24 results on '"O'Connell, Kevin S."'
Search Results
2. Polygenic liability for antipsychotic dosage and polypharmacy - a real-world registry and biobank study
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Koch, Elise, Kämpe, Anders, Alver, Maris, Sigurðarson, Sindri, Einarsson, Guðmundur, Partanen, Juulia, Smith, Robert L., Jaholkowski, Piotr, Taipale, Heidi, Lähteenvuo, Markku, Steen, Nils Eiel, Smeland, Olav B., Djurovic, Srdjan, Molden, Espen, Sigurdsson, Engilbert, Stefánsson, Hreinn, Stefánsson, Kári, Palotie, Aarno, Milani, Lili, O’Connell, Kevin S., and Andreassen, Ole A.
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- 2024
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3. Rapid Metabolism Underlying Subtherapeutic Serum Levels of Atypical Antipsychotics Preceding Clozapine Treatment: A Retrospective Analysis of Real-World Data
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Lenk, Hasan Çağın, Smith, Robert Løvsletten, O’Connell, Kevin S., Andreassen, Ole A., and Molden, Espen
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- 2024
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4. How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry
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Koch, Elise, Pardiñas, Antonio F., O’Connell, Kevin S., Selvaggi, Pierluigi, Camacho Collados, José, Babic, Aleksandar, Marshall, Serena E., Van der Eycken, Erik, Angulo, Cecilia, Lu, Yi, Sullivan, Patrick F., Dale, Anders M., Molden, Espen, Posthuma, Danielle, White, Nathan, Schubert, Alexander, Djurovic, Srdjan, Heimer, Hakon, Stefánsson, Hreinn, Stefánsson, Kári, Werge, Thomas, Sønderby, Ida, O’Donovan, Michael C., Walters, James T.R., Milani, Lili, and Andreassen, Ole A.
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- 2024
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5. Genome-wide analyses reveal shared genetic architecture and novel risk loci between opioid use disorder and general cognitive ability
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Holen, Børge, Kutrolli, Gleda, Shadrin, Alexey A., Icick, Romain, Hindley, Guy, Rødevand, Linn, O’Connell, Kevin S., Frei, Oleksandr, Parker, Nadine, Tesfaye, Markos, Deak, Joseph D., Jahołkowski, Piotr, Dale, Anders M., Djurovic, Srdjan, Andreassen, Ole A., and Smeland, Olav B.
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- 2024
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6. Genetic Overlap Between Global Cortical Brain Structure, C-Reactive Protein, and White Blood Cell Counts
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Parker, Nadine, Cheng, Weiqiu, Hindley, Guy F.L., O’Connell, Kevin S., Karthikeyan, Sandeep, Holen, Børge, Shadrin, Alexey A., Rahman, Zillur, Karadag, Naz, Bahrami, Shahram, Lin, Aihua, Steen, Nils Eiel, Ueland, Thor, Aukrust, Pål, Djurovic, Srdjan, Dale, Anders M., Smeland, Olav B., Frei, Oleksandr, and Andreassen, Ole A.
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- 2024
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7. Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology
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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
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- 2024
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8. Genome-wide association analysis of treatment resistant schizophrenia for variant discovery and polygenic assessment.
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Lenk, Hasan Çağın, Koch, Elise, O'Connell, Kevin S., Smith, Robert Løvsletten, Akkouh, Ibrahim A., Djurovic, Srdjan, Andreassen, Ole A., and Molden, Espen
- Abstract
Background: Treatment resistant schizophrenia (TRS) is broadly defined as inadequate response to adequate treatment and is associated with a substantial increase in disease burden. Clozapine is the only approved treatment for TRS, showing superior clinical effect on overall symptomatology compared to other drugs, and is the prototype of atypical antipsychotics. Risperidone, another atypical antipsychotic with a more distinctive dopamine 2 antagonism, is commonly used in treatment of schizophrenia. Here, we conducted a genome-wide association study on patients treated with clozapine (TRS) vs. risperidone (non-TRS) and investigated whether single variants and/or polygenic risk score for schizophrenia are associated with TRS status. We hypothesized that patients who are treated with clozapine and risperidone might exhibit distinct neurobiological phenotypes that match pharmacological profiles of these drugs and can be explained by genetic differences. The study population (n = 1286) was recruited from a routine therapeutic drug monitoring (TDM) service between 2005 and 2022. History of a detectable serum concentration of clozapine and risperidone (without TDM history of clozapine) defined the TRS (n = 478) and non-TRS (n = 808) group, respectively. Results: We identified a suggestive association between TRS and a common variant within the LINC00523 gene with a significance just below the genome-wide threshold (rs79229764 C > T, OR = 4.89; p = 1.8 × 10
−7 ). Polygenic risk score for schizophrenia was significantly associated with TRS (OR = 1.4, p = 2.1 × 10−6 ). In a large post-mortem brain sample from schizophrenia donors (n = 214; CommonMind Consortium), gene expression analysis indicated that the rs79229764 variant allele might be involved in the regulation of GPR88 and PUDP, which plays a role in striatal neurotransmission and intellectual disability, respectively. Conclusions: We report a suggestive genetic association at the rs79229764 locus with TRS and show that genetic liability for schizophrenia is positively associated with TRS. These results suggest a candidate locus for future follow-up studies to elucidate the molecular underpinnings of TRS. Our findings further demonstrate the value of both single variant and polygenic association analyses for TRS prediction. [ABSTRACT FROM AUTHOR]- Published
- 2024
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9. Corrigendum to “Cross-trait genome-wide association analysis of C-reactive protein level and psychiatric disorders” [Psychoneuroendocrinology 157 (2023) 106368]
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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
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- 2024
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10. Unraveling the shared genetics of common epilepsies and general cognitive ability.
