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1. Correction: Social connectedness as a determinant of mental health: A scoping review.

Author: Priya J Wickramaratne, Tenzin Yangchen, Lauren Lepow, Braja G Patra, Benjamin S Glicksberg, Ardesheer Talati, Prakash Adekkanattu, Euijung Ryu, Joanna M Biernacka, Alexander Charney, J John Mann, Jyotishman Pathak, Mark Olfson, and Myrna M Weissman
Subjects: Medicine, Science
Abstract: [This corrects the article DOI: 10.1371/journal.pone.0275004.].
Published: 2024
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2. Social connectedness as a determinant of mental health: A scoping review.

Author: Priya J Wickramaratne, Tenzin Yangchen, Lauren Lepow, Braja G Patra, Benjamin Glicksburg, Ardesheer Talati, Prakash Adekkanattu, Euijung Ryu, Joanna M Biernacka, Alexander Charney, J John Mann, Jyotishman Pathak, Mark Olfson, and Myrna M Weissman
Subjects: Medicine, Science
Abstract: Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2021 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 66 unique studies. In research with other than pregnant women, 83% (19 of 23) studies reported that social support benefited symptoms of depression with the remaining 17% (5 of 23) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 83% (24 of 29 studies) found that low social support increased postpartum depressive symptoms. Among 8 of 9 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 8 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social connectedness protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness.
Published: 2022
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3. Impact of variant-level batch effects on identification of genetic risk factors in large sequencing studies.

Author: Daniel P Wickland, Yingxue Ren, Jason P Sinnwell, Joseph S Reddy, Cyril Pottier, Vivekananda Sarangi, Minerva M Carrasquillo, Owen A Ross, Steven G Younkin, Nilüfer Ertekin-Taner, Rosa Rademakers, Matthew E Hudson, Liudmila Sergeevna Mainzer, Joanna M Biernacka, and Yan W Asmann
Subjects: Medicine, Science
Abstract: Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer's Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality.
Published: 2021
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4. Genetic Overlap Between Alzheimer’s Disease and Bipolar Disorder Implicates the MARK2 and VAC14 Genes

Author: Ole Kristian Drange, Olav Bjerkehagen Smeland, Alexey A. Shadrin, Per Ivar Finseth, Aree Witoelar, Oleksandr Frei, Psychiatric Genomics Consortium Bipolar Disorder Working Group, Yunpeng Wang, Sahar Hassani, Srdjan Djurovic, Anders M. Dale, Ole A. Andreassen, Eli A Stahl, Gerome Breen, Andreas J Forstner, Andrew McQuillin, Stephan Ripke, Vassily Trubetskoy, Manuel Mattheisen, Jonathan R I Coleman, Heìleìna A Gaspar, Christiaan A de Leeuw, Stacy Steinberg, Jennifer M Whitehead Pavlides, Maciej Trzaskowski, Tune H Pers, Peter A Holmans, Liam Abbott, Esben Agerbo, Huda Akil, Diego Albani, Ney Alliey-Rodriguez, Thomas D Als, Adebayo Anjorin, Verneri Antilla, Swapnil Awasthi, Judith A Badner, Marie Bækvad-Hansen, Jack D Barchas, Nicholas Bass, Michael Bauer, Richard Belliveau, Sarah E Bergen, Carsten Bøcker Pedersen, Erlend Bøen, Marco Boks, James Boocock, Monika Budde, William Bunney, Margit Burmeister, Jonas Bybjerg-Grauholm, William Byerley, Miquel Casas, Felecia Cerrato, Pablo Cervantes, Kimberly Chambert, Alexander W Charney, Danfeng Chen, Claire Churchhouse, Toni-Kim Clarke, William Coryell, David W Craig, Cristiana Cruceanu, David Curtis, Piotr M Czerski, Anders M Dale, Simone de Jong, Franziska Degenhardt, Jurgen Del-Favero, J Raymond DePaulo, Amanda L Dobbyn, Ashley Dumont, Torbjørn Elvsåshagen, Valentina Escott-Price, Chun Chieh Fan, Sascha B Fischer, Matthew Flickinger, Tatiana M Foroud, Liz Forty, Josef Frank, Christine Fraser, Nelson B Freimer, Louise Friseìn, Katrin Gade, Diane Gage, Julie Garnham, Claudia Giambartolomei, Marianne Giørtz Pedersen, Jaqueline Goldstein, Scott D Gordon, Katherine Gordon-Smith, Elaine K Green, Melissa J Green, Tiffany A Greenwood, Jakob Grove, Weihua Guan, Joseì Guzman Parra, Marian L Hamshere, Martin Hautzinger, Urs Heilbronner, Stefan Herms, Maria Hipolito, Per Hoffmann, Dominic Holland, Laura Huckins, Steìphane Jamain, Jessica S Johnson, Anders Jureìus, Radhika Kandaswamy, Robert Karlsson, James L Kennedy, Sarah Kittel-Schneider, Sarah V Knott, James A Knowles, Manolis Kogevinas, Anna C Koller, Ralph Kupka, Catharina Lavebratt, Jacob Lawrence, William B Lawson, Markus Leber, Phil H Lee, Shawn E Levy, Jun Z Li, Chunyu Liu, Susanne Lucae, Anna Maaser, Donald J MacIntyre, Pamela B Mahon, Wolfgang Maier, Lina Martinsson, Steve McCarroll, Peter McGuffin, Melvin G McInnis, James D McKay, Helena Medeiros, Sarah E Medland, Fan Meng, Lili Milani, Grant W Montgomery, Derek W Morris, Thomas W Mühleisen, Niamh Mullins, Hoang Nguyen, Caroline M Nievergelt, Annelie Nordin Adolfsson, Evaristus A Nwulia, Claire O’Donovan, Loes M Olde Loohuis, Anil P S Ori, Lilijana Oruc, Urban Ösby, Roy H Perlis, Amy Perry, Andrea Pfennig, James B Potash, Shaun M Purcell, Eline J Regeer, Andreas Reif, Ceìline S Reinbold, John P Rice, Fabio Rivas, Margarita Rivera, Panos Roussos, Douglas M Ruderfer, Euijung Ryu, Cristina Saìnchez-Mora, Alan F Schatzberg, William A Scheftner, Nicholas J Schork, Cynthia Shannon Weickert, Tatyana Shehktman, Paul D Shilling, Engilbert Sigurdsson, Claire Slaney, Olav B Smeland, Janet L Sobell, Christine Søholm Hansen, Anne T Spijker, David St Clair, Michael Steffens, John S Strauss, Fabian Streit, Jana Strohmaier, Szabolcs Szelinger, Robert C Thompson, Thorgeir E Thorgeirsson, Jens Treutlein, Helmut Vedder, Weiqing Wang, Stanley J Watson, Thomas W Weickert, Stephanie H Witt, Simon Xi, Wei Xu, Allan H Young, Peter Zandi, Peng Zhang, Sebastian Zollner, Rolf Adolfsson, Ingrid Agartz, Martin Alda, Lena Backlund, Bernhard T Baune, Frank Bellivier, Wade H Berrettini, Joanna M Biernacka, Douglas H R Blackwood, Michael Boehnke, Anders D Børglum, Aiden Corvin, Nicholas Craddock, Mark J Daly, Udo Dannlowski, ToÞnu Esko, Bruno Etain, Mark Frye, Janice M Fullerton, Elliot S Gershon, Michael Gill, Fernando Goes, Maria Grigoroiu-Serbanescu, Joanna Hauser, David M Hougaard, Christina M Hultman, Ian Jones, Lisa A Jones, Reneì S Kahn, George Kirov, Mikael Landeìn, Marion Leboyer, Cathryn M Lewis, Qingqin S Li, Jolanta Lissowska, Nicholas G Martin, Fermin Mayoral, Susan L McElroy, Andrew M McIntosh, Francis J McMahon, Ingrid Melle, Andres Metspalu, Philip B Mitchell, Gunnar Morken, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Richard M Myers, Benjamin M Neale, Vishwajit Nimgaonkar, Merete Nordentoft, Markus M Nöthen, Michael C O’Donovan, Ketil J Oedegaard, Michael J Owen, Sara A Paciga, Carlos Pato, Michele T Pato, Danielle Posthuma, Josep Antoni Ramos-Quiroga, Marta Ribaseìs, Marcella Rietschel, Guy A Rouleau, Martin Schalling, Peter R Schofield, Thomas G Schulze, Alessandro Serretti, Jordan W Smoller, Hreinn Stefansson, Kari Stefansson, Eystein Stordal, Patrick F Sullivan, Gustavo Turecki, Arne E Vaaler, Eduard Vieta, John B Vincent, Thomas Werge, John I Nurnberger, Naomi R Wray, Arianna Di Florio, Howard J Edenberg, Sven Cichon, Roel A Ophoff, Laura J Scott, Ole A Andreassen, John Kelsoe, and Pamela Sklar
Subjects: Alzheimer’s disease, bipolar disorder, GWAS, pleiotropy, cognitive symptoms, affective symptoms, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
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
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5. Lack of replication of the GRIN2A-by-coffee interaction in Parkinson disease.

Author: Ismaïl Ahmed, Pei-Chen Lee, Christina M Lill, Susan Searles Nielsen, Fanny Artaud, Lisa G Gallagher, Marie-Anne Loriot, Claire Mulot, Magali Nacfer, Tian Liu, Joanna M Biernacka, Sebastian Armasu, Kari Anderson, Federico M Farin, Christina Funch Lassen, Johnni Hansen, Jørgen H Olsen, Lars Bertram, Demetrius M Maraganore, Harvey Checkoway, Beate Ritz, and Alexis Elbaz
Subjects: Genetics, QH426-470
Published: 2014
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6. Replication of genome wide association studies of alcohol dependence: support for association with variation in ADH1C.

Author: Joanna M Biernacka, Jennifer R Geske, Terry D Schneekloth, Mark A Frye, Julie M Cunningham, Doo-Sup Choi, Courtney L Tapp, Bradley R Lewis, Maureen S Drews, Tracy L Pietrzak, Colin L Colby, Daniel K Hall-Flavin, Larissa L Loukianova, John A Heit, David A Mrazek, and Victor M Karpyak
Subjects: Medicine, Science
Abstract: Genome-wide association studies (GWAS) have revealed many single nucleotide polymorphisms (SNPs) associated with complex traits. Although these studies frequently fail to identify statistically significant associations, the top association signals from GWAS may be enriched for true associations. We therefore investigated the association of alcohol dependence with 43 SNPs selected from association signals in the first two published GWAS of alcoholism. Our analysis of 808 alcohol-dependent cases and 1,248 controls provided evidence of association of alcohol dependence with SNP rs1614972 in the ADH1C gene (unadjusted p = 0.0017). Because the GWAS study that originally reported association of alcohol dependence with this SNP [1] included only men, we also performed analyses in sex-specific strata. The results suggest that this SNP has a similar effect in both sexes (men: OR (95%CI) = 0.80 (0.66, 0.95); women: OR (95%CI) = 0.83 (0.66, 1.03)). We also observed marginal evidence of association of the rs1614972 minor allele with lower alcohol consumption in the non-alcoholic controls (p = 0.081), and independently in the alcohol-dependent cases (p = 0.046). Despite a number of potential differences between the samples investigated by the prior GWAS and the current study, data presented here provide additional support for the association of SNP rs1614972 in ADH1C with alcohol dependence and extend this finding by demonstrating association with consumption levels in both non-alcoholic and alcohol-dependent populations. Further studies should investigate the association of other polymorphisms in this gene with alcohol dependence and related alcohol-use phenotypes.
Published: 2013
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7. Assessment of Response to Lithium Maintenance Treatment in Bipolar Disorder: A Consortium on Lithium Genetics (ConLiGen) Report.

Author: Mirko Manchia, Mazda Adli, Nirmala Akula, Raffaella Ardau, Jean-Michel Aubry, Lena Backlund, Claudio Em Banzato, Bernhard T Baune, Frank Bellivier, Susanne Bengesser, Joanna M Biernacka, Clara Brichant-Petitjean, Elise Bui, Cynthia V Calkin, Andrew Tai Ann Cheng, Caterina Chillotti, Sven Cichon, Scott Clark, Piotr M Czerski, Clarissa Dantas, Maria Del Zompo, J Raymond Depaulo, Sevilla D Detera-Wadleigh, Bruno Etain, Peter Falkai, Louise Frisén, Mark A Frye, Jan Fullerton, Sébastien Gard, Julie Garnham, Fernando S Goes, Paul Grof, Oliver Gruber, Ryota Hashimoto, Joanna Hauser, Urs Heilbronner, Rebecca Hoban, Liping Hou, Stéphane Jamain, Jean-Pierre Kahn, Layla Kassem, Tadafumi Kato, John R Kelsoe, Sarah Kittel-Schneider, Sebastian Kliwicki, Po-Hsiu Kuo, Ichiro Kusumi, Gonzalo Laje, Catharina Lavebratt, Marion Leboyer, Susan G Leckband, Carlos A López Jaramillo, Mario Maj, Alain Malafosse, Lina Martinsson, Takuya Masui, Philip B Mitchell, Frank Mondimore, Palmiero Monteleone, Audrey Nallet, Maria Neuner, Tomás Novák, Claire O'Donovan, Urban Osby, Norio Ozaki, Roy H Perlis, Andrea Pfennig, James B Potash, Daniela Reich-Erkelenz, Andreas Reif, Eva Reininghaus, Sara Richardson, Guy A Rouleau, Janusz K Rybakowski, Martin Schalling, Peter R Schofield, Oliver K Schubert, Barbara Schweizer, Florian Seemüller, Maria Grigoroiu-Serbanescu, Giovanni Severino, Lisa R Seymour, Claire Slaney, Jordan W Smoller, Alessio Squassina, Thomas Stamm, Jo Steele, Pavla Stopkova, Sarah K Tighe, Alfonso Tortorella, Gustavo Turecki, Naomi R Wray, Adam Wright, Peter P Zandi, David Zilles, Michael Bauer, Marcella Rietschel, Francis J McMahon, Thomas G Schulze, and Martin Alda
Subjects: Medicine, Science
Abstract: The assessment of response to lithium maintenance treatment in bipolar disorder (BD) is complicated by variable length of treatment, unpredictable clinical course, and often inconsistent compliance. Prospective and retrospective methods of assessment of lithium response have been proposed in the literature. In this study we report the key phenotypic measures of the "Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder" scale currently used in the Consortium on Lithium Genetics (ConLiGen) study.Twenty-nine ConLiGen sites took part in a two-stage case-vignette rating procedure to examine inter-rater agreement [Kappa (κ)] and reliability [intra-class correlation coefficient (ICC)] of lithium response. Annotated first-round vignettes and rating guidelines were circulated to expert research clinicians for training purposes between the two stages. Further, we analyzed the distributional properties of the treatment response scores available for 1,308 patients using mixture modeling.Substantial and moderate agreement was shown across sites in the first and second sets of vignettes (κ = 0.66 and κ = 0.54, respectively), without significant improvement from training. However, definition of response using the A score as a quantitative trait and selecting cases with B criteria of 4 or less showed an improvement between the two stages (ICC1 = 0.71 and ICC2 = 0.75, respectively). Mixture modeling of score distribution indicated three subpopulations (full responders, partial responders, non responders).We identified two definitions of lithium response, one dichotomous and the other continuous, with moderate to substantial inter-rater agreement and reliability. Accurate phenotypic measurement of lithium response is crucial for the ongoing ConLiGen pharmacogenomic study.
Published: 2013
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8. PDYN rs2281285 variant association with drinking to avoid emotional or somatic discomfort.

Author: Ulrich W Preuss, Stacey J Winham, Joanna M Biernacka, Jennifer R Geske, Georgy Bakalkin, Gabriele Koller, Peter Zill, Michael Soyka, and Victor M Karpyak
Subjects: Medicine, Science
Abstract: One of the proposed psychobiological pathways of craving attributes the desire for drinking in the context of tension, discomfort or unpleasant emotions, to "negative" (or "relief") craving. The aim of this study was to replicate a previously reported association of the PDYN rs2281285 variant with negative craving using a different phenotyping approach.The TaqMan® Genotyping Assay was used to genotype the rs2281285 variant in 417 German alcohol-dependent subjects. The presence of negative/relief craving was assessed by asking if participants ever ingested alcohol to avoid unwanted emotional or somatic discomfort.The minor allele of rs2281285 was associated with an increased risk of drinking to avoid/escape unwanted emotional or somatic events (OR=2.29, 95% CI=1.08-4.85, p=0.0298).Despite the use of a different phenotyping approach to the measurement of negative craving, our results confirm the association between negative craving and PDYN rs2281285. Genetic markers of negative craving may help to identify subgroups of alcohol-dependent individuals vulnerable to relapse in the context of negative emotions or somatic discomfort, leading to the development of specifically tailored treatment strategies.
Published: 2013
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9. Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics: The PDGene database.

Author: Christina M Lill, Johannes T Roehr, Matthew B McQueen, Fotini K Kavvoura, Sachin Bagade, Brit-Maren M Schjeide, Leif M Schjeide, Esther Meissner, Ute Zauft, Nicole C Allen, Tian Liu, Marcel Schilling, Kari J Anderson, Gary Beecham, Daniela Berg, Joanna M Biernacka, Alexis Brice, Anita L DeStefano, Chuong B Do, Nicholas Eriksson, Stewart A Factor, Matthew J Farrer, Tatiana Foroud, Thomas Gasser, Taye Hamza, John A Hardy, Peter Heutink, Erin M Hill-Burns, Christine Klein, Jeanne C Latourelle, Demetrius M Maraganore, Eden R Martin, Maria Martinez, Richard H Myers, Michael A Nalls, Nathan Pankratz, Haydeh Payami, Wataru Satake, William K Scott, Manu Sharma, Andrew B Singleton, Kari Stefansson, Tatsushi Toda, Joyce Y Tung, Jeffery Vance, Nick W Wood, Cyrus P Zabetian, andMe Genetic Epidemiology of Parkinson's Disease Consortium, International Parkinson's Disease Genomics Consortium, Parkinson's Disease GWAS Consortium, Wellcome Trust Case Control Consortium 2), Peter Young, Rudolph E Tanzi, Muin J Khoury, Frauke Zipp, Hans Lehrach, John P A Ioannidis, and Lars Bertram
Subjects: Genetics, QH426-470
Abstract: More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of -27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P < 5 × 10(-8)) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3 × 10(-8)). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.
Published: 2012
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10. Functional role of the polymorphic 647 T/C variant of ENT1 (SLC29A1) and its association with alcohol withdrawal seizures.

