for association in an independent population-based study of ADHD symptoms (n=17,666). Genetic correlation between ADHD and EA was estimated using LD score regression and Pearson correlation. RESULTS:At levels of condFDR10-fold mutual enrichment of SNPs associated with both traits. CONCLUSION:We identified 5 novel loci associated with ADHD and provided evidence for a shared genetic basis between ADHD and EA. These findings could aid understanding of the genetic risk architecture of ADHD and its relation to EA.

Published
2018

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