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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, Tauboll, Erik, additional, Ueland, Torill, additional, Steen, Nils Eiel, additional, Djurovic, Srdjan, additional, Dale, Anders M., additional, Frei, Oleksandr, additional, Andreassen, Ole A., additional, and Smeland, Olav B., additional
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- 2024
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11. Toxoplasma gondii infection associated with inflammasome activation and neuronal injury
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Andreou, Dimitrios, primary, Steen, Nils Eiel, additional, Mørch-Johnsen, Lynn, additional, Jørgensen, Kjetil Nordbø, additional, Wortinger, Laura A., additional, Barth, Claudia, additional, Szabo, Attila, additional, O’Connell, Kevin S., additional, Lekva, Tove, additional, Hjell, Gabriela, additional, Johansen, Ingrid Torp, additional, Ormerod, Monica B. E. G., additional, Haukvik, Unn K., additional, Aukrust, Pål, additional, Djurovic, Srdjan, additional, Yolken, Robert H., additional, Andreassen, Ole A., additional, Ueland, Thor, additional, and Agartz, Ingrid, additional
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- 2024
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12. Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR
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Shadrin, Alexey A, primary, Hindley, Guy, additional, Hagen, Espen, additional, Parker, Nadine, additional, Tesfaye, Markos, additional, Jaholkowski, Piotr, additional, Rahman, Zillur, additional, Kutrolli, Gleda, additional, Fominykh, Vera, additional, Djurovic, Srdjan, additional, Smeland, Olav B, additional, O'Connell, Kevin S, additional, van der Meer, Dennis, additional, Frei, Oleksandr, additional, Andreassen, Ole A, additional, and Dale, Anders M, additional
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- 2024
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13. Fine-mapping genomic loci refines bipolar disorder risk genes
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Koromina, Maria, primary, Ravi, Ashvin, additional, Panagiotaropoulou, Georgia, additional, Schilder, Brian M., additional, Humphrey, Jack, additional, Braun, Alice, additional, Bidgeli, Tim, additional, Chatzinakos, Chris, additional, Coombes, Brandon, additional, Kim, Jaeyoung, additional, Liu, Xiaoxi, additional, Terao, Chikashi, additional, O'Connell, Kevin S., additional, Adams, Mark, additional, Adolfsson, Rolf, additional, Alda, Martin, additional, Alfredsson, Lars, additional, Andlauer, Till F.M., additional, Andreassen, Ole A., additional, Antoniou, Anastasia, additional, Baune, Bernhard T., additional, Bengesser, Susanne, additional, Biernacka, Joanna, additional, Boehnke, Michael, additional, Bosch, Rosa, additional, Cairns, Murray, additional, Carr, Vaughan J., additional, Casas, Miquel, additional, Catts, Stanley, additional, Cichon, Sven, additional, Corvin, Aiden, additional, Craddock, Nicholas, additional, Dafnas, Konstantinos, additional, Dalkner, Nina, additional, Dannlowski, Udo, additional, Degenhardt, Franziska, additional, Di Florio, Arianna, additional, Dikeos, Dimitris, additional, Fellendorf, Frederike Tabea, additional, Ferentinos, Panagiotis, additional, Forstner, Andreas J., additional, Forty, Liz, additional, Frye, Mark, additional, Fullerton, Janice M., additional, Gawlik, Micha, additional, Gizer, Ian R., additional, Gordon-Smith, Katherine, additional, Green, Melissa J., additional, Grigoroiu-Serbanescu, Maria, additional, Guzman-Parra, Josep, additional, Hahn, Tim, additional, Henskens, Frans, additional, Hillert, Jan, additional, Jablensky, Assen V., additional, Jones, Lisa, additional, Jones, Ian, additional, Jonsson, Lina, additional, Kelsoe, John