Author: Jeong-Hyun Kim, Victor M Karpyak, Joanna M Biernacka, Hyung Wook Nam, Moonnoh R Lee, Ulrich W Preuss, Peter Zill, Gihyun Yoon, Colin Colby, David A Mrazek, and Doo-Sup Choi
Subjects: Medicine, Science
Abstract: Adenosine is involved in several neurological and behavioral disorders including alcoholism. In cultured cell and animal studies, type 1 equilibrative nucleoside transporter (ENT1, slc29a1), which regulates adenosine levels, is known to regulate ethanol sensitivity and preference. Interestingly, in humans, the ENT1 (SLC29A1) gene contains a non-synonymous single nucleotide polymorphism (647 T/C; rs45573936) that might be involved in the functional change of ENT1.Our functional analysis showed that prolonged ethanol exposure increased adenosine uptake activity of mutant cells (ENT1-216Thr) compared to wild-type (ENT1-216Ile) transfected cells, which might result in reduced extracellular adenosine levels. We found that mice lacking ENT1 displayed increased propensity to ethanol withdrawal seizures compared to wild-type littermates. We further investigated a possible association of the 647C variant with alcoholism and the history of alcohol withdrawal seizures in subjects of European ancestry recruited from two independent sites. Analyses of the combined data set showed an association of the 647C variant and alcohol dependence with withdrawal seizures at the nominally significant level.Together with the functional data, our findings suggest a potential contribution of a genetic variant of ENT1 to the development of alcoholism with increased risk of alcohol withdrawal-induced seizures in humans.
Published: 2011
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11. Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods.

Author: Brooke L Fridley, Gregory D Jenkins, and Joanna M Biernacka
Subjects: Medicine, Science
Abstract: Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fisher's method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.
Published: 2010
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12. Antidepressants that increase mitochondrial energetics may elevate risk of treatment-emergent mania

Author: Manuel Gardea-Resendez, Brandon J. Coombes, Marin Veldic, Susannah J. Tye, Francisco Romo-Nava, Aysegul Ozerdem, Miguel L. Prieto, Alfredo Cuellar-Barboza, Nicolas A. Nunez, Balwinder Singh, Richard S. Pendegraft, Alessandro Miola, Susan L. McElroy, Joanna M. Biernacka, Eva Morava, Tamas Kozicz, and Mark A. Frye
Subjects: Cellular and Molecular Neuroscience, Psychiatry and Mental health, Molecular Biology
Abstract: Preclinical evidence suggests that antidepressants (ADs) may differentially influence mitochondrial energetics. This study was conducted to investigate the relationship between mitochondrial function and illness vulnerability in bipolar disorder (BD), specifically risk of treatment-emergent mania (TEM). Participants with BD already clinically phenotyped as TEM+ (n = 176) or TEM− (n = 516) were further classified whether the TEM associated AD, based on preclinical studies, increased (Mito+, n = 600) or decreased (Mito−, n = 289) mitochondrial electron transport chain (ETC) activity. Comparison of TEM+ rates between Mito+ and Mito− ADs was performed using generalized estimating equations to account for participants exposed to multiple ADs while adjusting for sex, age at time of enrollment into the biobank and BD type (BD-I/schizoaffective vs. BD-II). A total of 692 subjects (62.7% female, 91.4% White, mean age 43.0 ± 14.0 years) including 176 cases (25.3%) of TEM+ and 516 cases (74.7%) of TEM- with previous exposure to Mito+ and/or Mito- antidepressants were identified. Adjusting for age, sex and BD subtype, TEM+ was more frequent with antidepressants that increased (24.7%), versus decreased (13.5%) mitochondrial energetics (OR = 2.21; p = 0.000009). Our preliminary retrospective data suggests there may be merit in reconceptualizing AD classification, not solely based on monoaminergic conventional drug mechanism of action, but additionally based on mitochondrial energetics. Future prospective clinical studies on specific antidepressants and mitochondrial activity are encouraged. Recognizing pharmacogenomic investigation of drug response may extend or overlap to genomics of disease risk, future studies should investigate potential interactions between mitochondrial mechanisms of disease risk and drug response.
Published: 2022
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13. The importance of social activity to risk of major depression in older adults

Author: Alexander W. Charney, Myrna M. Weissman, Euijung Ryu, Brandon J. Coombes, Lauren Lepow, Benjamin S. Glicksberg, Joanna M. Biernacka, Priya Wickramaratne, Mark Olfson, J. John Mann, Gregory D. Jenkins, Ardesheer Talati, Yanshan Wang, and Jyotishman Pathak
Subjects: Gerontology, Proportional hazards model, business.industry, Social activity, Hazard ratio, medicine.disease, Biobank, Psychiatry and Mental health, Cohort, medicine, Major depressive disorder, Social determinants of health, business, Applied Psychology, Depression (differential diagnoses)
Abstract: BackgroundSeveral social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults.MethodsWe used self-reported health-related survey data from 41 174 older adults (50–89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis.ResultsFollowing biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00–2.50) for highest v. lowest level].ConclusionAcross a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.
Published: 2023

14. Genetic variants associated with acamprosate treatment response in alcohol use disorder patients: A multiple omics study

Author: Ming‐Fen Ho, Cheng Zhang, Lixuan Wei, Lingxin Zhang, Irene Moon, Jennifer R. Geske, Michelle K. Skime, Doo‐Sup Choi, Joanna M. Biernacka, Tyler S. Oesterle, Mark A. Frye, Marvin D. Seppala, Victor M. Karpyak, Hu Li, and Richard M. Weinshilboum
Subjects: Pharmacology, Alcoholism, Alcohol Drinking, Ethanol, Taurine, Acamprosate, Humans, Alcohol Deterrents, Genome-Wide Association Study
Abstract: Acamprosate is an anti-craving drug used for the pharmacotherapy of alcohol use disorder (AUD). However, only some patients achieve optimal therapeutic outcomes. This study was designed to explore differences in metabolomic profiles between patients who maintained sobriety and those who relapsed, to determine whether those differences provide insight into variation in acamprosate treatment response phenotypes.We previously conducted an acamprosate trial involving 442 AUD patients, and 267 of these subjects presented themselves for a 3-month follow-up. The primary outcome was abstinence. Clinical information, genomic data and metabolomics data were collected. Baseline plasma samples were assayed using targeted metabolomics.Baseline plasma arginine, threonine, α-aminoadipic acid and ethanolamine concentrations were associated with acamprosate treatment outcomes and baseline craving intensity, a measure that has been associated with acamprosate treatment response. We next applied a pharmacometabolomics-informed genome-wide association study (GWAS) strategy to identify genetic variants that might contribute to variations in plasma metabolomic profiles that were associated with craving and/or acamprosate treatment outcome. Gene expression data for induced pluripotent stem cell-derived forebrain astrocytes showed that a series of genes identified during the metabolomics-informed GWAS were ethanol responsive. Furthermore, a large number of those genes could be regulated by acamprosate. Finally, we identified a series of single nucleotide polymorphisms that were associated with acamprosate treatment outcomes.These results serve as an important step towards advancing our understanding of disease pathophysiology and drug action responsible for variation in acamprosate response and alcohol craving in AUD patients.
Published: 2022
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15. Long-Term Lithium Therapy and Thyroid Disorders in Bipolar Disorder: A Historical Cohort Study

Author: Boney Joseph, Nicolas A. Nunez, Vanessa Pazdernik, Rakesh Kumar, Mehak Pahwa, Mete Ercis, Aysegul Ozerdem, Alfredo B. Cuellar-Barboza, Francisco Romo-Nava, Susan L. McElroy, Brandon J. Coombes, Joanna M. Biernacka, Marius N. Stan, Mark A. Frye, and Balwinder Singh
Subjects: lithium, bipolar disorder, thyroid, mood disorders, retrospective studies, General Neuroscience
Abstract: Lithium has been a cornerstone treatment for bipolar disorder (BD). Despite descriptions in the literature regarding associations between long-term lithium therapy (LTLT) and development of a thyroid disorder (overt/subclinical hypo/hyperthyroidism, thyroid nodule, and goiter) in BD, factors such as time to onset of thyroid abnormalities and impact on clinical outcomes in the course of illness have not been fully characterized. In this study we aimed to compare clinical characteristics of adult BD patients with and without thyroid disorders who were on LTLT. We aimed to identify the incidence of thyroid disorders in patients with BD on LTLT and response to lithium between patients with and without thyroid disorders in BD. The Cox proportional model was used to find the median time to the development of a thyroid disorder. Our results showed that up to 32% of patients with BD on LTLT developed a thyroid disorder, of which 79% developed hypothyroidism, which was corrected with thyroid hormone replacement. We did not find significant differences in lithium response between patients with or without thyroid disorders in BD. Findings from this study suggest that patients with BD and comorbid thyroid disorders when adequately treated have a response to lithium similar to patients with BD and no thyroid disorders.
Published: 2023
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16. TCF7L2 lncRNA: a link between bipolar disorder and body mass index through glucocorticoid signaling

Author: Huanyao Gao, Brandon J. Coombes, Duan Liu, Richard M. Weinshilboum, Daniel C. Kim, Zhenqing Ye, Tamas Ordog, Mark A. Frye, Jeong Heon Lee, Thanh Thanh L. Nguyen, Brenna Sharp, Huaizhi Huang, Liewei Wang, and Joanna M. Biernacka
Subjects: endocrine system, Bipolar Disorder, endocrine system diseases, Induced Pluripotent Stem Cells, Genome-wide association study, Biology, Polymorphism, Single Nucleotide, Transcription Factor 7-Like 2, Body Mass Index, Cellular and Molecular Neuroscience, Humans, SNP, Induced pluripotent stem cell, Glucocorticoids, Molecular Biology, Gene, Gene knockdown, nutritional and metabolic diseases, Psychiatry and Mental health, Diabetes Mellitus, Type 2, Expression quantitative trait loci, Cancer research, RNA, Long Noncoding, Transcription Factor 7-Like 2 Protein, TCF7L2, Genome-Wide Association Study
Abstract: Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI), which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in the BD and BMI interaction remained unclear. In the present study, we demonstrated by studying human induced pluripotent stem cell (hiPSC)-derived astrocytes, cells that highly express TCF7L2 in the CNS, that the BD-BMI GWAS risk SNP is associated with glucocorticoid-dependent repression of the expression of a previously uncharacterized TCF7L2 transcript variant. That transcript is a long non-coding RNA (lncRNA-TCF7L2) that is highly expressed in the CNS but not in peripheral tissues such as the liver and pancreas that are involved in metabolism. In astrocytes, knockdown of the lncRNA-TCF7L2 resulted in decreased expression of the parent gene, TCF7L2, as well as alterations in the expression of a series of genes involved in insulin signaling and diabetes. We also studied the function of TCF7L2 in hiPSC-derived astrocytes by integrating RNA sequencing data after TCF7L2 knockdown with TCF7L2 chromatin-immunoprecipitation sequencing (ChIP-seq) data. Those studies showed that TCF7L2 directly regulated a series of BD risk genes. In summary, these results support the existence of a CNS-based mechanism underlying BD-BMI genetic risk, a mechanism based on a glucocorticoid-dependent expression quantitative trait locus that regulates the expression of a novel TCF7L2 non-coding transcript.
Published: 2021
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17. Genetics and antiepileptic mood stabilizer treatment response in bipolar disorder: what do we know?

Author: Richard M. Weinshilboum, Mark A. Frye, Ada Man Choi Ho, and Joanna M. Biernacka
Subjects: Bipolar Disorder, medicine.drug_class, Lamotrigine, Bioinformatics, Antimanic Agents, Genetics, medicine, Humans, Bipolar disorder, Pharmacology, Valproic Acid, Mood Disorders, business.industry, Mood stabilizer, Carbamazepine, Precision medicine, medicine.disease, Treatment Outcome, Mood, Pharmacogenomics, Molecular Medicine, Anticonvulsants, business, Antipsychotic Agents, medicine.drug
Abstract: Antiepileptic mood stabilizers (AED-MS) are often used to treat bipolar disorder (BD). Similar to other mood disorder medications, AED-MS treatment response varies between patients. Identification of biomarkers associated with treatment response may ultimately help with the delivery of individualized treatment and lead to improved treatment efficacy. Here, we conducted a narrative review of the current knowledge of the pharmacogenomics of AED-MS (valproic acid, lamotrigine and carbamazepine) treatment response in BD, including genetic contributions to AED-MS pharmacokinetics. Genes involved in neurotransmitter systems and drug transport have been shown to be associated with AED-MS treatment response. As more studies are conducted, and experimental and analytical methods advance, knowledge of AED-MS pharmacogenomics is expected to grow and contribute to precision medicine in BD.
Published: 2021
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18. Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study

Author: Richard M. Weinshilboum, David Goldman, Jennifer R. Geske, Victor M. Karpyak, Sofia Pozsonyiova, Colin A. Hodgkinson, Lea Zillich, Ada Man-Choi Ho, Ray Anton, Colin L. Colby, Ming Fen Ho, Brandon J. Coombes, Anthony Batzler, Stephanie S. O'Malley, Josef Frank, Joanna M. Biernacka, M. Rietschel, Karl Mann, Falk Kiefer, and Michelle K. Skime
Subjects: Male, 0301 basic medicine, Oncology, medicine.medical_specialty, Taurine, Narcotic Antagonists, media_common.quotation_subject, Single-nucleotide polymorphism, Alcohol use disorder, Predictive markers, Article, Naltrexone, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Humans, Medicine, media_common, Pharmacology, business.industry, Addiction, Abstinence, medicine.disease, Alcoholism, Psychiatry and Mental health, Treatment Outcome, 030104 developmental biology, Acamprosate, Pharmacogenetics, Pharmacogenomics, Behavioural genetics, Female, Animal studies, business, 030217 neurology & neurosurgery, Alcohol Deterrents, Genome-Wide Association Study, medicine.drug
Abstract: Naltrexone can aid in reducing alcohol consumption, while acamprosate supports abstinence; however, not all patients with alcohol use disorder (AUD) benefit from these treatments. Here we present the first genome-wide association study of AUD treatment outcomes based on data from the COMBINE and PREDICT studies of acamprosate and naltrexone, and the Mayo Clinic CITA study of acamprosate. Primary analyses focused on treatment outcomes regardless of pharmacological intervention and were followed by drug-stratified analyses to identify treatment-specific pharmacogenomic predictors of acamprosate and naltrexone response. Treatment outcomes were defined as: (1) time until relapse to any drinking (TR) and (2) time until relapse to heavy drinking (THR; ≥ 5 drinks for men, ≥4 drinks for women in a day), during the first 3 months of treatment. Analyses were performed within each dataset, followed by meta-analysis across the studies (N = 1083 European ancestry participants). Single nucleotide polymorphisms (SNPs) in the BRE gene were associated with THR (min p = 1.6E−8) in the entire sample, while two intergenic SNPs were associated with medication-specific outcomes (naltrexone THR: rs12749274, p = 3.9E−8; acamprosate TR: rs77583603, p = 3.1E−9). The top association signal for TR (p = 7.7E−8) and second strongest signal in the THR (p = 6.1E−8) analysis of naltrexone-treated patients maps to PTPRD, a gene previously implicated in addiction phenotypes in human and animal studies. Leave-one-out polygenic risk score analyses showed significant associations with TR (p = 3.7E−4) and THR (p = 2.6E−4). This study provides the first evidence of a polygenic effect on AUD treatment response, and identifies genetic variants associated with potentially medication-specific effects on AUD treatment response.
Published: 2021
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19. Polygenic risk score analysis identifies deleterious protein-coding variants in novel immune pathway genes ATP8B4, FCGR1A, and LILRB1 that associate with Alzheimer’s disease

Author: Joseph S. Reddy, Xue Wang, Mariet Allen, Minerva M. Carrasquillo, Joanna M. Biernacka, Gregory D. Jenkins, Brandon J. Coombes, Olivia Belbin, Todd E. Golde, Nilüfer Ertekin-Taner, and Steven G. Younkin
Abstract: Background: Alterations in innate immunity are pathologically associated with and genetically implicated in Alzheimer’s disease (AD). In the whole exome sequence (WES) dataset generated by the Alzheimer’s Disease Sequencing Project (ADSP), only the previously identified p.R47H variant in the innate immunity gene, TREM2, shows study-wide association with risk of AD. Using a novel approach, we searched the ADSP WES data to identify additional immune pathway genes with deleterious variants that, like TREM2.pR47H, show strong association with AD. Methods: Using polygenic risk scores (PRS) to analyze association with AD, we evaluated deleterious variants (CADD Phred-scaled score > 20) with a minor allele count of 20 or more in 228 genes comprising an immune co-expression network containing TREM2 (CENTREM2). A significant polygenic component composed of deleterious stop-gain and non-synonymous variants was identified, and false discovery rates were determined for the variants in this component. In genes harboring a significant variant, PRS for all variants in the genes were then analyzed. Results: The PRS for the 182 deleterious variants in CENTREM2 showed significant association with AD that was driven by 142 deleterious variants (136 non-synonymous, 6 stop-gain). In the 142 variant polygenic component, four variants had significant AD risk association: TREM2.pR47H, two deleterious stop-gain variants (FCGR1A.pR92X, and LILRB1.pY331X) in novel AD genes and 1 non-synonymous variant (ATP8B4.pG395S). Remarkably, PRS for the 36 additional variants in these four genes also showed significant association with AD. The PRS for all 40 variants in the 4 genes, showed significant, replicable association with AD and 3 additional variants in this polygenic component had significant false discovery rates: ATP8B4.pR1059Q, LILRB1.pP7P, and LILRB1.pY327Y. Conclusions: Here, we identify 3 immune pathway genes (ATP8B4, LILRB1, and FCGR1A) with a variant that associates with AD. Like TREM2.pR47H, each of the variants has a minor allele frequency less than 1% and is a deleterious, protein altering variant with a strong effect that increases or decreases (LILRB1.pY331X) risk of AD. Additional variants in these genes also alter risk of AD. The variants identified here are ideally suited for studies aimed at understanding how the innate immune system may be modulated to alter risk of AD.
Published: 2022
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20. Testing and estimation of X‐chromosome SNP effects: Impact of model assumptions

Author: Yilin Song, Stacey J. Winham, and Joanna M. Biernacka
Subjects: Male, bias, Epidemiology, Single-nucleotide polymorphism, Polymorphism, Single Nucleotide, X-inactivation, 03 medical and health sciences, model assumptions, X Chromosome Inactivation, Genetic model, Statistics, Humans, SNP, SNP coefficient, Research Articles, Genetics (clinical), X chromosome, 030304 developmental biology, Mathematics, Estimation, Chromosomes, Human, X, 0303 health sciences, X chromosome variants, Models, Genetic, 030305 genetics & heredity, Female, sex coefficient, Research Article
Abstract: Interest in analyzing X chromosome single nucleotide polymorphisms (SNPs) is growing and several approaches have been proposed. Prior studies have compared power of different approaches, but bias and interpretation of coefficients have received less attention. We performed simulations to demonstrate the impact of X chromosome model assumptions on effect estimates. We investigated the coefficient biases of SNP and sex effects with commonly used models for X chromosome SNPs, including models with and without assumptions of X chromosome inactivation (XCI), and with and without SNP–sex interaction terms. Sex and SNP coefficient biases were observed when assumptions made about XCI and sex differences in SNP effect in the analysis model were inconsistent with the data‐generating model. However, including a SNP–sex interaction term often eliminated these biases. To illustrate these findings, estimates under different genetic model assumptions are compared and interpreted in a real data example. Models to analyze X chromosome SNPs make assumptions beyond those made in autosomal variant analysis. Assumptions made about X chromosome SNP effects should be stated clearly when reporting and interpreting X chromosome associations. Fitting models with SNP × Sex interaction terms can avoid reliance on assumptions, eliminating coefficient bias even in the absence of sex differences in SNP effect.
Published: 2021
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21. Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings

Author: Ravishankar K. Iyer, Tanja Brückl, Joanna M. Biernacka, Madhukar H. Trivedi, Rickey E. Carter, Mark A. Frye, Michelle K. Skime, Richard M. Weinshilboum, Taryn L. Mayes, A. John Rush, Elisabeth B. Binder, Drew Neavin, Paul E. Croarkin, Arjun P. Athreya, William V. Bobo, Liewei Wang, and Ditlev Monrad
Subjects: medicine.medical_specialty, MEDLINE, Article, 03 medical and health sciences, 0302 clinical medicine, Delusion, Internal medicine, medicine, Humans, Graphical model, Prospective Studies, Prospective cohort study, Depression (differential diagnoses), Pharmacology, Depressive Disorder, Major, business.industry, Depression, Translational research, medicine.disease, Antidepressive Agents, 030227 psychiatry, Psychiatry and Mental health, Pharmaceutical Preparations, Antidepressant, Major depressive disorder, Anxiety, medicine.symptom, business, 030217 neurology & neurosurgery, Selective Serotonin Reuptake Inhibitors
Abstract: Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.
Published: 2021

22. Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach - CORRIGENDUM

Author: Micah, Cearns, Azmeraw T, Amare, Klaus Oliver, Schubert, Anbupalam, Thalamuthu, Joseph, Frank, Fabian, Streit, Mazda, Adli, Nirmala, Akula, Kazufumi, Akiyama, Raffaella, Ardau, Bárbara, Arias, JeanMichel, Aubry, Lena, Backlund, Abesh Kumar, Bhattacharjee, Frank, Bellivier, Antonio, Benabarre, Susanne, Bengesser, Joanna M, Biernacka, Armin, Birner, Clara, Brichant-Petitjean, Pablo, Cervantes, HsiChung, Chen, Caterina, Chillotti, Sven, Cichon, Cristiana, Cruceanu, Piotr M, Czerski, Nina, Dalkner, Alexandre, Dayer, Franziska, Degenhardt, Maria Del, Zompo, J Raymond, DePaulo, Bruno, Étain, Peter, Falkai, Andreas J, Forstner, Louise, Frisen, Mark A, Frye, Janice M, Fullerton, Sébastien, Gard, Julie S, Garnham, Fernando S, Goes, Maria, Grigoroiu-Serbanescu, Paul, Grof, Ryota, Hashimoto, Joanna, Hauser, Urs, Heilbronner, Stefan, Herms, Per, Hoffmann, Andrea, Hofmann, Liping, Hou, Yi-Hsiang, Hsu, Stephane, Jamain, Esther, Jiménez, Jean-Pierre, Kahn, Layla, Kassem, Po-Hsiu, Kuo, Tadafumi, Kato, John, Kelsoe, Sarah, Kittel-Schneider, Sebastian, Kliwicki, Barbara, König, Ichiro, Kusumi, Gonzalo, Laje, Mikael, Landén, Catharina, Lavebratt, Marion, Leboyer, Susan G, Leckband, Mario, Maj, Mirko, Manchia, Lina, Martinsson, Michael J, McCarthy, Susan, McElroy, Francesc, Colom, Marina, Mitjans, Francis M, Mondimore, Palmiero, Monteleone, Caroline M, Nievergelt, Markus M, Nöthen, Tomas, Novák, Claire, O'Donovan, Norio, Ozaki, Vincent, Millischer, Sergi, Papiol, Andrea, Pfennig, Claudia, Pisanu, James B, Potash, Andreas, Reif, Eva, Reininghaus, Guy A, Rouleau, Janusz K, Rybakowski, Martin, Schalling, Peter R, Schofield, Barbara W, Schweizer, Giovanni, Severino, Tatyana, Shekhtman, Paul D, Shilling, Katzutaka, Shimoda, Christian, Simhandl, Claire M, Slaney, Alessio, Squassina, Thomas, Stamm, Pavla, Stopkova, Fasil, TekolaAyele, Alfonso, Tortorella, Gustavo, Turecki, Julia, Veeh, Eduard, Vieta, Stephanie H, Witt, Gloria, Roberts, Peter P, Zandi, Martin, Alda, Michael, Bauer, Francis J, McMahon, Philip B, Mitchell, Thomas G, Schulze, Marcella, Rietschel, Scott R, Clark, and Bernhard T, Baune
Subjects: Machine Learning, Psychiatry and Mental health, Depressive Disorder, Major, Depressive Disorder, Treatment-Resistant, Bipolar Disorder, Humans, Lithium
Published: 2022

23. Identifying the Common Genetic Basis of Antidepressant Response

Author: Sara A. Paciga, Richard M. Weinshilboum, Andrew M. McIntosh, Tim B. Bigdeli, Stephanie H. Witt, Sven Cichon, Glyn Lewis, Henning Teismann, Brenda W.J.H. Penninx, Gerome Breen, Roseann E. Peterson, Saira Saeed Mirza, Diego Albani, Lisa Jones, Andreas J. Forstner, Sara Mostafavi, Julien Bryois, Qingqin S. Li, Kenneth S. Kendler, Thomas Damm Als, Fernando S. Goes, Marie Bækvad-Hansen, Nancy L. Pedersen, Gianluigi Forloni, Per Qvist, Carsten Horn, Per Hoffmann, Steven P. Hamilton, Georg Homuth, Michael Gill, Julien Mendlewicz, Katharina Domschke, Volker Arolt, Adrian I. Campos, Christine Søholm Hansen, Scott D. Gordon, Hogni Oskarsson, Peter McGuffin, Oliver Pain, Eric Jorgenson, Victoria S. Marshe, Stacy Steinberg, Bertram Müller-Myhsok, Mark Adams, J. Raymond DePaulo, Rick Jansen, Katherine J. Aitchison, Vassily Trubetskoy, Henry Völzke, Manuel Mattheisen, Bernard Ng, James A. Knowles, Dorret I. Boomsma, Tracy Air, Elisabeth B. Binder, Ian B. Hickie, Christel M. Middeldorp, Tõnu Esko, David M. Hougaard, E.J.C. de Geus, Toni-Kim Clarke, Helena Gaspar, Bernhard T. Baune, Abdel Abdellaoui, Engilbert Sigurdsson, Andres Metspalu, Klaus Berger, Jorge A. Quiroz, Patrick F. Sullivan, Aartjan T.F. Beekman, Thomas Hansen, Panagiotis Ferentinos, Jürgen Wellmann, Miguel E. Rentería, Daniel Umbricht, Marcella Rietschel, Stanley I. Shyn, Chiara Fabbri, Hreinn Stefansson, Jerome C. Foo, Daniel Souery, Zoltán Kutalik, Yu-Li Liu, Paul F. O'Reilly, Michael John Owen, Nese Direk, Douglas F. Levinson, Stuart Montgomery, Hamdi Mbarek, David M. Howard, Guido Bondolfi, Lucía Colodro-Conde, Pippa A. Thomson, Merete Nordentoft, Stefan Kloiber, Yunpeng Wang, Michael Conlon O'Donovan, Grant C.B. Sinnamon, Alexander Viktorin, Hilary K. Finucane, Esben Agerbo, Stefan Herms, Markus M. Nöthen, Till F. M. Andlauer, Divya Mehta, Bradley T. Webb, Joanna M. Biernacka, David J. Porteous, Jordan W. Smoller, Jonathan R. I. Coleman, Dean F. MacKinnon, Farnush Farhadi Hassan Kiadeh, Baptiste Couvy-Duchesne, Evelin Mihailov, Eleanor M. Wigmore, Franziska Degenhardt, Jianxin Shi, Dale R. Nyholt, Enda M. Byrne, Stephan Ripke, Ole Mors, Patrik K. E. Magnusson, Eric J. Lenze, Warren W. Kretzschmar, Masaki Kato, Marcus Ising, Ian Jones, Lynsey S. Hall, Wouter J. Peyrot, Ling Shen, Nader Perroud, Na Cai, Maciej Trzaskowski, Matthias Nauck, Isaac S. Kohane, Enrico Domenici, Fabian Streit, James L. Kennedy, Peter M. Visscher, Valentina Escott-Price, Donald J. MacIntyre, Enrique Castelao, Margarita Rivera, Mojca Z. Dernovsek, John P. Rice, Joseph Zohar, Gail Davies, Andrew C. Heath, Josef Frank, Wesley K. Thompson, Caroline Hayward, Penelope A. Lind, Thorgeir E. Thorgeirsson, Rudolf Uher, Jana Strohmaier, Henriette N. Buttenschøn, Erin C. Dunn, Jonas Bybjerg-Grauholm, Alexander Teumer, Jakob Grove, Eske M. Derks, Nicholas G. Martin, Jodie N. Painter, Myrna M. Weissman, Preben Bo Mortensen, Michel G. Nivard, Catherine Schaefer, Yihan Li, Daniel J. Smith, Shih-Jen Tsai, Niamh Mullins, Jian Yang, Marianne Giørtz Pedersen, Dan Rujescu, Thomas G. Schulze, Lili Milani, Yuri Milaneschi, Giorgio Pistis, James B. Potash, Neven Henigsberg, Nicholas John Craddock, Karen Hodgson, Silviu-Alin Bacanu, Shantel Weinsheimer, Charles F. Reynolds, Johannes H. Smit, Gonneke Willemsen, Futao Zhang, Henning Tiemeier, Grant W. Montgomery, Martin Preisig, Udo Dannlowski, Thalia C. Eley, Thomas Werge, Katherine E. Tansey, Jane H. Christensen, Julia Kraft, Ian J. Deary, Cathryn M. Lewis, Sarah E. Medland, André G. Uitterlinden, Daniel J. Müller, Carsten Bøcker Pedersen, Gustavo Turecki, Hans J. Grabe, Matthew Traylor, Brien P. Riley, Roy H. Perlis, Patrick J. McGrath, Conor V. Dolan, Hualin S. Xi, Jonathan Marchini, Robert A. Schoevers, Albert M. van Hemert, Anders D. Børglum, Susanne Lucae, Jouke-Jan Hottenga, Kari Stefansson, Benoit H. Mulsant, Francis M. Mondimore, Naomi R. Wray, Yang Wu, Wolfgang Maier, Danielle Posthuma, Annamaria Cattaneo, Gregory E. Crawford, Siegfried Kasper, Alessandro Serretti, Tania Carrillo-Roa, Robert Maier, Pamela A. F. Madden, Eva C. Schulte, Jens Treutlein, Joanna Hauser, Sandra Van der Auwera, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Complex Trait Genetics, APH - Methodology, Human genetics, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Reproduction & Development (AR&D), Adult Psychiatry, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Epidemiology, Child and Adolescent Psychiatry / Psychology, and Internal Medicine
Subjects: Oncology, MDD, medicine.medical_specialty, Single-nucleotide polymorphism, Genome-wide association study, Biology, Antidepressant response, Depression, GWAS, Genetics, Polygenic score, SDG 3 - Good Health and Well-being, Internal medicine, Genetic variation, medicine, ddc:610, Genetic association, General Medicine, Heritability, medicine.disease, Schizophrenia, Sample size determination, Settore BIO/14 - Farmacologia, Major depressive disorder
Abstract: Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.Methods: Genome-wide analysis of remission (nremit = 1852, nnonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism–based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism–based heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = −0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.
Published: 2022
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24. ERICH3: vesicular association and antidepressant treatment response

Author: Richard M. Weinshilboum, Thanh Thanh L. Nguyen, W. Edward Craighead, Rima Kaddurah-Daouk, Duan Liu, Mark A. Frye, Liewei Wang, Yani Wang, Yongxian Zhuang, Drew Neavin, Lingxin Zhang, Helen S. Mayberg, Sisi Qin, Huanyao Gao, Elisabeth B. Binder, Daniel C. Kim, Boadie W. Dunlop, Jia Yu, Erica Liu, Joanna M. Biernacka, and Tania Carrillo-Roa
Subjects: 0301 basic medicine, Serotonin, Cell type, Pharmacology, Serotonergic, Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Dopamine, Genetics, Humans, Medicine, Molecular Biology, Depressive Disorder, Major, business.industry, Colocalization, medicine.disease, Antidepressive Agents, Psychiatry and Mental health, 030104 developmental biology, Major depressive disorder, Antidepressant, business, Reuptake inhibitor, Selective Serotonin Reuptake Inhibitors, Biomarkers, 030217 neurology & neurosurgery, Genome-Wide Association Study, medicine.drug
Abstract: Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.
Published: 2020
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25. Dissecting clinical heterogeneity of bipolar disorder using multiple polygenic risk scores

Author: Brandon J. Coombes, Joanna M. Biernacka, J. John Mann, Colin L. Colby, Mark A. Frye, Matej Markota, Myrna M. Weissman, Jyotishman Pathak, Susan L. McElroy, Eli A. Stahl, and Ardesheer Talati
Subjects: Male, Multifactorial Inheritance, medicine.medical_specialty, Psychosis, Bipolar Disorder, media_common.quotation_subject, Suicide, Attempted, Article, lcsh:RC321-571, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Risk Factors, Internal medicine, medicine, Humans, Personality, Bipolar disorder, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Biological Psychiatry, 030304 developmental biology, Genetic association, media_common, 0303 health sciences, Suicide attempt, Genetic heterogeneity, business.industry, Comparative genomics, Anhedonia, medicine.disease, 3. Good health, Psychiatry and Mental health, Psychotic Disorders, Female, Polygenic risk score, medicine.symptom, business, 030217 neurology & neurosurgery
Abstract: Bipolar disorder (BD) has high clinical heterogeneity, frequent psychiatric comorbidities, and elevated suicide risk. To determine genetic differences between common clinical sub-phenotypes of BD, we performed a systematic polygenic risk score (PRS) analysis using multiple PRSs from a range of psychiatric, personality, and lifestyle traits to dissect differences in BD sub-phenotypes in two BD cohorts: the Mayo Clinic BD Biobank (N = 968) and Genetic Association Information Network (N = 1001). Participants were assessed for history of psychosis, early-onset BD, rapid cycling (defined as four or more episodes in a year), and suicide attempts using questionnaires and the Structured Clinical Interview for DSM-IV. In a combined sample of 1969 bipolar cases (45.5% male), those with psychosis had higher PRS for SCZ (OR = 1.3 per S.D.; p = 3e-5) but lower PRSs for anhedonia (OR = 0.87; p = 0.003) and BMI (OR = 0.87; p = 0.003). Rapid cycling cases had higher PRS for ADHD (OR = 1.23; p = 7e-5) and MDD (OR = 1.23; p = 4e-5) and lower BD PRS (OR = 0.8; p = 0.004). Cases with a suicide attempt had higher PRS for MDD (OR = 1.26; p = 1e-6) and anhedonia (OR = 1.22; p = 2e-5) as well as lower PRS for educational attainment (OR = 0.87; p = 0.003). The observed novel PRS associations with sub-phenotypes align with clinical observations such as rapid cycling BD patients having a greater lifetime prevalence of ADHD. Our findings confirm that genetic heterogeneity contributes to clinical heterogeneity of BD and consideration of genetic contribution to psychopathologic components of psychiatric disorders may improve genetic prediction of complex psychiatric disorders.
Published: 2020
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26. Mood‐Stabilizing Antiepileptic Treatment Response in Bipolar Disorder: A Genome‐Wide Association Study