R., additional, Kircher, Tilo, additional, Kirov, George, additional, Kittel-Schneider, Sarah, additional, Kogevinas, Manolis, additional, Landen, Mikael, additional, Leboyer, Marion, additional, Lenger, Melanie, additional, Lissowska, Jolanta, additional, Lochner, Christine, additional, Loughland, Carmel, additional, MacIntyre, Donald, additional, Martin, Nicholas G., additional, Maratou, Eirini, additional, Mathews, Carol A., additional, Mayoral, Fermin, additional, McElroy, Susan L., additional, McGregor, Nathaniel W., additional, McIntosh, Andrew, additional, McQuillin, Andrew, additional, Michie, Patricia, additional, Mitchell, Philip B., additional, Moutsatsou, Paraskevi, additional, Mowry, Bryan, additional, Mueller-Myhsok, Bertram, additional, Myers, Richard, additional, Nenadic, Igor, additional, Noethen, Markus M., additional, O'Donovan, Michael, additional, O'Donovan, Claire, additional, Ophoff, Roel A., additional, Owen, Michael J., additional, Pantelis, Chris, additional, Pato, Carlos, additional, Pato, Michele T., additional, Patrinos, George P., additional, Pawlak, Joanna M., additional, Perlis, Roy H., additional, Porichi, Evgenia, additional, Posthuma, Danielle, additional, Ramos-Quiroga, Josep Antoni, additional, Reif, Andreas, additional, Reininghaus, Eva Z., additional, Ribases, Marta, additional, Rietschel, Marcella, additional, Schall, Ulrich, additional, Schulze, Thomas G., additional, Scott, Laura, additional, Scott, Rodney J., additional, Serretti, Alessandro, additional, Shannon Weickert, Cynthia, additional, Smoller, Jordan W., additional, Soler Artigas, Maria Soler, additional, Stein, Dan J., additional, Streit, Fabian, additional, Toma, Claudio, additional, Tooney, Paul, additional, Vieta, Eduard, additional, Vincent, John B., additional, Waldman, Irwin D., additional, Weickert, Thomas, additional, Witt, Stephanie H., additional, Swiatkowska, Beata, additional, Hong, Kyung Sue Sue, additional, Ikeda, Masashi, additional, Iwata, Nakao, additional, Won, Hong-Hee, additional, Edenberg, Howard J., additional, Ripke, Stephan, additional, Raj, Towfique, additional, Coleman, Jonathan R. I., additional, and Mullins, Niamh, additional
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- 2024
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14. Effect of the NFIB rs28379954 T>C polymorphism on CYP2D6‐catalyzed metabolism of solanidine
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Smith, Robert Løvsletten, primary, Wollmann, Birgit M., additional, Størset, Elisabet, additional, Lenk, Hasan Çağın, additional, O'Connell, Kevin S., additional, Kristiansen, Marianne Kringen, additional, Ingelman‐Sundberg, Magnus, additional, and Molden, Espen, additional
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- 2024
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15. Fine-mapping genomic loci refines bipolar disorder risk genes
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Koromina, Maria, Ravi, Ashvin, Panagiotaropoulou, Georgia, Schilder, Brian M., Humphrey, Jack, Braun, Alice, Bidgeli, Tim, Chatzinakos, Chris, Coombes, Brandon, Kim, Jaeyoung, Liu, Xiaoxi, Terao, Chikashi, O'Connell, Kevin S., Adams, Mark, Adolfsson, Rolf, Alda, Martin, Alfredsson, Lars, Andlauer, Till, Andreassen, Ole A., Antoniou, Anastasia, Baune, Bernhard T., Bengesser, Susanne, Biernacka, Joanna, Boehnke, Michael, Bosch, Rosa, Cairns, Murray, Carr, Vaughan J., Casas Brugué, Miquel, Catts, Stanley, Cichon, Sven, Corvin, Aiden, Craddock, Nicholas, Dafnas, Konstantinos, Dalkner, Nina, Dannlowski, Udo, Degenhardt, Franziska, Di Florio, Arianna, Dikeos, Dimitris, Fellendorf, Frederike Tabea, Ferentinos, Panagiotis, Forstner, Andreas Josef, Forty, Liz, Frye, Mark, Fullerton, Janice M., Gawlik, Micha, Gizer, Ian R., Gordon-Smith, Katherine, Green, Melissa J., Grigoroiu-Serbanescu, Maria, Guzman-Parra, José, Hahn, Tim, Henskens, Frans, Hillert, Jan, Jablensky, Assen