Author: Balwinder Singh, Duan Liu, Joanna M. Biernacka, Mark A. Frye, Colin L. Colby, Malik Nassan, Beth R. Larrabee, Ada Man Choi Ho, Brandon J. Coombes, Susan L. McElroy, Thanh Thanh L. Nguyen, and Richard M. Weinshilboum
Subjects: Oncology, Divalproex, Adult, Male, medicine.medical_specialty, Bipolar Disorder, Pharmacogenomic Variants, medicine.medical_treatment, Quantitative Trait Loci, Oxcarbazepine, Single-nucleotide polymorphism, Genome-wide association study, Lamotrigine, Polymorphism, Single Nucleotide, Article, Antimanic Agents, Internal medicine, medicine, SNP, Humans, Pharmacology (medical), Bipolar disorder, Retrospective Studies, Pharmacology, business.industry, Research, Valproic Acid, Articles, Middle Aged, medicine.disease, Affect, Anticonvulsant, Treatment Outcome, Gastrointestinal Absorption, Pharmacogenetics, Pharmacogenomics, Anticonvulsants, Female, Multidrug Resistance-Associated Proteins, business, Thrombospondins, medicine.drug, Genome-Wide Association Study
Abstract: Several antiepileptic drugs (AEDs) have US Food and Drug Administration (FDA) approval for use as mood stabilizers in bipolar disorder (BD), but not all BD patients respond to these AED mood stabilizers (AED-MSs). To identify genetic polymorphisms that contribute to the variability in AED-MS response, we performed a discovery genome-wide association study (GWAS) of 199 BD patients from the Mayo Clinic Bipolar Disorder Biobank. Most of these patients had been treated with the AED-MS valproate/divalproex and/or lamotrigine. AED-MS response was assessed using the Alda scale, which quantifies clinical improvement while accounting for potential confounding factors. We identified two genome-wide significant single-nucleotide polymorphism (SNP) signals that mapped to the THSD7A (rs78835388, P = 7.1E-09) and SLC35F3 (rs114872993, P = 3.2E-08) genes. We also identified two genes with statistically significant gene-level associations: ABCC1 (P = 6.7E-07; top SNP rs875740, P = 2.0E-6), and DISP1 (P = 8.9E-07; top SNP rs34701716, P = 8.9E-07). THSD7A SNPs were previously found to be associated with risk for several psychiatric disorders, including BD. Both THSD7A and SLC35F3 are expressed in excitatory/glutamatergic and inhibitory/γ-aminobutyric acidergic (GABAergic) neurons, which are targets of AED-MSs. ABCC1 is involved in the transport of valproate and lamotrigine metabolites, and the SNPs in ABCC1 and DISP1 with the strongest evidence of association in our GWAS are strong splicing quantitative trait loci in the human gut, suggesting a possible influence on drug absorption. In conclusion, our pharmacogenomic study identified novel genetic loci that appear to contribute to AED-MS treatment response, and may facilitate precision medicine in BD.
Published: 2020

27. Inferring multimodal latent topics from electronic health records

Author: Jose Davila-Velderrain, Weiqi Liu, Liming Guo, Euijung Ryu, Zhi Wen, Yan Miao, Manolis Kellis, Janet E. Olson, Mark A. Frye, Yue Li, Ariane Marelli, Yuening Wang, Aihua Liu, Yuri Ahuja, Xing Han Lu, Tamas Ordog, Pratheeksha Nair, Joanna M. Biernacka, and Amir Ardalan Kalantari Dehaghi
Subjects: Topic model, Databases, Factual, Computer science, Science, MEDLINE, General Physics and Astronomy, 02 engineering and technology, Health informatics, Data type, General Biochemistry, Genetics and Molecular Biology, Article, Machine Learning, Bayes' theorem, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), Electronic Health Records, Humans, lcsh:Science, Multidisciplinary, Models, Statistical, business.industry, Computational science, Health care, Bayes Theorem, General Chemistry, Precision medicine, Data science, 3. Good health, Phenotype, ComputingMilieux_COMPUTERSANDSOCIETY, 020201 artificial intelligence & image processing, lcsh:Q, Diagnosis code, business, Medical Informatics
Abstract: Electronic health records (EHR) are rich heterogeneous collections of patient health information, whose broad adoption provides clinicians and researchers unprecedented opportunities for health informatics, disease-risk prediction, actionable clinical recommendations, and precision medicine. However, EHRs present several modeling challenges, including highly sparse data matrices, noisy irregular clinical notes, arbitrary biases in billing code assignment, diagnosis-driven lab tests, and heterogeneous data types. To address these challenges, we present MixEHR, a multi-view Bayesian topic model. We demonstrate MixEHR on MIMIC-III, Mayo Clinic Bipolar Disorder, and Quebec Congenital Heart Disease EHR datasets. Qualitatively, MixEHR disease topics reveal meaningful combinations of clinical features across heterogeneous data types. Quantitatively, we observe superior prediction accuracy of diagnostic codes and lab test imputations compared to the state-of-art methods. We leverage the inferred patient topic mixtures to classify target diseases and predict mortality of patients in critical conditions. In all comparison, MixEHR confers competitive performance and reveals meaningful disease-related topics., Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.
Published: 2020
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28. Quantification of diet quality utilizing the rapid eating assessment for participants-shortened version in bipolar disorder: Implications for prospective depression and cardiometabolic studies

Author: Manuel Gardea-Resendez, Stacey J. Winham, Francisco Romo-Nava, Alfredo Cuellar-Barboza, Matthew M. Clark, Ana Cristina Andreazza, Alejandra Cabello-Arreola, Marin Veldic, David J. Bond, Balwinder Singh, Miguel L. Prieto, Nicolas A. Nunez, Hannah Betcher, Katherine M. Moore, Thomas Blom, Colin Colby, Richard S. Pendegraft, Sydney S. Kelpin, Aysegul Ozerdem, Alessandro Miola, Eleanna De Filippis, Joanna M. Biernacka, Susan L. McElroy, and Mark A. Frye
Subjects: Male, Psychiatry and Mental health, Clinical Psychology, Bipolar Disorder, Cross-Sectional Studies, Cardiovascular Diseases, Depression, Humans, Female, Prospective Studies, Article, Diet
Abstract: OBJECTIVES: Recognizing bipolar disorder as a multi-system metabolic condition driven, in part, by binge eating behavior and atypical depressive symptoms, this study aimed to quantify diet quality and evaluate clinical correlates in a bipolar disorder cohort. METHODS: Participants from the Mayo Clinic Bipolar Disorder Biobank (n = 734) completed the Rapid Eating Assessment for Participants – Shortened version (REAP-S) to determine diet quality. The average REAP-S score for a U.S. omnivorous diet is 32 (range 13 to 39) with higher scores indicating healthier diet. Demographic variables were collected in a standardized clinical questionnaire. Depressive symptoms were assessed by the Bipolar Inventory of Symptoms Scale. Cardiometabolic variables were retrieved from the electronic health record. Associations between continuous variables and REAP-S scores (total, ‘healthy foods’ and ‘avoidance of unhealthy foods’) were assessed using linear regression. RESULTS: Overall, our sample had a mean REAP-S score of 27.6 (4.9), suggestive of a lower diet quality than the average general population in the US. There was a significant inverse relationship between mean REAP-S lower scores with increased BMI, waist circumference, disordered eating and depression. All these associations were significantly stronger in female participants. LIMITATIONS: EHR cross-sectional data. CONCLUSIONS: Our data suggest unhealthy diet quality in bipolar disorder is associated with depression, obesity and cardiometabolic abnormalities. Additional work is encouraged to prospectively track mood and diet quality to further understand the bidirectional relationship and clarify if dietary interventions can positively impact mood. Further delineating potential sex differences in diet quality and depression may provide greater appreciation of modifiable risk factors for future cardiometabolic burden.
Published: 2022

29. Predictors of functional impairment in bipolar disorder: Results from 13 cohorts from seven countries by the global bipolar cohort collaborative

Author: Katherine E. Burdick, Caitlin E. Millett, Anastasia K. Yocum, Cara M. Altimus, Ole A. Andreassen, Valerie Aubin, Raoul Belzeaux, Michael Berk, Joanna M. Biernacka, Hilary P. Blumberg, Anthony J. Cleare, Claudia Diaz‐Byrd, Caroline Dubertret, Bruno Etain, Lisa T. Eyler, Brent P. Forester, Janice M. Fullerton, Mark A. Frye, Sébastien Gard, Ophelia Godin, Emmanuel Haffen, Federica Klaus, Trine Vik Lagerberg, Marion Leboyer, Anabel Martinez‐Aran, Susan McElroy, Philip B. Mitchell, Emilie Olie, Phebe Olorunfemi, Christine Passerieux, Amy T. Peters, Daniel L. Pham, Mircea Polosan, Julia R. Potter, Martha Sajatovic, Ludovic Samalin, Raymund Schwan, Megan Shanahan, Brisa Solé, Rebecca Strawbridge, Amanda L. Stuart, Ivan Torres, Torrill Ueland, Eduard Vieta, Lana J. Williams, Anna L. Wrobel, Lakshmi N. Yatham, Allan H. Young, Andrew A. Nierenberg, Melvin G. McInnis, Centre Hospitalier Princesse Grace, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Optimisation thérapeutique en Neuropsychopharmacologie (OPTeN (UMR_S_1144 / U1144)), IMRB - 'Neuropsychiatrie translationnelle' [Créteil] (U955 Inserm - UPEC), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), CHU Henri Mondor, CHU Montpellier, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA), Université de Lorraine (UL), Harvard Medical School [Boston] (HMS), University of Michigan [Ann Arbor], University of Michigan System, Milken Institute School of Public Health, The George Washington University (GW), Norwegian Centre for Mental Disorders Research [Oslo] (NORMENT), University of Oslo (UiO)-Haukeland University Hospital, University of Bergen (UiB)-University of Bergen (UiB)-Oslo University Hospital [Oslo], Assistance Publique - Hôpitaux de Marseille (APHM), Deakin University [Burwood], Orygen Youth Health Research Centre [Melbourne], University of Melbourne, Mayo Clinic [Rochester], Yale School of Medicine [New Haven, Connecticut] (YSM), King‘s College London, Hôpital Louis Mourier - AP-HP [Colombes], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Hôpital Lariboisière-Fernand-Widal [APHP], University of California [San Diego] (UC San Diego), University of California (UC), McLean Hospital [Belmont, Ma.], University of New South Wales [Sydney] (UNSW), Centre hospitalier Charles Perrens [Bordeaux], Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Fondation FondaMental [Créteil], Université Bourgogne Franche-Comté [COMUE] (UBFC), Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM), University of Cincinnati College of Medicine, Institut de Génomique Fonctionnelle (IGF), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Massachusetts General Hospital [Boston], Centre Hospitalier de Versailles André Mignot (CHV), Centre Hospitalier Universitaire [Grenoble] (CHU), Case Western Reserve University [Cleveland], CHU Clermont-Ferrand, University of British Columbia (UBC), University of Oslo (UiO), IMPACT Strategic Research Centre [VIC, Australia] (Barwon Health), Guerineau, Nathalie C., CHU Henri Mondor [Créteil], and Hôpital Charles Perrens
Subjects: Bipolar Disorder, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, [SDV]Life Sciences [q-bio], Cohort Studies, [SDV] Life Sciences [q-bio], Psychiatry and Mental health, Affect, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Prospective Studies, Longitudinal Studies, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Biological Psychiatry, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: International audience; Objectives: Persistent functional impairment is common in bipolar disorder (BD) and is influenced by a number of demographic, clinical, and cognitive features. The goal of this project was to estimate and compare the influence of key factors on community function in multiple cohorts of well-characterized samples of individuals with BD.Methods: Thirteen cohorts from 7 countries included n=5,882 individuals with BD across multiple sites. The statistical approach consisted of a systematic uniform application of analyses across sites. Each site performed a logistic regression analysis with empirically derived "higher versus lower function" as the dependent variable and selected clinical and demographic variables as predictors.Results: We found high rates of functional impairment, ranging from 41-75%. Lower community functioning was associated with depressive symptoms in 10 of 12 of the cohorts that included this variable in the analysis. Lower levels of education, a greater number of prior mood episodes, presence of a comorbid substance use disorder, and a greater total number of psychotropic medications were also associated with low functioning.Conclusions: The bipolar clinical research community is poised to work together to characterize the multi-dimensional contributors to impairment and address the barriers that impede patients' complete recovery. We must also identify the core features which enable many to thrive and live successfully with BD. A large-scale, worldwide, prospective longitudinal study focused squarely on BD and its heterogeneous presentations will serve as a platform for discovery and promote major advances toward optimizing outcomes for every individual with this illness.
Published: 2022
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30. Clinical and Genetic Correlates of Bipolar Disorder With Childhood-Onset Attention Deficit Disorder

Author: Nicolas A. Nunez, Brandon J. Coombes, Francisco Romo-Nava, David J. Bond, Jennifer Vande Voort, Paul E. Croarkin, Nicole Leibman, Manuel Gardea Resendez, Marin Veldic, Hannah Betcher, Balwinder Singh, Colin Colby, Alfredo Cuellar-Barboza, Miguel Prieto, Katherine M. Moore, Aysegul Ozerdem, Susan L. McElroy, Mark A. Frye, and Joanna M. Biernacka
Subjects: Psychiatry and Mental health, mental disorders
Abstract: Background:Bipolar disorder (BD) with co-occurring attention deficit-hyperactivity disorder (ADHD) is associated with an unfavorable course of illness. We aimed to identify potential clinical and genetic correlates of BD with and without ADHD.MethodsAmong patients with BD (N = 2,198) enrolled in the Mayo Clinic Bipolar Biobank we identified those with ADHD diagnosed in childhood (BD+cADHD; N = 350), those with adult-onset attention deficit symptoms (BD+aAD; N = 254), and those without ADHD (N = 1,594). We compared the groups using linear or logistic regression adjusting for age, sex, and recruitment site. For genotyped patients (N = 1,443), logistic regression was used to compare ADHD and BD polygenic risk scores (PRSs) between the BD groups, as well as to non-BD controls (N = 777).ResultsCompared to the non-ADHD BD group, BD+cADHD patients were younger, more often men and had a greater number of co-occurring anxiety and substance use disorders (all p < 0.001). Additionally, BD+cADHD patients had poorer responses to lithium and lamotrigine (p = 0.005 and p = 0.007, respectively). In PRS analyses, all BD patient subsets had greater genetic risk for BD and ADHD when compared to non-BD controls (p < 0.001 in all comparisons). BD+cADHD patients had a higher ADHD-PRS than non-ADHD BD patients (p = 0.012). However, BD+aAD patients showed no evidence of higher ADHD-PRS than non-ADHD BD patients (p = 0.38).ConclusionsBD+cADHD was associated with a greater number of comorbidities and reduced response to mood stabilizing treatments. The higher ADHD PRS for the BD+cADHD group may reflect a greater influence of genetic factors on early presentation of ADHD symptoms.
Published: 2022

31. Social connectedness as a determinant of mental health: A scoping review

Author: Priya J. Wickramaratne, Tenzin Yangchen, Lauren Lepow, Braja G. Patra, Benjamin Glicksburg, Ardesheer Talati, Prakash Adekkanattu, Euijung Ryu, Joanna M. Biernacka, Alexander Charney, J. John Mann, Jyotishman Pathak, Mark Olfson, and Myrna M. Weissman
Subjects: Adult, Depressive Disorder, Major, Mental Health, Multidisciplinary, Depression, Pregnancy, Loneliness, Humans, Social Support, Female, Anxiety Disorders
Abstract: Public health and epidemiologic research have established that social connectedness promotes overall health. Yet there have been no recent reviews of findings from research examining social connectedness as a determinant of mental health. The goal of this review was to evaluate recent longitudinal research probing the effects of social connectedness on depression and anxiety symptoms and diagnoses in the general population. A scoping review was performed of PubMed and PsychInfo databases from January 2015 to December 2020 following PRISMA-ScR guidelines using a defined search strategy. The search yielded 56 articles representing 52 unique studies. In research with other than pregnant women, 84% (16 of 19) studies reported that social support benefited symptoms of depression with the remaining 16% (3 of 19) reporting minimal or no evidence that lower levels of social support predict depression at follow-up. In research with pregnant women, 80% (21 of 26 studies) found that low social support increased postpartum depressive symptoms. Among 3 of 4 studies that focused on loneliness, feeling lonely at baseline was related to adverse outcomes at follow-up including higher risks of major depressive disorder, depressive symptom severity, generalized anxiety disorder, and lower levels of physical activity. In 5 of 7 reports, smaller social network size predicted depressive symptoms or disorder at follow-up. In summary, most recent relevant longitudinal studies have demonstrated that social support protects adults in the general population from depressive symptoms and disorders. The results, which were largely consistent across settings, exposure measures, and populations, support efforts to improve clinical detection of high-risk patients, including adults with low social support and elevated loneliness.
Published: 2022
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32. Augmentation strategies for treatment resistant major depression: A systematic review and network meta-analysis(☆)

Author: Nicolas A Nuñez, Boney Joseph, Mehak Pahwa, Rakesh Kumar, Manuel Gardea Resendez, Larry J Prokop, Marin Veldic, Ashok Seshadri, Joanna M Biernacka, Mark A Frye, Zhen Wang, and Balwinder Singh
Subjects: Adult, Psychiatry and Mental health, Clinical Psychology, Depressive Disorder, Major, Depressive Disorder, Treatment-Resistant, Depression, Network Meta-Analysis, Humans, Article, Antidepressive Agents, Antipsychotic Agents
Abstract: OBJECTIVE: To compare the efficacy and discontinuation of augmentation agents in adult patients with treatment-resistant depression (TRD). We conducted a systematic review and network meta-analyses (NMA) to combine direct and indirect comparisons of augmentation agents. METHODS: We included randomized controlled trials comparing one active drug with another or with placebo following a treatment course up to 24 weeks. Nineteen agents were included: stimulants, atypical antipsychotics, thyroid hormones, antidepressants, and mood stabilizers. Data for response/remission and all-cause discontinuation rates were analyzed. We estimated effect-size by relative risk using pairwise and NMA with random-effects model. RESULTS: A total of 65 studies (N = 12,415) with 19 augmentation agents were included in the NMA. Our findings from the NMA for response rates, compared to placebo, were significant for: liothyronine, nortriptyline, aripiprazole, brexpiprazole, quetiapine, lithium, modafinil, olanzapine (fluoxetine), cariprazine, and lisdexamfetamine. For remission rates, compared to placebo, were significant for: thyroid hormone(T4), aripiprazole, brexpiprazole, risperidone, quetiapine, and olanzapine (fluoxetine). Compared to placebo, ziprasidone, mirtazapine, and cariprazine had statistically significant higher discontinuation rates. Overall, 24% studies were rated as having low risk of bias (RoB), 63% had moderate RoB and 13% had high RoB. LIMITATIONS: Heterogeneity in TRD definitions, variable trial duration and methodological clinical design of older studies and small number of trials per comparisons. CONCLUSIONS: This NMA suggests a superiority of the regulatory approved adjunctive atypical antipsychotics, thyroid hormones, dopamine compounds (modafinil and lisdexamfetamine) and lithium. Acceptability was lower with ziprasidone, mirtazapine, and cariprazine. Further research and head-to-head studies should be considered to strengthen the best available options for TRD.
Published: 2022

33. Shared brain transcriptomic signature in TDP-43 type A FTLD patients with or without GRN mutations

Author: Cyril Pottier, Ligia Mateiu, Matthew C Baker, Mariely DeJesus-Hernandez, Cristina Teixeira Vicente, NiCole A Finch, Shulan Tian, Marka van Blitterswijk, Melissa E Murray, Yingxue Ren, Leonard Petrucelli, Björn Oskarsson, Joanna M Biernacka, Neill R Graff-Radford, Bradley F Boeve, Ronald C Petersen, Keith A Josephs, Yan W Asmann, Dennis W Dickson, and Rosa Rademakers
Subjects: Brain, nutritional and metabolic diseases, nervous system diseases, DNA-Binding Proteins, Progranulins, Frontotemporal Dementia, Mutation, mental disorders, Humans, Intercellular Signaling Peptides and Proteins, Neurology (clinical), Human medicine, Frontotemporal Lobar Degeneration, Transcriptome
Abstract: Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) is a complex heterogeneous neurodegenerative disorder for which mechanisms are poorly understood. To explore transcriptional changes underlying FTLD-TDP, we performed RNA-sequencing on 66 genetically unexplained FTLD-TDP patients, 24 FTLD-TDP patients with GRN mutations and 24 control participants. Using principal component analysis, hierarchical clustering, differential expression and coexpression network analyses, we showed that GRN mutation carriers and FTLD-TDP-A patients without a known mutation shared a common transcriptional signature that is independent of GRN loss-of-function. After combining both groups, differential expression as compared to the control group and coexpression analyses revealed alteration of processes related to immune response, synaptic transmission, RNA metabolism, angiogenesis and vesicle-mediated transport. Deconvolution of the data highlighted strong cellular alterations that were similar in FTLD-TDP-A and GRN mutation carriers with NSF as a potentially important player in both groups. We propose several potentially druggable pathways such as the GABAergic, GDNF and sphingolipid pathways. Our findings underline new disease mechanisms and strongly suggest that affected pathways in GRN mutation carriers extend beyond GRN and contribute to genetically unexplained forms of FTLD-TDP-A.
Published: 2022

34. Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders

Author: Gabriëlla A.M. Blokland, Jakob Grove, Chia-Yen Chen, Chris Cotsapas, Stuart Tobet, Robert Handa, David St Clair, Todd Lencz, Bryan J. Mowry, Sathish Periyasamy, Murray J. Cairns, Paul A. Tooney, Jing Qin Wu, Brian Kelly, George Kirov, Patrick F. Sullivan, Aiden Corvin, Brien P. Riley, Tõnu Esko, Lili Milani, Erik G. Jönsson, Aarno Palotie, Hannelore Ehrenreich, Martin Begemann, Agnes Steixner-Kumar, Pak C. Sham, Nakao Iwata, Daniel R. Weinberger, Pablo V. Gejman, Alan R. Sanders, Joseph D. Buxbaum, Dan Rujescu, Ina Giegling, Bettina Konte, Annette M. Hartmann, Elvira Bramon, Robin M. Murray, Michele T. Pato, Jimmy Lee, Ingrid Melle, Espen Molden, Roel A. Ophoff, Andrew McQuillin, Nicholas J. Bass, Rolf Adolfsson, Anil K. Malhotra, Nicholas G. Martin, Janice M. Fullerton, Philip B. Mitchell, Peter R. Schofield, Andreas J. Forstner, Franziska Degenhardt, Sabrina Schaupp, Ashley L. Comes, Manolis Kogevinas, José Guzman-Parra, Andreas Reif, Fabian Streit, Lea Sirignano, Sven Cichon, Maria Grigoroiu-Serbanescu, Joanna Hauser, Jolanta Lissowska, Fermin Mayoral, Bertram Müller-Myhsok, Beata Świątkowska, Thomas G. Schulze, Markus M. Nöthen, Marcella Rietschel, John Kelsoe, Marion Leboyer, Stéphane Jamain, Bruno Etain, Frank Bellivier, John B. Vincent, Martin Alda, Claire O’Donovan, Pablo Cervantes, Joanna M. Biernacka, Mark Frye, Susan L. McElroy, Laura J. Scott, Eli A. Stahl, Mikael Landén, Marian L. Hamshere, Olav B. Smeland, Srdjan Djurovic, Arne E. Vaaler, Ole A. Andreassen, Bernhard T. Baune, Tracy Air, Martin Preisig, Rudolf Uher, Douglas F. Levinson, Myrna M. Weissman, James B. Potash, Jianxin Shi, James A. Knowles, Roy H. Perlis, Susanne Lucae, Dorret I. Boomsma, Brenda W.J.H. Penninx, Jouke-Jan Hottenga, Eco J.C. de Geus, Gonneke Willemsen, Yuri Milaneschi, Henning Tiemeier, Hans J. Grabe, Alexander Teumer, Sandra Van der Auwera, Uwe Völker, Steven P. Hamilton, Patrik K.E. Magnusson, Alexander Viktorin, Divya Mehta, Niamh Mullins, Mark J. Adams, Gerome Breen, Andrew M. McIntosh, Cathryn M. Lewis, David M. Hougaard, Merete Nordentoft, Ole Mors, Preben B. Mortensen, Thomas Werge, Thomas D. Als, Anders D. Børglum, Tracey L. Petryshen, Jordan W. Smoller, Jill M. Goldstein, Stephan Ripke, Benjamin M. Neale, James T.R. Walters, Kai-How Farh, Peter A. Holmans, Phil Lee, Brendan Bulik-Sullivan, David A. Collier, Hailiang Huang, Tune H. Pers, Ingrid Agartz, Esben Agerbo, Margot Albus, Madeline Alexander, Farooq Amin, Silviu A. Bacanu, Richard A. Belliveau, Judit Bene, Sarah E. Bergen, Elizabeth Bevilacqua, Tim B. Bigdeli, Donald W. Black, Richard Bruggeman, Nancy G. Buccola, Randy L. Buckner, William Byerley, Wiepke Cahn, Guiqing Cai, Dominique Campion, Rita M. Cantor, Vaughan J. Carr, Noa Carrera, Stanley V. Catts, Kimberly D. Chambert, Raymond C.K. Chan, Ronald Y.L. Chen, Eric Y.H. Chen, Wei Cheng, Eric F.C. Cheung, Siow Ann Chong, C. Robert Cloninger, David Cohen, Nadine Cohen, Paul Cormican, Nick Craddock, James J. Crowley, David Curtis, Michael Davidson, Kenneth L. Davis, Jurgen Del Favero, Ditte Demontis, Dimitris Dikeos, Timothy Dinan, Gary Donohoe, Elodie Drapeau, Jubao Duan, Frank Dudbridge, Naser Durmishi, Peter Eichhammer, Johan Eriksson, Valentina Escott-Price, Laurent Essioux, Ayman H. Fanous, Martilias S. Farrell, Josef Frank, Lude Franke, Robert Freedman, Nelson B. Freimer, Marion Friedl, Joseph I. Friedman, Menachem Fromer, Giulio Genovese, Lyudmila Georgieva, Paola Giusti-Rodríguez, Stephanie Godard, Jacqueline I. Goldstein, Vera Golimbet, Srihari Gopal, Jacob Gratten, Lieuwe de Haan, Christian Hammer, Mark Hansen, Thomas Hansen, Vahram Haroutunian, Frans A. Henskens, Stefan Herms, Joel N. Hirschhorn, Per Hoffmann, Andrea Hofman, Mads V. Hollegaard, Masashi Ikeda, Inge Joa, Antonio Julià, René S. Kahn, Luba Kalaydjieva, Sena Karachanak-Yankova, Juha Karjalainen, David Kavanagh, Matthew C. Keller, James L. Kennedy, Andrey Khrunin, Yunjung Kim, Janis Klovins, Vaidutis Kucinskas, Zita Ausrele Kucinskiene, Hana Kuzelova-Ptackova, Anna K. Kähler, Claudine Laurent, Jimmy Lee Chee Keong, S. Hong Lee, Sophie E. Legge, Bernard Lerer, Miaoxin Li, Tao Li, Kung-Yee Liang, Jeffrey Lieberman, Svetlana Limborska, Carmel M. Loughland, Jan Lubinski, Jouko Lönnqvist, Milan Macek, Brion S. Maher, Wolfgang Maier, Jacques Mallet, Sara Marsal, Manuel Mattheisen, Morten Mattingsdal, Robert W. McCarley, Colm McDonald, Sandra Meier, Carin J. Meijer, Bela Melegh, Raquelle I. Mesholam-Gately, Andres Metspalu, Patricia T. Michie, Vihra Milanova, Younes Mokrab, Derek W. Morris, Kieran C. Murphy, Inez Myin-Germeys, Mari Nelis, Igor Nenadic, Deborah A. Nertney, Gerald Nestadt, Kristin K. Nicodemus, Liene Nikitina-Zake, Laura Nisenbaum, Annelie Nordin, Eadbhard O’Callaghan, Colm O’Dushlaine, F. Anthony O’Neill, Sang-Yun Oh, Ann Olincy, Line Olsen, Jim Van Os, Christos Pantelis, George N. Papadimitriou, Sergi Papiol, Elena Parkhomenko, Tiina Paunio, Milica Pejovic-Milovancevic, Diana O. Perkins, Olli Pietiläinen, Jonathan Pimm, Andrew J. Pocklington, John Powell, Alkes Price, Ann E. Pulver, Shaun M. Purcell, Digby Quested, Henrik B. Rasmussen, Abraham Reichenberg, Mark A. Reimers, Alexander L. Richards, Joshua L. Roffman, Panos Roussos, Douglas M. Ruderfer, Veikko Salomaa, Ulrich Schall, Christian R. Schubert, Sibylle G. Schwab, Edward M. Scolnick, Rodney J. Scott, Larry J. Seidman, Engilbert Sigurdsson, Teimuraz Silagadze, Jeremy M. Silverman, Kang Sim, Petr Slominsky, Hon-Cheong So, Chris C.A. Spencer, Hreinn Stefansson, Stacy Steinberg, Elisabeth Stogmann, Richard E. Straub, Eric Strengman, Jana Strohmaier, T. Scott Stroup, Mythily Subramaniam, Jaana Suvisaari, Dragan M. Svrakic, Jin P. Szatkiewicz, Erik Söderman, Srinivas Thirumalai, Draga Toncheva, Sarah Tosato, Juha Veijola, John Waddington, Dermot Walsh, Dai Wang, Qiang Wang, Bradley T. Webb, Mark Weiser, Dieter B. Wildenauer, Nigel M. Williams, Stephanie Williams, Stephanie H. Witt, Aaron R. Wolen, Emily H.M. Wong, Brandon K. Wormley, Hualin Simon Xi, Clement C. Zai, Xuebin Zheng, Fritz Zimprich, Naomi R. Wray, Kari Stefansson, Peter M. Visscher, Douglas H.R. Blackwood, Ariel Darvasi, Enrico Domenici, Michael Gill, Hugh Gurling, Christina M. Hultman, Assen V. Jablensky, Kenneth S. Kendler, Jo Knight, Qingqin S. Li, Jianjun Liu, Steven A. McCarroll, Jennifer L. Moran, Michael J. Owen, Carlos N. Pato, Danielle Posthuma, Pamela Sklar, Jens R. Wendland, Mark J. Daly, Michael C. O’Donovan, Peter Donnelly, Ines Barroso, Jenefer M. Blackwell, Matthew A. Brown, Juan P. Casas, Panos Deloukas, Audrey Duncanson, Janusz Jankowski, Hugh S. Markus, Christopher G. Mathew, Colin N.A. Palmer, Robert Plomin, Anna Rautanen, Stephen J. Sawcer, Richard C. Trembath, Ananth C. Viswanathan, Nicholas W. Wood, Gavin Band, Céline Bellenguez, Colin Freeman, Eleni Giannoulatou, Garrett Hellenthal, Richard Pearson, Matti Pirinen, Amy Strange, Zhan Su, Damjan Vukcevic, Cordelia Langford, Hannah Blackburn, Suzannah J. Bumpstead, Serge Dronov, Sarah Edkins, Matthew Gillman, Emma Gray, Rhian Gwilliam, Naomi Hammond, Sarah E. Hunt, Alagurevathi Jayakumar, Jennifer Liddle, Owen T. McCann, Simon C. Potter, Radhi Ravindrarajah, Michelle Ricketts, Avazeh Tashakkori-Ghanbaria, Matthew Waller, Paul Weston, Pamela Whittaker, Sara Widaa, Mark I. McCarthy, Maria J. Arranz, Steven Bakker, Stephan Bender, Benedicto Crespo-Facorro, Jeremy Hall, Conrad Iyegbe, Stephen Lawrie, Kuang Lin, Don H. Linszen, Ignacio Mata, Muriel Walshe, Matthias Weisbrod, Durk Wiersma, Vassily Trubetskoy, Yunpeng Wang, Jonathan R.I. Coleman, Héléna A. Gaspar, Christiaan A. de Leeuw, Jennifer M. Whitehead Pavlides, Maciej Trzaskowski, Enda M. Byrne, Liam Abbott, Huda Akil, Diego Albani, Ney Alliey-Rodriguez, Adebayo Anjorin, Verneri Antilla, Swapnil Awasthi, Judith A. Badner, Marie Bækvad-Hansen, Jack D. Barchas, Nicholas Bass, Michael Bauer, Richard Belliveau, Carsten Bøcker Pedersen, Erlend Bøen, Marco P. Boks, James Boocock, Monika Budde, William Bunney, Margit Burmeister, Jonas Bybjerg-Grauholm, Miquel Casas, Felecia Cerrato, Kimberly Chambert, Alexander W. Charney, Danfeng Chen, Claire Churchhouse, Toni-Kim Clarke, William Coryell, David W. Craig, Cristiana Cruceanu, Piotr M. Czerski, Anders M. Dale, Simone de Jong, Jurgen Del-Favero, J. Raymond DePaulo, Amanda L. Dobbyn, Ashley Dumont, Torbjørn Elvsåshagen, Chun Chieh Fan, Sascha B. Fischer, Matthew Flickinger, Tatiana M. Foroud, Liz Forty, Christine Fraser, Katrin Gade, Diane Gage, Julie Garnham, Claudia Giambartolomei, Marianne Giørtz Pedersen, Jaqueline Goldstein, Scott D. Gordon, Katherine Gordon-Smith, Elaine K. Green, Melissa J. Green, Tiffany A. Greenwood, Weihua Guan, Martin Hautzinger, Urs Heilbronner, Maria Hipolito, Dominic Holland, Laura Huckins, Jessica S. Johnson, Radhika Kandaswamy, Robert Karlsson, Sarah Kittel-Schneider, Anna C. Koller, Ralph Kupka, Catharina Lavebratt, Jacob Lawrence, William B. Lawson, Markus Leber, Phil H. Lee, Shawn E. Levy, Jun Z. Li, Chunyu Liu, Anna Maaser, Donald J. MacIntyre, Pamela B. Mahon, Lina Martinsson, Steve McCarroll, Peter McGuffin, Melvin G. McInnis, James D. McKay, Helena Medeiros, Sarah E. Medland, Fan Meng, Grant W. Montgomery, Thomas W. Mühleisen, Hoang Nguyen, Caroline M. Nievergelt, Annelie Nordin Adolfsson, Evaristus A. Nwulia, Claire O'Donovan, Loes M. Olde Loohuis, Anil P.S. Ori, Lilijana Oruc, Urban Ösby, Amy Perry, Andrea Pfennig, Eline J. Regeer, Céline S. Reinbold, John P. Rice, Fabio Rivas, Margarita Rivera, Euijung Ryu, Cristina Sánchez-Mora, Alan F. Schatzberg, William A. Scheftner, Nicholas J. Schork, Cynthia Shannon Weickert, Tatyana Shehktman, Paul D. Shilling, Claire Slaney, Janet L. Sobell, Christine Søholm Hansen, Anne T. Spijker, Michael Steffens, John S. Strauss, Szabolcs Szelinger, Robert C. Thompson, Thorgeir E. Thorgeirsson, Jens Treutlein, Helmut Vedder, Weiqing Wang, Stanley J. Watson, Thomas W. Weickert, Simon Xi, Wei Xu, Allan H. Young, Peter Zandi, Peng Zhang, Sebastian Zöllner, Abdel Abdellaoui, Tracy M. Air, Till F.M. Andlauer, Silviu-Alin Bacanu, Aartjan T.F. Beekman, Elisabeth B. Binder, Julien Bryois, Henriette N. Buttenschøn, Na Cai, Enrique Castelao, Jane Hvarregaard Christensen, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Gregory E. Crawford, Gail Davies, Ian J. Deary, Eske M. Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Jerome C. Foo, Fernando S. Goes, Lynsey S. Hall, Thomas F. Hansen, Ian B. Hickie, Georg Homuth, Carsten Horn, David M. Howard, Marcus Ising, Rick Jansen, Ian Jones, Lisa A. Jones, Eric Jorgenson, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Zoltán Kutalik, Yihan Li, Penelope A. Lind, Dean F. MacKinnon, Robert M. Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Christel M. Middeldorp, Evelin Mihailov, Francis M. Mondimore, Sara Mostafavi, Matthias Nauck, Bernard Ng, Michel G. Nivard, Dale R. Nyholt, Paul F. O'Reilly, Hogni Oskarsson, Jodie N. Painter, Roseann E. Peterson, Wouter J. Peyrot, Giorgio Pistis, Jorge A. Quiroz, Per Qvist, Saira Saeed Mirza, Robert Schoevers, Eva C. Schulte, Ling Shen, Stanley I. Shyn, Grant C.B. Sinnamon, Johannes H. Smit, Daniel J. Smith, Katherine E. Tansey, Henning Teismann, Wesley Thompson, Pippa A. Thomson, Matthew Traylor, André G. Uitterlinden, Daniel Umbricht, Albert M. van Hemert, Shantel Marie Weinsheimer, Jürgen Wellmann, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, Volker Arolt, Klaus Berger, Udo Dannlowski, Katharina Domschke, Caroline Hayward, Andrew C. Heath, Stefan Kloiber, Glyn Lewis, Pamela AF. Madden, Patrik K. Magnusson, Preben Bo Mortensen, Michael C. O'Donovan, Sara A. Paciga, Nancy L. Pedersen, David J. Porteous, Catherine Schaefer, Henry Völzke, Marco Bortolato, Janita Bralten, Cynthia M. Bulik, Christie L. Burton, Caitlin E. Carey, Lea K. Davis, Laramie E. Duncan, Howard J. Edenberg, Lauren Erdman, Stephen V. Faraone, Slavina B. Goleva, Wei Guo, Christopher Hübel, Laura M. Huckins, Ekaterina A. Khramtsova, Joanna Martin, Carol A. Mathews, Elise Robinson, Eli Stahl, Barbara E. Stranger, Michela Traglia, Raymond K. Walters, Lauren A. Weiss, Stacey J. Winham, Yin Yao, Kristjar Skajaa, Markus Nöthen, Michael Owen, Robert H. Yolken, Niels Plath, Jonathan Mill, Daniel Geschwind, Psychiatry 1, RS: MHeNs - R2 - Mental Health, Psychiatrie & Neuropsychologie, Centre of Excellence in Complex Disease Genetics, Research Programme of Molecular Medicine, Research Programs Unit, Aarno Palotie / Principal Investigator, Institute for Molecular Medicine Finland, Genomics of Neurological and Neuropsychiatric Disorders, Functional Genomics, Biological Psychology, APH - Mental Health, APH - Methodology, Sociology and Social Gerontology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Blokland, Gabriella AM, Grove, Jakob, Chen, Chia Yen, Cotsapas, Chris, Tobet, Stuart, Handa, Robert, Lee, Sang Hong, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Sex Differences Cross-Disorder Analysis Group of the Psychiatric Genomics Consortium, iPSYCH, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Human genetics, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Reproduction & Development (AR&D), Child and Adolescent Psychiatry / Psychology, Adult Psychiatry, ANS - Complex Trait Genetics, and ANS - Mood, Anxiety, Psychosis, Stress & Sleep
Subjects: 0301 basic medicine, Male, Bipolar Disorder, Schizophrenia/genetics, LD SCORE REGRESSION, Genome-wide association study, 0302 clinical medicine, Receptors, SCHIZOPHRENIA, Psychotic Disorders/genetics, KYNURENINE PATHWAY METABOLISM, Genetics, RISK, Sex Characteristics, Vascular Endothelial Growth Factor, Bipolar Disorder/genetics, Major/genetics, Single Nucleotide, AFFECTIVE STIMULI IMPACT, Schizophrenia, Sulfurtransferases, Major depressive disorder, Female, Depressive Disorder, Major/genetics, Bipolar disorder, Locus (genetics), Genomics, Biology, Polymorphism, Single Nucleotide, Article, DYSPHORIC MOOD, 03 medical and health sciences, Sex differences, medicine, Humans, Genetic Predisposition to Disease, ddc:610, Polymorphism, GENOME-WIDE ASSOCIATION, Genotype-by-sex interaction, Biological Psychiatry, Depressive Disorder, Major, Depressive Disorder, GENDER-DIFFERENCES, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], PARAVENTRICULAR NUCLEUS, 3112 Neurosciences, Endothelial Cells, MAJOR DEPRESSION, medicine.disease, Genetic architecture, 030104 developmental biology, Mood, Receptors, Vascular Endothelial Growth Factor, Psychotic Disorders, 3111 Biomedicine, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: Contains fulltext : 248656.pdf (Publisher’s version ) (Closed access) BACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. RESULTS: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10(-8)), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10(-6)) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10(-7); rs73033497, p = 8.8 × 10(-7); rs7914279, p = 6.4 × 10(-7)), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10(-7)) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10(-7)), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10(-7)) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). CONCLUSIONS: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.
Published: 2022
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35. Sex-Dependent Shared and Nonshared Genetic Architecture Across Mood and Psychotic Disorders