V., Jones, Lisa, Jones, Ian, Jonsson, Lina, Kelsoe, John R., Kircher, Tilo, Kirov, George, Kittel-Schneider, Sarah, Kogevinas, Manolis, Landén, Mikael, Leboyer, Marion, Lenger, Melanie, Lissowska, Jolanta, Lochner, Christine, Loughland, Carmel, MacIntyre, Donald, Martin, Nicholas G., Maratou, Eirini, Mathews, Carol A., Mayoral, Fermı́n, McElroy, Susan L., McGregor, Nathaniel W., McIntosh, Andrew M., McQuillin, Andrew, Michie, Patricia, Milanova, Vihra, Mitchell, Philip B., Moutsatsou, Paraskevi, Mowry, Bryan, Müller-Myhsok, Bertram, Myers, Richard M., Nenadić, Igor, Nöthen, Markus Maria, O'Donovan, Claire, O'Donovan, Michael, Ophoff, Roel André, Owen, Michael J., Pantelis, Christos, Pato, Carlos, Pato, Michele T., Patrinos, George P., Pawlak, Joanna M., Perlis, Roy H., Porichi, Evgenia, Posthuma, Danielle, Ramos-Quiroga, Josep Antoni, Reif, Andreas, Reininghaus, Eva Z., Ribasés, Marta, Rietschel, Marcella, Schall, Ulrich, Schulze, Thomas Gerd, Scott, Laura J., Scott, Rodney J., Serretti, Alessandro, Shannon Weickert, Cynthia, Smoller, Jordan W., Soler Artigas, Marı́a, Stein, Dan J., Streit, Fabian, Toma, Claudio, Tooney, Paul, Vieta, Eduard, Vincent, John B., Waldman, Irwin D., Weickert, Thomas, Witt, Stephanie H., Hong, Kyung Sue, Ikeda, Masashi, Iwata, Nakao, Świątkowska, Beata, Won, Hong-Hee, Edenberg, Howard J., Ripke, Stephan, Raj, Towfique, Coleman, Jonathan R. I., Mullins, Niamh, Koromina, Maria, Ravi, Ashvin, Panagiotaropoulou, Georgia, Schilder, Brian M., Humphrey, Jack, Braun, Alice, Bidgeli, Tim, Chatzinakos, Chris, Coombes, Brandon, Kim, Jaeyoung, Liu, Xiaoxi, Terao, Chikashi, O'Connell, Kevin S., Adams, Mark, Adolfsson, Rolf, Alda, Martin, Alfredsson, Lars, Andlauer, Till, Andreassen, Ole A., Antoniou, Anastasia, Baune, Bernhard T., Bengesser, Susanne, Biernacka, Joanna, Boehnke, Michael, Bosch, Rosa, Cairns, Murray, Carr, Vaughan J., Casas Brugué, Miquel, Catts, Stanley, Cichon, Sven, Corvin, Aiden, Craddock, Nicholas, Dafnas, Konstantinos, Dalkner, Nina, Dannlowski, Udo, Degenhardt, Franziska, Di Florio, Arianna, Dikeos, Dimitris, Fellendorf, Frederike Tabea, Ferentinos, Panagiotis, Forstner, Andreas Josef, Forty, Liz, Frye, Mark, Fullerton, Janice M., Gawlik, Micha, Gizer, Ian R., Gordon-Smith, Katherine, Green, Melissa J., Grigoroiu-Serbanescu, Maria, Guzman-Parra, José, Hahn, Tim, Henskens, Frans, Hillert, Jan, Jablensky, Assen V., Jones, Lisa, Jones, Ian, Jonsson, Lina, Kelsoe, John R., Kircher, Tilo, Kirov, George, Kittel-Schneider, Sarah, Kogevinas, Manolis, Landén, Mikael, Leboyer, Marion, Lenger, Melanie, Lissowska, Jolanta, Lochner, Christine, Loughland, Carmel, MacIntyre, Donald, Martin, Nicholas G., Maratou, Eirini, Mathews, Carol A., Mayoral, Fermı́n, McElroy, Susan L., McGregor, Nathaniel W., McIntosh, Andrew M., McQuillin, Andrew, Michie, Patricia, Milanova, Vihra, Mitchell, Philip B., Moutsatsou, Paraskevi, Mowry, Bryan, Müller-Myhsok, Bertram, Myers, Richard M., Nenadić, Igor, Nöthen, Markus Maria, O'Donovan, Claire, O'Donovan, Michael, Ophoff, Roel André, Owen, Michael J., Pantelis, Christos, Pato, Carlos, Pato, Michele T., Patrinos, George P., Pawlak, Joanna M., Perlis, Roy H., Porichi, Evgenia, Posthuma, Danielle, Ramos-Quiroga, Josep Antoni, Reif, Andreas, Reininghaus, Eva Z., Ribasés, Marta, Rietschel, Marcella, Schall, Ulrich, Schulze, Thomas Gerd, Scott, Laura J., Scott, Rodney J., Serretti, Alessandro, Shannon Weickert, Cynthia, Smoller, Jordan W., Soler Artigas, Marı́a, Stein, Dan J., Streit, Fabian, Toma, Claudio, Tooney, Paul, Vieta, Eduard, Vincent, John B., Waldman, Irwin D., Weickert, Thomas, Witt, Stephanie H., Hong, Kyung Sue, Ikeda, Masashi, Iwata, Nakao, Świątkowska, Beata, Won, Hong-Hee, Edenberg, Howard J., Ripke, Stephan, Raj, Towfique, Coleman, Jonathan R. I., and Mullins, Niamh
- Abstract
Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).