Author: Andreas J. Forstner, Martin Alda, Manolis Kogevinas, Pak C. Sham, Patrick F. Sullivan, Michele T. Pato, John R. Kelsoe, Paul A. Tooney, Steven P. Hamilton, Dorret I. Boomsma, Gerome Breen, Tracy Air, Hannelore Ehrenreich, Nakao Iwata, Jill M. Goldstein, Olav B. Smeland, Daniel R. Weinberger, Andrew McQuillin, Stuart A. Tobet, Elvira Bramon, Gabriëlla A.M. Blokland, Jing Qin Wu, Erik G. Jönsson, Peter R. Schofield, Fabian Streit, Nicholas Bass, Aarno Palotie, Brian Kelly, Cathryn M. Lewis, Srdjan Djurovic, Thomas Damm Als, Chia-Yen Chen, Bettina Konte, Joanna Hauser, Claire O'Donovan, Laura J. Scott, Hans J. Grabe, Murray J. Cairns, Rudolf Uher, Pablo Cervantes, Nicholas G. Martin, James A. Knowles, Aiden Corvin, Espen Molden, Lili Milani, Andreas Reif, Maria Grigoroiu-Serbanescu, George Kirov, Yuri Milaneschi, David St Clair, Eco J. C. de Geus, Robert Handa, Tõnu Esko, Alexander Teumer, Anders D. Børglum, Divya Mehta, Roel A. Ophoff, Susanne Lucae, Henning Tiemeier, Marion Leboyer, Ina Giegling, Alan R. Sanders, Jouke-Jan Hottenga, Pablo V. Gejman, Bernhard T. Baune, Brenda W.J.H. Penninx, Chris Cotsapas, Martin Preisig, Thomas Werge, Jakob Grove, Ingrid Melle, Jordan W. Smoller, Marcella Rietschel, Myrna M. Weissman, Preben Bo Mortensen, Philip B. Mitchell, Jolanta Lissowska, Andrew M. McIntosh, Annette M. Hartmann, Ole A. Andreassen, Lea Sirignano, John B. Vincent, Niamh Mullins, Jimmy Lee, Gonneke Willemsen, Marian L. Hamshere, Rolf Adolfsson, Mark A. Frye, Markus M. Nöthen, Jianxin Shi, Ashley L. Comes, Robin M. Murray, Patrik K. E. Magnusson, Frank Bellivier, Stéphane Jamain, Ole Mors, Sven Cichon, Uwe Völker, Fermín Mayoral, Bryan J. Mowry, Bruno Etain, James B. Potash, Beata Świątkowska, Bertram Müller-Myhsok, Mikael Landén, Tracey L. Petryshen, Franziska Degenhardt, Mark Adams, Dan Rujescu, Jose Guzman-Parra, Thomas G. Schulze, Merete Nordentoft, Joseph D. Buxbaum, Janice M. Fullerton, Brien P. Riley, Roy H. Perlis, Arne E. Vaaler, David M. Hougaard, Eli A. Stahl, Susan L. McElroy, Sabrina K. Schaupp, Martin Begemann, Sandra Van der Auwera, Todd Lencz, Joanna M. Biernacka, Agnes A. Steixner-Kumar, Douglas F. Levinson, Sathish Periyasamy, Alexander Viktorin, and Anil K. Malhotra
Subjects: Genetics, 0303 health sciences, Medizin, Locus (genetics), Genomics, Biology, medicine.disease, Genetic architecture, 03 medical and health sciences, 0302 clinical medicine, Mood, medicine, Major depressive disorder, Bipolar disorder, Gene, NKAIN2 Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: BACKGROUNDSex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk.METHODSWe conducted the largest to date genome-wide genotype–by–sex (GxS) interaction of risk for these disorders, using 85,735 cases (33,403 SCZ, 19,924 BIP, 32,408 MDD) and 109,946 controls from the Psychiatric Genomics Consortium (PGC) and iPSYCH.RESULTSAcross disorders, genome-wide significant SNP-by-sex interaction was detected for a locus encompassingNKAIN2(rs117780815;p=3.2×10−8), that interacts with sodium/potassium-transporting ATPase enzymes implicating neuronal excitability. Three additional loci showed evidence (p−6) for cross-disorder GxS interaction (rs7302529,p=1.6×10−7; rs73033497,p=8.8×10−7; rs7914279,p=6.4×10−7) implicating various functions. Gene-based analyses identified GxS interaction across disorders (p=8.97×10−7) with transcriptional inhibitorSLTM. Most significant in SCZ was aMOCOSgene locus (rs11665282;p=1.5×10−7), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509;p=1.1×10−7) in a locus containingIDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant GxS of genes regulating vascular endothelial growth factor (VEGF) receptor signaling in MDD (pFDRCONCLUSIONSIn the largest genome-wide GxS analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development, immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway enrichment levels.
Published: 2022

36. Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Author: Niamh Mullins, JooEun Kang, Adrian I. Campos, Jonathan R.I. Coleman, Alexis C. Edwards, Hanga Galfalvy, Daniel F. Levey, Adriana Lori, Andrey Shabalin, Anna Starnawska, Mei-Hsin Su, Hunna J. Watson, Mark Adams, Swapnil Awasthi, Michael Gandal, Jonathan D. Hafferty, Akitoyo Hishimoto, Minsoo Kim, Satoshi Okazaki, Ikuo Otsuka, Stephan Ripke, Erin B. Ware, Andrew W. Bergen, Wade H. Berrettini, Martin Bohus, Harry Brandt, Xiao Chang, Wei J. Chen, Hsi-Chung Chen, Steven Crawford, Scott Crow, Emily DiBlasi, Philibert Duriez, Fernando Fernández-Aranda, Manfred M. Fichter, Steven Gallinger, Stephen J. Glatt, Philip Gorwood, Yiran Guo, Hakon Hakonarson, Katherine A. Halmi, Hai-Gwo Hwu, Sonia Jain, Stéphane Jamain, Susana Jiménez-Murcia, Craig Johnson, Allan S. Kaplan, Walter H. Kaye, Pamela K. Keel, James L. Kennedy, Kelly L. Klump, Dong Li, Shih-Cheng Liao, Klaus Lieb, Lisa Lilenfeld, Chih-Min Liu, Pierre J. Magistretti, Christian R. Marshall, James E. Mitchell, Eric T. Monson, Richard M. Myers, Dalila Pinto, Abigail Powers, Nicolas Ramoz, Stefan Roepke, Vsevolod Rozanov, Stephen W. Scherer, Christian Schmahl, Marcus Sokolowski, Michael Strober, Laura M. Thornton, Janet Treasure, Ming T. Tsuang, Stephanie H. Witt, D. Blake Woodside, Zeynep Yilmaz, Lea Zillich, Rolf Adolfsson, Ingrid Agartz, Tracy M. Air, Martin Alda, Lars Alfredsson, Ole A. Andreassen, Adebayo Anjorin, Vivek Appadurai, María Soler Artigas, Sandra Van der Auwera, M. Helena Azevedo, Nicholas Bass, Claiton H.D. Bau, Bernhard T. Baune, Frank Bellivier, Klaus Berger, Joanna M. Biernacka, Tim B. Bigdeli, Elisabeth B. Binder, Michael Boehnke, Marco P. Boks, Rosa Bosch, David L. Braff, Richard Bryant, Monika Budde, Enda M. Byrne, Wiepke Cahn, Miguel Casas, Enrique Castelao, Jorge A. Cervilla, Boris Chaumette, Sven Cichon, Aiden Corvin, Nicholas Craddock, David Craig, Franziska Degenhardt, Srdjan Djurovic, Howard J. Edenberg, Ayman H. Fanous, Jerome C. Foo, Andreas J. Forstner, Mark Frye, Janice M. Fullerton, Justine M. Gatt, Pablo V. Gejman, Ina Giegling, Hans J. Grabe, Melissa J. Green, Eugenio H. Grevet, Maria Grigoroiu-Serbanescu, Blanca Gutierrez, Jose Guzman-Parra, Steven P. Hamilton, Marian L. Hamshere, Annette Hartmann, Joanna Hauser, Stefanie Heilmann-Heimbach, Per Hoffmann, Marcus Ising, Ian Jones, Lisa A. Jones, Lina Jonsson, René S. Kahn, John R. Kelsoe, Kenneth S. Kendler, Stefan Kloiber, Karestan C. Koenen, Manolis Kogevinas, Bettina Konte, Marie-Odile Krebs, Mikael Landén, Jacob Lawrence, Marion Leboyer, Phil H. Lee, Douglas F. Levinson, Calwing Liao, Jolanta Lissowska, Susanne Lucae, Fermin Mayoral, Susan L. McElroy, Patrick McGrath, Peter McGuffin, Andrew McQuillin, Sarah E. Medland, Divya Mehta, Ingrid Melle, Yuri Milaneschi, Philip B. Mitchell, Esther Molina, Gunnar Morken, Preben Bo Mortensen, Bertram Müller-Myhsok, Caroline Nievergelt, Vishwajit Nimgaonkar, Markus M. Nöthen, Michael C. O’Donovan, Roel A. Ophoff, Michael J. Owen, Carlos Pato, Michele T. Pato, Brenda W.J.H. Penninx, Jonathan Pimm, Giorgio Pistis, James B. Potash, Robert A. Power, Martin Preisig, Digby Quested, Josep Antoni Ramos-Quiroga, Andreas Reif, Marta Ribasés, Vanesa Richarte, Marcella Rietschel, Margarita Rivera, Andrea Roberts, Gloria Roberts, Guy A. Rouleau, Diego L. Rovaris, Dan Rujescu, Cristina Sánchez-Mora, Alan R. Sanders, Peter R. Schofield, Thomas G. Schulze, Laura J. Scott, Alessandro Serretti, Jianxin Shi, Stanley I. Shyn, Lea Sirignano, Pamela Sklar, Olav B. Smeland, Jordan W. Smoller, Edmund J.S. Sonuga-Barke, Gianfranco Spalletta, John S. Strauss, Beata Świątkowska, Maciej Trzaskowski, Gustavo Turecki, Laura Vilar-Ribó, John B. Vincent, Henry Völzke, James T.R. Walters, Cynthia Shannon Weickert, Thomas W. Weickert, Myrna M. Weissman, Leanne M. Williams, Naomi R. Wray, Clement C. Zai, Allison E. Ashley-Koch, Jean C. Beckham, Elizabeth R. Hauser, Michael A. Hauser, Nathan A. Kimbrel, Jennifer H. 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Derks, Nese Direk, Conor V. Dolan, Erin C. Dunn, Thalia C. Eley, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K. Finucane, Josef Frank, Héléna A. Gaspar, Michael Gill, Fernando S. Goes, Scott D. Gordon, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Yang Wu, Hualin S. Xi, Jian Yang, Futao Zhang, Volker Arolt, Dorret I. Boomsma, Udo Dannlowski, E.J.C. de Geus, J. Raymond Depaulo, Enrico Domenici, Katharina Domschke, Jakob Grove, Lynsey S. Hall, Christine Søholm Hansen, Thomas F. Hansen, Stefan Herms, Ian B. Hickie, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M. Howard, Rick Jansen, Eric Jorgenson, James A. Knowles, Isaac S. Kohane, Julia Kraft, Warren W. Kretzschmar, Zoltán Kutalik, Yihan Li, Penelope A. Lind, Donald J. MacIntyre, Dean F. MacKinnon, Robert M. Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Christel M. Middeldorp, Evelin Mihailov, Lili Milani, Francis M. Mondimore, Grant W. 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Regeer, Fabio Rivas, Julian Roth, Panos Roussos, Fanny Senner, Sally Sharp, Paul D. Shilling, Claire Slaney, Janet L. Sobell, Maria Soler Artigas, Anne T. Spijker, Dan J. Stein, Chikashi Terao, Claudio Toma, Paul Tooney, Evangelia-Eirini Tsermpini, Marquis P. Vawter, Helmut Vedder, Simon Xi, Wei Xu, Jessica Mei Kay Yang, Allan H. Young, Hannah Young, Peter P. Zandi, Hang Zhou, null HUNT All-In Psychiatry, Gulja Babadjanova, Lena Backlund, Susanne Bengesser, Douglas H.R. Blackwood, Vaughan J. Carr, Stanley Catts, Dimitris Dikeos, Bruno Etain, Panagiotis Ferentinos, Micha Gawlik, Elliot S. Gershon, Frans Henskens, Jan Hillert, Kyung Sue Hong, Christina M. Hultman, Kristian Hveem, Nakao Iwata, Assen V. Jablensky, George Kirov, Christine Lochner, Carmel Loughland, Carol A. Mathews, Francis J. McMahon, Patricia Michie, Bryan Mowry, Benjamin M. Neale, Caroline M. Nievergelt, Ketil J. Oedegaard, Tomas Olsson, Chris Pantelis, George P. Patrinos, Eva Z. 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J., Wasserman, D., Coon, H., Demontis, D., Docherty, A. R., Kuo, P. -H., Mann, J. J., Renteria, M. E., Stein, M. B., Willour, V., Psychiatry, Biological Psychology, APH - Methodology, APH - Mental Health, APH - Health Behaviors & Chronic Diseases, AMS - Sports, AMS - Ageing & Vitality, APH - Personalized Medicine, Amsterdam Neuroscience - Complex Trait Genetics, Complex Trait Genetics, Institute for Molecular Medicine Finland, Centre of Excellence in Complex Disease Genetics, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, HUS Psychiatry, Department of Public Health, Clinicum, Nuorisopsykiatria, Faculty Common Matters (Faculty of Social Sciences), Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, Biostatistics Helsinki, Anna Keski-Rahkonen / Principal Investigator, Elisabeth Ingrid Maria Widen / Principal Investigator, Genomic Discoveries and Clinical Translation, Internal medicine, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Digital Health, Mullins N., Kang J., Campos A.I., Coleman J.R.I., Edwards A.C., Galfalvy H., Levey D.F., Lori A., Shabalin A., Starnawska A., Su M.-H., Watson H.J., Adams M., Awasthi S., Gandal M., Hafferty J.D., Hishimoto A., Kim M., Okazaki S., Otsuka I., Ripke S., Ware E.B., Bergen A.W., Berrettini W.H., Bohus M., Brandt H., Chang X., Chen W.J., Chen H.-C., Crawford S., Crow S., DiBlasi E., Duriez P., Fernandez-Aranda F., Fichter M.M., Gallinger S., Glatt S.J., Gorwood P., Guo Y., Hakonarson H., Halmi K.A., Hwu H.-G., Jain S., Jamain S., Jimenez-Murcia S., Johnson C., Kaplan A.S., Kaye W.H., Keel P.K., Kennedy J.L., Klump K.L., Li D., Liao S.-C., Lieb K., Lilenfeld L., Liu C.-M., Magistretti P.J., Marshall C.R., Mitchell J.E., Monson E.T., Myers R.M., Pinto D., Powers A., Ramoz N., Roepke S., Rozanov V., Scherer S.W., Schmahl C., Sokolowski M., Strober M., Thornton L.M., Treasure J., Tsuang M.T., Witt S.H., Woodside D.B., Yilmaz Z., Zillich L., Adolfsson R., Agartz I., Air T.M., Alda M., Alfredsson L., Andreassen O.A., Anjorin A., Appadurai V., Soler Artigas M., Van der Auwera S., Azevedo M.H., Bass N., Bau C.H.D., Baune B.T., Bellivier F., Berger K., Biernacka J.M., Bigdeli T.B., Binder E.B., Boehnke M., Boks M.P., Bosch R., Braff D.L., Bryant R., Budde M., Byrne E.M., Cahn W., Casas M., Castelao E., Cervilla J.A., Chaumette B., Cichon S., Corvin A., Craddock N., Craig D., Degenhardt F., Djurovic S., Edenberg H.J., Fanous A.H., Foo J.C., Forstner A.J., Frye M., Fullerton J.M., Gatt J.M., Gejman P.V., Giegling I., Grabe H.J., Green M.J., Grevet E.H., Grigoroiu-Serbanescu M., Gutierrez B., Guzman-Parra J., Hamilton S.P., Hamshere M.L., Hartmann A., Hauser J., Heilmann-Heimbach S., Hoffmann P., Ising M., Jones I., Jones L.A., Jonsson L., Kahn R.S., Kelsoe J.R., Kendler K.S., Kloiber S., Koenen K.C., Kogevinas M., Konte B., Krebs M.-O., Landen M., Lawrence J., Leboyer M., Lee P.H., Levinson D.F., Liao C., Lissowska J., Lucae S., Mayoral F., McElroy S.L., McGrath P., McGuffin P., McQuillin A., Medland S.E., Mehta D., Melle I., Milaneschi Y., Mitchell P.B., Molina E., Morken G., Mortensen P.B., Muller-Myhsok B., Nievergelt C., Nimgaonkar V., Nothen M.M., O'Donovan M.C., Ophoff R.A., Owen M.J., Pato C., Pato M.T., Penninx B.W.J.H., Pimm J., Pistis G., Potash J.B., Power R.A., Preisig M., Quested D., Ramos-Quiroga J.A., Reif A., Ribases M., Richarte V., Rietschel M., Rivera M., Roberts A., Roberts G., Rouleau G.A., Rovaris D.L., Rujescu D., Sanchez-Mora C., Sanders A.R., Schofield P.R., Schulze T.G., Scott L.J., Serretti A., Shi J., Shyn S.I., Sirignano L., Sklar P., Smeland O.B., Smoller J.W., Sonuga-Barke E.J.S., Spalletta G., Strauss J.S., Swiatkowska B., Trzaskowski M., Turecki G., Vilar-Ribo L., Vincent J.B., Volzke H., Walters J.T.R., Shannon Weickert C., Weickert T.W., Weissman M.M., Williams L.M., Wray N.R., Zai C.C., Ashley-Koch A.E., Beckham J.C., Hauser E.R., Hauser M.A., Kimbrel N.A., Lindquist J.H., McMahon B., Oslin D.W., Qin X., Mattheisen M., Abdellaoui A., Adams M.J., Agerbo E., Andlauer T.F.M., Bacanu S.-A., Baekvad-Hansen M., Beekman A.T.F., Bryois J., Buttenschon H.N., Bybjerg-Grauholm J., Cai N., Christensen J.H., Clarke T.-K., Colodro-Conde L., Couvy-Duchesne B., Crawford G.E., Davies G., Derks E.M., Direk N., Dolan C.V., Dunn E.C., Eley T.C., Escott-Price V., Hassan Kiadeh F.F., Finucane H.K., Frank J., Gaspar H.A., Gill M., Goes F.S., Gordon S.D., Weinsheimer S.M., Wellmann J., Willemsen G., Wu Y., Xi H.S., Yang J., Zhang F., Arolt V., Boomsma D.I., Dannlowski U., de Geus E.J.C., Depaulo J.R., Domenici E., Domschke K., Esko T., Grove J., Hall L.S., Hansen C.S., Hansen T.F., Herms S., Hickie I.B., Homuth G., Horn C., Hottenga J.-J., Hougaard D.M., Howard D.M., Jansen R., Jorgenson E., Knowles J.A., Kohane I.S., Kraft J., Kretzschmar W.W., Kutalik Z., Li Y., Lind P.A., MacIntyre D.J., MacKinnon D.F., Maier R.M., Maier W., Marchini J., Mbarek H., Middeldorp C.M., Mihailov E., Milani L., Mondimore F.M., Montgomery G.W., Mostafavi S., Nauck M., Ng B., Nivard M.G., Nyholt D.R., O'Reilly P.F., Oskarsson H., Hayward C., Heath A.C., Lewis G., Li Q.S., Madden P.A.F., Magnusson P.K., Martin N.G., McIntosh A.M., Metspalu A., Mors O., Nordentoft M., Paciga S.A., Pedersen N.L., Painter J.N., Pedersen C.B., Pedersen M.G., Peterson R.E., Peyrot W.J., Posthuma D., Quiroz J.A., Qvist P., Rice J.P., Riley B.P., Mirza S.S., Schoevers R., Schulte E.C., Shen L., Sigurdsson E., Sinnamon G.C.B., Smit J.H., Smith D.J., Stefansson H., Steinberg S., Streit F., Strohmaier J., Tansey K.E., Teismann H., Teumer A., Thompson W., Thomson P.A., Thorgeirsson T.E., Traylor M., Treutlein J., Trubetskoy V., Uitterlinden A.G., Umbricht D., der Auwera S.V., van Hemert A.M., Viktorin A., Visscher P.M., Wang Y., Webb B.T., Perlis R.H., Porteous D.J., Schaefer C., Stefansson K., Tiemeier H., Uher R., Werge T., Lewis C.M., Breen G., Borglum A.D., Sullivan P.F., O'Connell K.S., Coombes B., Qiao Z., Als T.D., Borte S., Charney A.W., Drange O.K., Gandal M.J., Hagenaars S.P., Ikeda M., Kamitaki N., Krebs K., Panagiotaropoulou G., Schilder B.M., Sloofman L.G., Winsvold B.S., Won H.-H., Abramova L., Adorjan K., Al Eissa M., Albani D., Alliey-Rodriguez N., Antilla V., Antoniou A., Baek J.H., Bauer M., Beins E.C., Bergen S.E., Birner A., Boen E., Brum M., Brumpton B.M., Brunkhorst-Kanaan N., Byerley W., Cairns M., Cervantes P., Cruceanu C., Cuellar-Barboza A., Cunningham J., Curtis D., Czerski P.M., Dale A.M., Dalkner N., David F.S., Dobbyn A.L., Douzenis A., Elvsashagen T., Ferrier I.N., Fiorentino A., Foroud T.M., Forty L., Frei O., Freimer N.B., Frisen L., Gade K., Garnham J., Gelernter J., Gizer I.R., Gordon-Smith K., Greenwood T.A., Ha K., Haraldsson M., Hautzinger M., Heilbronner U., Hellgren D., Holmans P.A., Huckins L., Johnson J.S., Kalman J.L., Kamatani Y., Kittel-Schneider S., Koromina M., Kranz T.M., Kranzler H.R., Kubo M., Kupka R., Kushner S.A., Lavebratt C., Leber M., Lee H.-J., Levy S.E., Lewis C., Lundberg M., Magnusson S.H., Maihofer A., Malaspina D., Maratou E., Martinsson L., McGregor N.W., McKay J.D., Medeiros H., Millischer V., Moran J.L., Morris D.W., Muhleisen T.W., O'Brien N., O'Donovan C., Olde Loohuis L.M., Oruc L., Papiol S., Pardinas A.F., Perry A., Pfennig A., Porichi E., Raj T., Rapaport M.H., Regeer E.J., Rivas F., Roth J., Roussos P., Ruderfer D.M., Senner F., Sharp S., Shilling P.D., Slaney C., Sobell J.L., Artigas M.S., Spijker A.T., Stein D.J., Terao C., Toma C., Tooney P., Tsermpini E.-E., Vawter M.P., Vedder H., Xi S., Xu W., Kay Yang J.M., Young A.H., Young H., Zandi P.P., Zhou H., HUNT All-In Psychiatry, Babadjanova G., Backlund L., Bengesser S., Blackwood D.H.R., Carr V.J., Catts S., Dikeos D., Etain B., Ferentinos P., Gawlik M., Gershon E.S., Henskens F., Hillert J., Hong K.S., Hultman C.M., Hveem K., Iwata N., Jablensky A.V., Kirov G., Lochner C., Loughland C., Mathews C.A., McMahon F.J., Michie P., Mowry B., Neale B.M., Nievergelt C.M., Oedegaard K.J., Olsson T., Pantelis C., Patrinos G.P., Reininghaus E.Z., Saito T., Schall U., Schalling M., Scott R.J., Weickert C.S., Stordal E., Vaaler A.E., Vieta E., Waldman I.D., Zwart J.-A., Nurnberger J.I., Stahl E.A., Di Florio A., Adan R.A.H., Ando T., Aschauer H., Baker J.H., Bencko V., Birgegard A., Boden J.M., Boehm I., Boni C., Perica V.B., Buehren K., Bulik C.M., Burghardt R., Carlberg L., Cassina M., Clementi M., Cone R.D., Courtet P., Crowley J.J., Danner U.N., Davis O.S.P., de Zwaan M., Dedoussis G., Degortes D., DeSocio J.E., Dick D.M., Dina C., Dmitrzak-Weglarz M., Martinez E.D., Duncan L.E., Egberts K., Mattingsdal M., McDevitt S., Meulenbelt I., Micali N., Mitchell J., Mitchell K., Monteleone P., Monteleone A.M., Munn-Chernoff M.A., Nacmias B., Navratilova M., Ntalla I., Olsen C.M., O'Toole J.K., Padyukov L., Palotie A., Pantel J., Papezova H., Parker R., Pearson J.F., Ehrlich S., Escaramis G., Espeseth T., Estivill X., Farmer A., Favaro A., Fischer K., Floyd J.A.B., Focker M., Foretova L., Forzan M., Franklin C.S., Gambaro G., Giuranna J., Giusti-Rodriquez P., Gonidakis F., Gordon S., Mayora M.G., Guillaume S., Hanscombe K.B., Hatzikotoulas K., Hebebrand J., Helder S.G., Henders A.K., Herpertz-Dahlmann B., Herzog W., Hinney A., Horwood L.J., Hubel C., Petersen L.V., Purves K.L., Raevuori A., Reichborn-Kjennerud T., Ricca V., Ripatti S., Ritschel F., Roberts M., Rybakowski F., Santonastaso P., Scherag A., Schmidt U., Schork N.J., Schosser A., Seitz J., Slachtova L., Slagboom P.E., Slof-Op 't Landt M.C.T., Slopien A., Soranzo N., Sorbi S., Southam L., Steen V.W., Huckins L.M., Hudson J.I., Imgart H., Inoko H., Janout V., Jordan J., Julia A., Kalsi G., Kaminska D., Kaprio J., Karhunen L., Karwautz A., Kas M.J.H., Kennedy M.A., Keski-Rahkonen A., Kiezebrink K., Kim Y.-R., Kirk K.M., Klareskog L., Knudsen G.P.S., Larsen J.T., Le Hellard S., Leppa V.M., Lichtenstein P., Lin B.D., Lundervold A., Luykx J., Maj M., Mannik K., Marsal S., Stuber G.D., Szatkiewicz J.P., Tachmazidou I., Tenconi E., Tortorella A., Tozzi F., Tsitsika A., Tyszkiewicz-Nwafor M., Tziouvas K., van Elburg A.A., van Furth E.F., Wade T.D., Wagner G., Walton E., Whiteman D.C., Wichmann H.E., Widen E., Yao S., Zeggini E., Zerwas S., Zipfel S., Jungkunz M., Dietl L., Schwarze C.E., Dahmen N., Schott B.H., Mobascher A., Crivelli S., Dennis M.F., Harvey P.D., Carter B.W., Huffman J.E., Jacobson D., Madduri R., Olsen M.K., Pestian J., Gaziano J.M., Muralidhar S., Ramoni R., Beckham J., Chang K.-M., O'Donnell C.J., Tsao P.S., Breeling J., Huang G., Romero J.P.C., Moser J., Whitbourne S.B., Brewer J.V., Aslan M., Connor T., Argyres D.P., Stephens B., Brophy M.T., Humphries D.E., Selva L.E., Do N., Shayan S., Cho K., Pyarajan S., Hauser E., Sun Y., Zhao H., Wilson P., McArdle R., Dellitalia L., Mattocks K., Harley J., Zablocki C.J., Whittle J., Jacono F., Gutierrez S., Gibson G., Hammer K., Kaminsky L., Villareal G., Kinlay S., Xu J., Hamner M., Mathew R., Bhushan S., Iruvanti P., Godschalk M., Ballas Z., Ivins D., Mastorides S., Moorman J., Gappy S., Klein J., Ratcliffe N., Florez H., Okusaga O., Murdoch M., Sriram P., Yeh S.S., Tandon N., Jhala D., Aguayo S., Cohen D., Sharma S., Liangpunsakul S., Oursler K.A., Whooley M., Ahuja S., Constans J., Meyer P., Greco J., Rauchman M., Servatius R., Gaddy M., Wallbom A., Morgan T., Stapley T., Sherman S., Ross G., Tsao P., Strollo P., Boyko E., Meyer L., Gupta S., Huq M., Fayad J., Hung A., Lichy J., Hurley R., Robey B., Striker R., Erlangsen A., Kessler R.C., Porteous D., Ursano R.J., Wasserman D., Coon H., Demontis D., Docherty A.R., Kuo P.-H., Mann J.J., Renteria M.E., Stein M.B., and Willour V.
Subjects: LD SCORE REGRESSION, Genome-wide association study, Suicide, Attempted, 3124 Neurology and psychiatry, 0302 clinical medicine, Risk Factors, Insomnia, Suicide attempt, GWAS, Suïcidi, Depression (differential diagnoses), Cause of death, Psychiatry, 0303 health sciences, Factors de risc en les malalties, Mental Disorders, Genetic Correlation, Genome-wide Association Study, Pleiotropy, Polygenicity, Suicide, Suicide Attempt, DEPRESSION, 3. Good health, Genetic correlation, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Depressive Disorder, Major, Mental illness, Cohort, SEX, medicine.symptom, Human, medicine.medical_specialty, Risk factors in diseases, BF, Locus (genetics), BEHAVIORS, Psykiatri, EVENTS, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, ddc:610, GENOME-WIDE ASSOCIATION, IDEATION, Socioeconomic status, METAANALYSIS, Biological Psychiatry, 030304 developmental biology, business.industry, Risk Factor, Genetic architecture, THOUGHTS, RC0321, business, Malalties mentals, 030217 neurology & neurosurgery
Abstract: Statistical analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org) hosted by SURFsara and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu), which is supported by the Office of Research Infrastructure of the National Institutes of Health (Grant Nos. S10OD018522 and S10OD026880). This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. This work was funded by the National Institutes of Health (Grant Nos. R01MH116269 and R01MH121455 [to DMR]), NIGMS of the National Institutes of Health (Grant No. T32GM007347 [to JK]), and the Brain & Behavior Research Foundation (NARSAD Young Investigator Award No. 29551 [to NM])., BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders., Office of Research Infrastructure of the National Institutes of Health S10OD018522 S10OD026880, United States Department of Health & Human Services, National Institutes of Health (NIH) - USA R01MH116269 R01MH121455, NIH National Institute of General Medical Sciences (NIGMS) T32GM007347 NARSAD 29551
Published: 2022
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37. Genetic predisposition to major depressive disorder differentially impacts alcohol consumption and high-risk drinking situations in men and women with alcohol use disorder