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- 2024
16. Genomic insights into the shared and distinct genetic architecture of cognitive function and schizophrenia.
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Wootton, Olivia, Shadrin, Alexey A., Bjella, Thomas, Smeland, Olav B., van der Meer, Dennis, Frei, Oleksandr, O'Connell, Kevin S., Ueland, Torill, Andreassen, Ole A., Stein, Dan J., and Dalvie, Shareefa
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COGNITIVE ability ,SCHIZOPHRENIA ,GENOME-wide association studies ,STRUCTURAL equation modeling ,COGNITION disorders ,GENETIC correlations - Abstract
Cognitive impairment is a major determinant of functional outcomes in schizophrenia, however, understanding of the biological mechanisms underpinning cognitive dysfunction in the disorder remains incomplete. Here, we apply Genomic Structural Equation Modelling to identify latent cognitive factors capturing genetic liabilities to 12 cognitive traits measured in the UK Biobank. We identified three broad factors that underly the genetic correlations between the cognitive tests. We explore the overlap between latent cognitive factors, schizophrenia, and schizophrenia symptom dimensions using a complementary set of statistical approaches, applied to data from the latest schizophrenia genome-wide association study (Ncase = 53,386, Ncontrol = 77,258) and the Thematically Organised Psychosis study (Ncase = 306, Ncontrol = 1060). Global genetic correlations showed a significant moderate negative genetic correlation between each cognitive factor and schizophrenia. Local genetic correlations implicated unique genomic regions underlying the overlap between schizophrenia and each cognitive factor. We found substantial polygenic overlap between each cognitive factor and schizophrenia and biological annotation of the shared loci implicated gene-sets related to neurodevelopment and neuronal function. Lastly, we show that the common genetic determinants of the latent cognitive factors are not predictive of schizophrenia symptoms in the Norwegian Thematically Organized Psychosis cohort. Overall, these findings inform our understanding of cognitive function in schizophrenia by demonstrating important differences in the shared genetic architecture of schizophrenia and cognitive abilities. [ABSTRACT FROM AUTHOR]
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- 2024
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17. Identification of novel genomic loci for anxiety symptoms and extensive genetic overlap with psychiatric disorders.
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Tesfaye, Markos, Jaholkowski, Piotr, Shadrin, Alexey A., Meer, Dennis, Hindley, Guy F.L., Holen, Børge, Parker, Nadine, Parekh, Pravesh, Birkenæs, Viktoria, Rahman, Zillur, Bahrami, Shahram, Kutrolli, Gleda, Frei, Oleksandr, Djurovic, Srdjan, Dale, Anders M., Smeland, Olav B., O'Connell, Kevin S., and Andreassen, Ole A.
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GENETIC correlations , *MENTAL illness , *FALSE discovery rate , *AUTISM spectrum disorders , *MENTAL depression - Abstract
Aims Methods Results Conclusions Anxiety disorders are prevalent and anxiety symptoms (ANX) co‐occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders.We included a genome‐wide association study of ANX (meta‐analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention‐deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders.Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k–11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MD n=47$$ \left(n=47\right) $$, BIP n=33$$ \left(n=33\right) $$, SCZ n=71$$ \left(n=71\right) $$, ADHD n=20$$ \left(n=20\right) $$, and ASD n=5$$ \left(n=5\right) $$. Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci.Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross‐disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets. [ABSTRACT FROM AUTHOR]
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- 2024
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18. The genetic landscape of basal ganglia and implications for common brain disorders.
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Bahrami S, Nordengen K, Rokicki J, Shadrin AA, Rahman Z, Smeland OB, Jaholkowski PP, Parker N, Parekh P, O'Connell KS, Elvsåshagen T, Toft M, Djurovic S, Dale AM, Westlye LT, Kaufmann T, and Andreassen OA
- Subjects
- Humans, Female, Male, Middle Aged, Genetic Predisposition to Disease, Aged, Polymorphism, Single Nucleotide, Alzheimer Disease genetics, Alzheimer Disease pathology, Brain Diseases genetics, Brain Diseases pathology, Mendelian Randomization Analysis, White People genetics, Adult, Basal Ganglia diagnostic imaging, Genome-Wide Association Study, Parkinson Disease genetics
- Abstract
The basal ganglia are subcortical brain structures involved in motor control, cognition, and emotion regulation. We conducted univariate and multivariate genome-wide association analyses (GWAS) to explore the genetic architecture of basal ganglia volumes using brain scans obtained from 34,794 Europeans with replication in 4,808 white and generalization in 5,220 non-white Europeans. Our multivariate GWAS identified 72 genetic loci associated with basal ganglia volumes with a replication rate of 55.6% at P < 0.05 and 87.5% showed the same direction, revealing a distributed genetic architecture across basal ganglia structures. Of these, 50 loci were novel, including exonic regions of APOE, NBR1 and HLAA. We examined the genetic overlap between basal ganglia volumes and several neurological and psychiatric disorders. The strongest genetic overlap was between basal ganglia and Parkinson's disease, as supported by robust LD-score regression-based genetic correlations. Mendelian randomization indicated genetic liability to larger striatal volume as potentially causal for Parkinson's disease, in addition to a suggestive causal effect of greater genetic liability to Alzheimer's disease on smaller accumbens. Functional analyses implicated neurogenesis, neuron differentiation and development in basal ganglia volumes. These results enhance our understanding of the genetic architecture and molecular associations of basal ganglia structure and their role in brain disorders., (© 2024. The Author(s).)
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- 2024
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19. Genome-wide meta-analyses of non-response to antidepressants identify novel loci and potential drugs.
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Koch E, Jurgenson T, Einarsson G, Mitchell B, Harder A, Garcia-Marin LM, Krebs K, Lin Y, Xiong Y, Research Team EB, Lu Y, Hagg S, Renteria ME, Medland SE, Wray NR, Martin NG, Huebel C, Breen G, Thorgeirsson T, Stefansson H, Stefansson K, Milani L, Andreassen OA, and O'Connell KS
- Abstract
Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants. We performed genome-wide association meta-analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified two novel loci associated with non-response to antidepressants and showed significant polygenic prediction in independent samples. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.
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- 2024
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20. Identification of Novel Genomic Loci for Anxiety and Extensive Genetic Overlap with Psychiatric Disorders.
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Tesfaye M, Jaholkowski P, Shadrin AA, van der Meer D, Hindley GFL, Holen B, Parker N, Parekh P, Birkenæs V, Rahman Z, Bahrami S, Kutrolli G, Frei O, Djurovic S, Dale AM, Smeland OB, O'Connell KS, and Andreassen OA
- Abstract
Background: Anxiety disorders are prevalent and anxiety symptoms co-occur with many psychiatric disorders. We aimed to identify genomic risk loci associated with anxiety, characterize its genetic architecture, and genetic overlap with psychiatric disorders., Methods: We used the GWAS of anxiety symptoms, schizophrenia, bipolar disorder, major depression, and attention deficit hyperactivity disorder (ADHD). We employed MiXeR and LAVA to characterize the genetic architecture and genetic overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of genomic loci associated with anxiety and those shared with psychiatric disorders. Gene annotation and gene set analyses were conducted using OpenTargets and FUMA, respectively., Results: Anxiety was polygenic with 12.9k estimated genetic risk variants and overlapped extensively with psychiatric disorders (4.1-11.4k variants). MiXeR and LAVA revealed predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 114 novel loci for anxiety by conditioning on the psychiatric disorders. We also identified loci shared between anxiety and major depression ( n = 47), bipolar disorder ( n = 33), schizophrenia ( n = 71), and ADHD ( n = 20). Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways and differential tissue expression in more diverse tissues than those annotated to the shared loci., Conclusions: Anxiety is a highly polygenic phenotype with extensive genetic overlap with psychiatric disorders. These genetic overlaps enabled the identification of novel loci for anxiety. The shared genetic architecture may underlie the extensive cross-disorder comorbidity of anxiety, and the identified genetic loci implicate molecular pathways that may lead to potential drug targets., Competing Interests: Competing interests Ole A. Andreassen is a consultant for Cortechs.ai and Precision Health, and has received speaker’s honoraria from Lundbeck, Janssen, Otsuka and Sunovion. Srdjan Djurovic has received speaker’s honoraria from Lundbeck. Anders M. Dale was a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is also a member of the Scientific Advisory Board of Human Longevity, Inc. (HLI), and the Mohn Medical Imaging and Visualization Centre in Bergen, Norway. He receives funding through a research agreement with General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. The other authors have no conflicts of interest to declare.
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- 2024
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21. Unraveling the shared genetics of common epilepsies and general cognitive ability.
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Karadag N, Hagen E, Shadrin AA, van der Meer D, O'Connell KS, Rahman Z, Kutrolli G, Parker N, Bahrami S, Fominykh V, Heuser K, Taubøll E, Ueland T, Steen NE, Djurovic S, Dale AM, Frei O, Andreassen OA, and Smeland OB
- Abstract
Objective: Cognitive impairment is prevalent among individuals with epilepsy, and it is possible that genetic factors can underlie this relationship. Here, we investigated the potential shared genetic basis of common epilepsies and general cognitive ability (COG)., Methods: We applied linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR) to analyze different aspects of genetic overlap between COG and epilepsies. We used the largest available genome-wide association study data on COG ( n = 269,867) and common epilepsies ( n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), and as well as specific subtypes. We functionally annotated the identified loci using a variety of biological resources and validated the results in independent samples., Results: Using MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for analysis. We show extensive genetic overlap between COG and epilepsies with significant negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 1.0 × 10
-14 ; 'all epilepsy': p = 5.6 × 10-3 )., Significance: Our study demonstrates a substantial genetic basis shared between epilepsies and COG and identifies novel overlapping genomic loci. Enhancing our understanding of the relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.- Published
- 2024
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22. Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci.
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Strom NI, Gerring ZF, Galimberti M, Yu D, Halvorsen MW, Abdellaoui A, Rodriguez-Fontenla C, Sealock JM, Bigdeli T, Coleman JR, Mahjani B, Thorp JG, Bey K, Burton CL, Luykx JJ, Zai G, Alemany S, Andre C, Askland KD, Banaj N, Barlassina C, Nissen JB, Bienvenu OJ, Black D, Bloch MH, Boberg J, Børte S, Bosch R, Breen M, Brennan BP, Brentani H, Buxbaum JD, Bybjerg-Grauholm J, Byrne EM, Cabana-Dominguez J, Camarena B, Camarena A, Cappi C, Carracedo A, Casas M, Cavallini MC, Ciullo V, Cook EH, Crosby J, Cullen BA, De Schipper EJ, Delorme R, Djurovic S, Elias JA, Estivill X, Falkenstein MJ, Fundin BT, Garner L, German C, Gironda C, Goes FS, Grados MA, Grove J, Guo W, Haavik J, Hagen K, Harrington K, Havdahl A, Höffler KD, Hounie AG, Hucks D, Hultman C, Janecka M, Jenike E, Karlsson EK, Kelley K, Klawohn J, Krasnow JE, Krebs K, Lange C, Lanzagorta N, Levey D, Lindblad-Toh K, Macciardi F, Maher B, Mathes B, McArthur E, McGregor N, McLaughlin NC, Meier S, Miguel EC, Mulhern M, Nestadt PS, Nurmi EL, O'Connell KS, Osiecki L, Ousdal OT, Palviainen T, Pedersen NL, Piras F, Piras F, Potluri S, Rabionet R, Ramirez A, Rauch S, Reichenberg A, Riddle MA, Ripke S, Rosário MC, Sampaio AS, Schiele MA, Skogholt AH, Sloofman LGSG, Smit J, Soler AM, Thomas LF, Tifft E, Vallada H, van Kirk N, Veenstra-VanderWeele J, Vulink NN, Walker CP, Wang Y, Wendland JR, Winsvold BS, Yao Y, Zhou H, Agrawal A, Alonso P, Berberich G, Bucholz KK, Bulik CM, Cath D, Denys D, Eapen V, Edenberg H, Falkai P, Fernandez TV, Fyer AJ, Gaziano JM, Geller DA, Grabe HJ, Greenberg BD, Hanna GL, Hickie IB, Hougaard DM, Kathmann N, Kennedy J, Lai D, Landén M, Le Hellard S, Leboyer M, Lochner C, McCracken JT, Medland SE, Mortensen PB, Neale BM, Nicolini H, Nordentoft M, Pato M, Pato C, Pauls DL, Piacentini J, Pittenger C, Posthuma D, Ramos-Quiroga JA, Rasmussen SA, Richter MA, Rosenberg DR, Ruhrmann S, Samuels JF, Sandin S, Sandor P, Spalletta G, Stein DJ, Stewart SE, Storch EA, Stranger BE, Turiel M, Werge T, Andreassen OA, Børglum AD, Walitza S, Hveem K, Hansen BK, Rück CP, Martin NG, Milani L, Mors O, Reichborn-Kjennerud T, Ribasés M, Kvale G, Mataix-Cols D, Domschke K, Grünblatt E, Wagner M, Zwart JA, Breen G, Nestadt G, Kaprio J, Arnold PD, Grice DE, Knowles JA, Ask H, Verweij KJ, Davis LK, Smit DJ, Crowley JJ, Scharf JM, Stein MB, Gelernter J, Mathews CA, Derks EM, and Mattheisen M
- Abstract
Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6 , DALRD3 , CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder., Competing Interests: Chris German is employed by and hold stock or stock options in 23andMe, Inc. Erika L. Nurmi is on the Scientific Advisory Board for Myriad Genetics and Medical Advisory Board for Tourette Association of America and received Clinical trial funding from Emalex and Octapharma Pharmaceuticals. Jeremy Veenstra-VanderWeele has served on advisory boards or consulted with Roche, Novartis, and SynapDx; received research funding from Roche, Novartis, SynapDx, Seaside Therapeutics, Forest, Janssen, Acadia, Yamo, and MapLight; received stipends for editorial work from Wiley and Springer. Jens R. Wendland is a current employee and shareholder of Takeda Pharmaceuticals and a past employee and shareholder of F. Hoffmann-La Roche, Pfizer and Nestle Health Science. Cynthia M. Bulik reports: Pearson (author, royalty recipient).Peter Falkai reports no conflict of interest regarding this study and reports to have received financial support and Advisory Board: Richter, Recordati, Boehringer-Ingelheim, Otsuka, Janssen and Lundbeck. Hans J. Grabe has received travel grants and speakers honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag as well as research funding from Fresenius Medical Care. Ian B. Hickie is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney, Australia. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. Professor Hickie has previously led community-based and pharmaceutical industry-supported (Wyeth, Eli Lily, Servier, Pfizer, AstraZeneca, Janssen Cilag) projects focused on the identification and better management of anxiety and depression. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd which aims to transform mental health services through the use of innovative technologies. Benjamin M. Neale is a member of the scientific advisory board at Deep Genomics and Neumora. Christopher Pittenger consults and/or receives research support from Biohaven Pharmaceuticals, Freedom Biosciences, Ceruvia Lifesciences, Transcend Therapeutics, UCB BioPharma, and F-Prime Capital Partners. He owns equity in Alco Therapeutics. These relationships are not related to the current work. Dan J. Stein has received consultancy honoraria from Discovery Vitality, Johnson & Johnson, Kanna, L’Oreal, Lundbeck, Orion, Sanofi, Servier, Takeda and Vistagen. Eric A. Storch reports receiving research funding to his institution from the Ream Foundation, International OCD Foundation, and NIH. He was formerly a consultant for Brainsway and Biohaven Pharmaceuticals in the past 12 months. He owns stock less than $5000 in NView/Proem for distribution related to the YBOCS scales. He receives book royalties from Elsevier, Wiley, Oxford, American Psychological Association, Guildford, Springer, Routledge, and Jessica Kingsley. Ole A. Andreasson reports to be a consultant to Cortechs.ai, Precision Health AS, speakers honorarium from Otsuka, Lundbeck, Sunovion, Janssen. Anders D. Børglum has received speaker fee from Lundbeck. David Mataix-Cols receives royalties for contributing articles to UpToDate, Wolters Kluwer Health, and personal fees for editorial work from Elsevier, all unrelated to the current work. Murray B. Stein has in the past 3 years received consulting income from Acadia Pharmaceuticals, BigHealth, Biogen, Bionomics, Boehringer Ingelheim, Clexio, Eisai, EmpowerPharm, Engrail Therapeutics, Janssen, Jazz Pharmaceuticals, NeuroTrauma Sciences, Otsuka, PureTech Health, Sage Therapeutics, Sumitomo Pharma, and Roche/Genentech. Dr. Stein has stock options in Oxeia Biopharmaceuticals and EpiVario. He has been paid for his editorial work on Depression and Anxiety (Editor-in-Chief), Biological Psychiatry (Deputy Editor), and UpToDate (Co-Editor-in-Chief for Psychiatry). Joel Gelernter is paid for editorial work by the journal Complex Psychiatry. Pino Alonso has received funding from Biohaven, Boston Scientific, Medtronic. All other authors report no conflicts of interest.
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- 2024
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23. Dissecting the genetic overlap between three complex phenotypes with trivariate MiXeR.
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Shadrin AA, Hindley G, Hagen E, Parker N, Tesfaye M, Jaholkowski P, Rahman Z, Kutrolli G, Fominykh V, Djurovic S, Smeland OB, O'Connell KS, van der Meer D, Frei O, Andreassen OA, and Dale AM
- Abstract
Comorbidities are an increasing global health challenge. Accumulating evidence suggests overlapping genetic architectures underlying comorbid complex human traits and disorders. The bivariate causal mixture model (MiXeR) can quantify the polygenic overlap between complex phenotypes beyond global genetic correlation. Still, the pattern of genetic overlap between three distinct phenotypes, which is important to better characterize multimorbidities, has previously not been possible to quantify. Here, we present and validate the trivariate MiXeR tool, which disentangles the pattern of genetic overlap between three phenotypes using summary statistics from genome-wide association studies (GWAS). Our simulations show that the trivariate MiXeR can reliably reconstruct different patterns of genetic overlap. We further demonstrate how the tool can be used to estimate the proportions of genetic overlap between three phenotypes using real GWAS data, providing examples of complex patterns of genetic overlap between diverse human traits and diseases that could not be deduced from bivariate analyses. This contributes to a better understanding of the etiology of complex phenotypes and the nature of their relationship, which may aid in dissecting comorbidity patterns and their biological underpinnings., Competing Interests: Conflict of Interest Srdjan Djurovic has received speaker’s Honoria from Lundbeck. Anders M. Dale is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is also a member of the Scientific Advisory Board of Human Longevity, Inc. (HLI), and the Mohn Medical Imaging and Visualization Centre in Bergen, Norway. He receives funding through a research agreement with General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict-of-interest policies. Ole A. Andreassen has received speaker fees from Lundbeck, Janssen, Otsuka, and Sunovion and is a consultant to Cortechs.ai and Precision Health AS.
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- 2024
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24. Distinguishing different psychiatric disorders using DDx-PRS.
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Peyrot WJ, Panagiotaropoulou G, Olde Loohuis LM, Adams MJ, Awasthi S, Ge T, McIntosh AM, Mitchell BL, Mullins N, O'Connell KS, Penninx BWJH, Posthuma D, Ripke S, Ruderfer DM, Uffelmann E, Vilhjalmsson BJ, Zhu Z, Smoller JW, and Price AL
- Abstract
Despite great progress on methods for case-control polygenic prediction (e.g. schizophrenia vs. control), there remains an unmet need for a method that genetically distinguishes clinically related disorders (e.g. schizophrenia (SCZ) vs. bipolar disorder (BIP) vs. depression (MDD) vs. control); such a method could have important clinical value, especially at disorder onset when differential diagnosis can be challenging. Here, we introduce a method, Differential Diagnosis-Polygenic Risk Score (DDx-PRS), that jointly estimates posterior probabilities of each possible diagnostic category (e.g. SCZ=50%, BIP=25%, MDD=15%, control=10%) by modeling variance/covariance structure across disorders, leveraging case-control polygenic risk scores (PRS) for each disorder (computed using existing methods) and prior clinical probabilities for each diagnostic category. DDx-PRS uses only summary-level training data and does not use tuning data, facilitating implementation in clinical settings. In simulations, DDx-PRS was well-calibrated (whereas a simpler approach that analyzes each disorder marginally was poorly calibrated), and effective in distinguishing each diagnostic category vs. the rest. We then applied DDx-PRS to Psychiatric Genomics Consortium SCZ/BIP/MDD/control data, including summary-level training data from 3 case-control GWAS ( N =41,917-173,140 cases; total N =1,048,683) and held-out test data from different cohorts with equal numbers of each diagnostic category (total N =11,460). DDx-PRS was well-calibrated and well-powered relative to these training sample sizes, attaining AUCs of 0.66 for SCZ vs. rest, 0.64 for BIP vs. rest, 0.59 for MDD vs. rest, and 0.68 for control vs. rest. DDx-PRS produced comparable results to methods that leverage tuning data, confirming that DDx-PRS is an effective method. True diagnosis probabilities in top deciles of predicted diagnosis probabilities were considerably larger than prior baseline probabilities, particularly in projections to larger training sample sizes, implying considerable potential for clinical utility under certain circumstances. In conclusion, DDx-PRS is an effective method for distinguishing clinically related disorders.
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- 2024
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