Author: Victor M. Karpyak, Brandon J. Coombes, Jennifer R. Geske, Vanessa M. Pazdernik, Terry Schneekloth, Bhanu Prakash Kolla, Tyler Oesterle, Larissa L. Loukianova, Michelle K. Skime, Ada Man-Choi Ho, Quyen Ngo, Cedric Skillon, Ming-Fen Ho, Richard Weinshilboum, and Joanna M. Biernacka
Subjects: Pharmacology, Psychiatry and Mental health, Pharmacology (medical), Toxicology
Abstract: Lifetime history of major depressive disorder (MDD) has a sex-specific association with pretreatment alcohol consumption in patients with alcohol dependence. Here, we investigated the association of genetic load for MDD estimated using a polygenic risk score (PRS) with pretreatment alcohol consumption assessed with Timeline Follow Back in a sample of 287 men and 156 women meeting DSM-IV-TR criteria for alcohol dependence. Preferred drinking situations were assessed using the Inventory of Drug Taking Situations (IDTS). Linear models were used to test for association of normalized alcohol consumption measures with the MDD-PRS, adjusting for ancestry, age, sex, and number of days sober at baseline. We fit models both with and without adjustment for MDD history and alcohol-use-related PRSs as covariates. Higher MDD-PRS was associated with lower 90-day total alcohol consumption in men (β = -0.16, p = 0.0012) but not in women (β = 0.11, p = 0.18). The association of MDD-PRS with IDTS measures was also sex-specific: higher MDD-PRS was associated with higher propensity to drink in temptation-related situations in women, while the opposite (negative association)was found in men. MDD-PRS was not associated with lifetime MDD history in our sample, and adjustment for lifetime MDD and alcohol-related PRSs did not impact the results. Our results suggest that genetic load for MDD impacts pretreatment alcohol consumption in a sex-specific manner, which is similar to, but independent from, the effect of history of MDD. The clinical implications of these findings and contributing biological and psychological factors should be investigated in future studies.
Published: 2023
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38. Combining schizophrenia and depression polygenic risk scores improves the genetic prediction of lithium response in bipolar disorder patients

Author: Michael McCarthy, Claire O'Donovan, Urs Heilbronner, Ichiro Kusumi, Eduard Vieta, Liping Hou, Hsi-Chung Chen, Claire Slaney, Maria Grigoroiu-Serbanescu, Kazufumi Akiyama, Michael Bauer, Janusz K. Rybakowski, Frank Bellivier, Marion Leboyer, Katzutaka Shimoda, Palmiero Monteleone, Cristiana Cruceanu, Alessio Squassina, Stephanie H. Witt, Tadafumi Kato, Giovanni Severino, Alfonso Tortorella, J. Raymond DePaulo, Martin Alda, Louise Frisén, Mazda Adl, Martin Schalling, Per Hoffmann, Susan G. Leckband, Jean-Pierre Kahn, Jean-Michel Aubry, Francis J. McMahon, Sven Cichon, Alexandre Dayer, Tatyana Shekhtman, Franziska Degenhardt, James B. Potash, Bruno Etain, Joseph Frank, Antonio Benabarre, Bernhard T. Baune, Gloria Roberts, Ryota Hashimoto, Tomas Novak, Paul D. Shilling, Julia Veeh, Joanna M. Biernacka, Barbara König, Peter Falkai, Philip B. Mitchell, Urban Ösby, Esther Jiménez, Sébastien Gard, Mark A. Frye, Sarah Kittel-Schneider, Layla Kassem, Fasil Tekola-Ayele, Armin Birner, Cynthia Marie-Claire, Raffaella Ardau, Abesh Kumar Bhattacharjee, Stéphane Jamain, Julie Garnham, Guy A. Rouleau, Caterina Chillotti, Piotr M. Czerski, Thomas G. Schulze, Gustavo Turecki, Anbupalam Thalamuthu, Claudia Pisanu, Azmeraw T. Amare, Marina Mitjans, Sergi Papiol, Mario Maj, Bárbara Arias, Janice M. Fullerton, Nina Dalkner, Peter R. Schofield, Susanne Bengesser, Stefan Herms, Klaus Oliver Schubert, Francis M. Mondimore, Eva Z. Reininghaus, Fernando S. Goes, Lena Backlund, Francesc Colom, Catharina Lavebratt, Christian Simhandl, Marcella Rietschel, Micah Cearns, Mikael Landén, Norio Ozaki, Gonzalo Laje, Barbara W. Schweizer, Nirmala Akula, Andrea Pfennig, Yi-Hsiang Hsu, John R. Kelsoe, Lina Martinsson, Markus M. Nöthen, Caroline M. Nievergelt, Pavla Stopkova, Mirko Manchia, Susan L. McElroy, Peter P. Zandi, Scott R. Clark, Joanna Hauser, Andreas J. Forstner, Po-Hsiu Kuo, Andreas Reif, Maria Del Zompo, Paul Grof, Fabian Streit, Ewa Ferensztajn-Rochowiak, Pablo Cervantes, Thomas Stamm, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Complex Trait Genetics, Psychiatry, APH - Digital Health, Schubert, K. O., Thalamuthu, A., Amare, A. T., Frank, J., Streit, F., Adl, M., Akula, N., Akiyama, K., Ardau, R., Arias, B., Aubry, J. -M., Backlund, L., Bhattacharjee, A. K., Bellivier, F., Benabarre, A., Bengesser, S., Biernacka, J. M., Birner, A., Marie-Claire, C., Cearns, M., Cervantes, P., Chen, H. -C., Chillotti, C., Cichon, S., Clark, S. R., Cruceanu, C., Czerski, P. M., Dalkner, N., Dayer, A., Degenhardt, F., Del Zompo, M., Depaulo, J. R., Etain, B., Falkai, P., Forstner, A. J., Frisen, L., Frye, M. A., Fullerton, J. M., Gard, S., Garnham, J. S., Goes, F. S., Grigoroiu-Serbanescu, M., Grof, P., Hashimoto, R., Hauser, J., Heilbronner, U., Herms, S., Hoffmann, P., Hou, L., Hsu, Y. -H., Jamain, S., Jimenez, E., Kahn, J. -P., Kassem, L., Kuo, P. -H., Kato, T., Kelsoe, J., Kittel-Schneider, S., Ferensztajn-Rochowiak, E., Konig, B., Kusumi, I., Laje, G., Landen, M., Lavebratt, C., Leboyer, M., Leckband, S. G., Maj, M., Manchia, M., Martinsson, L., Mccarthy, M. J., Mcelroy, S., Colom, F., Mitjans, M., Mondimore, F. M., Monteleone, P., Nievergelt, C. M., Nothen, M. M., Novak, T., O'Donovan, C., Ozaki, N., Osby, U., Papiol, S., Pfennig, A., Pisanu, C., Potash, J. B., Reif, A., Reininghaus, E., Rouleau, G. A., Rybakowski, J. K., Schalling, M., Schofield, P. R., Schweizer, B. W., Severino, G., Shekhtman, T., Shilling, P. D., Shimoda, K., Simhandl, C., Slaney, C. M., Squassina, A., Stamm, T., Stopkova, P., Tekola-Ayele, F., Tortorella, A., Turecki, G., Veeh, J., Vieta, E., Witt, S. H., Roberts, G., Zandi, P. P., Alda, M., Bauer, M., Mcmahon, F. J., Mitchell, P. B., Schulze, T. G., Rietschel, M., and Baune, B. T.
Subjects: Oncology, Multifactorial Inheritance, Treatment response, medicine.medical_specialty, Lithium (medication), Bipolar disorder, Poor responder, Neurosciences. Biological psychiatry. Neuropsychiatry, Lithium, DISEASE, Article, Cellular and Molecular Neuroscience, Risk Factors, Internal medicine, medicine, Humans, Manic-depressive illness, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Depressió psíquica, METAANALYSIS, Biological Psychiatry, Depression (differential diagnoses), MANIA, Depressive Disorder, Depressive Disorder, Major, Trastorn bipolar, Depression, business.industry, Major, medicine.disease, Pathway analysis, Liti, COMPARATIVE EFFICACY, Psychiatry and Mental health, Mental depression, Schizophrenia, Polygenic risk score, Esquizofrènia, Pharmacogenomics, business, RC321-571, medicine.drug
Abstract: Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium’s therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.
Published: 2021
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39. Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication

Author: Paul E. Croarkin, Jeremiah B. Joyce, Joanna M. Biernacka, Madhukar H. Trivedi, Duan Liu, Mark A. Frye, Liewei Wang, Caroline Grant, William V. Bobo, Arjun P. Athreya, Richard M. Weinshilboum, Michelle K. Skime, Siamak MahmoudianDehkordi, Rima Kaddurah-Daouk, Taryn L. Mayes, and Thomas J. Carmody
Subjects: Treatment outcome, Neurosciences. Biological psychiatry. Neuropsychiatry, Single-nucleotide polymorphism, Citalopram, Predictive markers, Machine learning, computer.software_genre, Article, Machine Learning, Cellular and Molecular Neuroscience, mental disorders, medicine, Humans, Escitalopram, Biological Psychiatry, Depressive Disorder, Major, Depression, business.industry, medicine.disease, Antidepressive Agents, Psychiatry and Mental health, Treatment Outcome, Antidepressant therapy, Pharmacogenomics, Major depressive disorder, Antidepressant, Artificial intelligence, business, computer, RC321-571, medicine.drug
Abstract: Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p p p p
Published: 2021
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40. Dual Roles for the TSPYL Family in Mediating Serotonin Transport and the Metabolism of Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder

Author: Andy R. Eugene, Duan Liu, Richard M. Weinshilboum, Sisi Qin, Liewei Wang, Lingxin Zhang, Joanna M. Biernacka, Jia Yu, and Drew Neavin
Subjects: Serotonin, Serotonin transport, CYP2C19, Citalopram, Pharmacology, Polymorphism, Single Nucleotide, Severity of Illness Index, behavioral disciplines and activities, 030226 pharmacology & pharmacy, Article, Linkage Disequilibrium, 03 medical and health sciences, 0302 clinical medicine, mental disorders, Humans, Medicine, Escitalopram, Pharmacology (medical), Serotonin transporter, Serotonin Plasma Membrane Transport Proteins, Depressive Disorder, Major, biology, business.industry, Research, Nuclear Proteins, Articles, Hep G2 Cells, medicine.disease, 3. Good health, Cytochrome P-450 CYP2C19, Pharmacogenetics, 030220 oncology & carcinogenesis, Pharmacogenomics, biology.protein, Major depressive disorder, Caco-2 Cells, business, Selective Serotonin Reuptake Inhibitors, medicine.drug
Abstract: We previously reported that testis-specific Y-encoded-like protein (TSPYLs) are transcription regulators for CYP3A4, CYP2C9, and CYP2C19. Here, we observed dual roles for TSPYLs in mediating serotonin transport and the metabolism of selective serotonin reuptake inhibitors (SSRIs) in patients with major depressive disorder (MDD). The widely prescribed SSRIs, citalopram, and escitalopram are metabolized mainly by CYP2C19. The TSPYL1 rs3828743 single nucleotide polymorphism (SNP), which decreases its suppression of CYP2C19 expression, was associated with rapid escitalopram metabolism and worse treatment response in the Mayo PGRN-AMPS clinical trial. We also found that TSPYLs can regulate expression of the serotonin transporter protein, SLC6A4, and, in turn, serotonin transport into cells. The SNPs in tight linkage disequilibrium with the TSPYL1 rs10223646 SNP were significantly correlated with baseline severity of depression in patients with MDD in the Sequenced Treatment Alternatives to Relieve Depression and International SSRI Pharmacogenomics Consortium clinical trials. Our findings suggest that genetic variation in TSPYL genes may be novel indicators for baseline severity of depression and SSRI poor response.
Published: 2019
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41. Clinical phenotype and genetic risk factors for bipolar disorder with binge eating: an update

Author: Alfredo B. Cuellar-Barboza, Mark A. Frye, Stacey J. Winham, Susan L. McElroy, and Joanna M. Biernacka
Subjects: medicine.medical_specialty, Bipolar Disorder, Binge eating, business.industry, General Neuroscience, Genome-wide association study, Comorbidity, medicine.disease, 030227 psychiatry, 03 medical and health sciences, Eating disorders, 0302 clinical medicine, medicine, Humans, Pharmacology (medical), Neurology (clinical), Bipolar disorder, medicine.symptom, Genetic risk, Psychiatry, business, Clinical phenotype, Binge-Eating Disorder, 030217 neurology & neurosurgery
Abstract: Introduction: Clinical and genetic study of psychiatric conditions has underscored the co-occurrence of complex phenotypes and the need to refine them. Bipolar Disorder (BD) and Binge Eating (BE) b...
Published: 2019
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42. Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication

Author: Richard M. Weinshilboum, A. John Rush, Michelle K. Skime, Joanna M. Biernacka, Liewei Wang, Ravishankar K. Iyer, Drew Neavin, Elisabeth B. Binder, William V. Bobo, Mark A. Frye, Tania Carrillo-Roa, and Arjun P. Athreya
Subjects: Adult, Genetic Markers, Male, Pharmacogenomic Variants, Serotonin reuptake inhibitor, Single-nucleotide polymorphism, Citalopram, Machine learning, computer.software_genre, Polymorphism, Single Nucleotide, behavioral disciplines and activities, 030226 pharmacology & pharmacy, Article, Biomarkers, Pharmacological, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Clinical Decision Rules, mental disorders, medicine, Humans, Escitalopram, Pharmacology (medical), Pharmacology, Depressive Disorder, Major, business.industry, Research, Remission Induction, Articles, medicine.disease, Pharmacogenomic Testing, 3. Good health, 030220 oncology & carcinogenesis, Pharmacogenomics, Major depressive disorder, Antidepressant, Female, Artificial intelligence, business, computer, Algorithms, Selective Serotonin Reuptake Inhibitors, Genome-Wide Association Study, medicine.drug
Abstract: We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1,ERICH3,AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.
Published: 2019
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43. Alcohol Craving Predicts Relapse After Residential Addiction Treatment

Author: Terry D. Schneekloth, Jennifer R. Geske, Joanna M. Biernacka, Victor M. Karpyak, and Matthew E Stohs
Subjects: Adult, Male, medicine.medical_specialty, Time Factors, Subsequent Relapse, Craving, Relapse prevention, Predictive Value of Tests, Recurrence, Internal medicine, medicine, Humans, Residential Treatment, Addiction treatment, Proportional hazards model, business.industry, Alcohol dependence, General Medicine, Middle Aged, Alcohol craving, Alcoholism, Predictive value of tests, Female, medicine.symptom, business
Abstract: Aims Replicate the previously reported association of elevated alcohol craving, measured by Penn Alcohol Craving Scale (PACS) during residential treatment, with post-treatment relapse and explore whether elevated craving scores 3 months post-treatment are also associated with subsequent relapse. Methods Alcohol craving was assessed with the PACS on admission and at several time points post-treatment in 190 subjects with DSM-IV diagnosis of alcohol dependence admitted to residential treatment. Data about relapse to any drinking (primary outcome measure) was collected at 3, 6, 9 and 12 months after treatment. Cox regression models were used to determine whether PACS scores were associated with relapse. Statistical models were adjusted for meaningful demographic and clinical covariates. Results Follow-up data was available for 149/190 (78%) of subjects. Elevated PACS scores at discharge were associated with increased relapse risk within the first 3 and 12 months after discharge (P = 0.032 and P = 0.045, respectively). Elevated PACS scores at 3 months were associated with increased risk of subsequent relapse within 12 months after treatment in contacted subjects (P = 0.034) and in the intent-to-treat analysis (P = 0.0001). Conclusions Our findings indicate strong association of post-treatment relapse with elevated alcohol craving measured at treatment completion and at 3 months after treatment and justify the use of this measure to guide relapse-prevention efforts.
Published: 2019
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44. Differences in perceived life stress in bipolar I and II disorder: Implications for future epigenetic quantification

Author: Adrienne Grzenda, Marin Veldic, Yun-Fang Jia, Susan L. McElroy, David J. Bond, Jennifer R. Geske, Aysegul Ozerdem, Balwinder Singh, Joanna M. Biernacka, Doo-Sup Choi, and Mark A. Frye
Subjects: Psychiatry and Mental health, Clinical Psychology, Neurology (clinical)
Published: 2022
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45. Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Author: Euijung Ryu, Mohit Manoj Sharma, Mark Olfson, William R. Hogan, Jyotishman Pathak, Benjamin S. Glicksberg, Myrna M. Weissman, Olga V. Patterson, Thomas J. George, Priya Wickramaratne, Veer Vekaria, Al'ona Furmanchuk, Xi Yang, Prakash Adekkanattu, Jian-Guo Bian, Mark G. Weiner, J. John Mann, Thomas R. Campion, Yonghui Wu, Lauren Lepow, Braja Gopal Patra, and Joanna M. Biernacka
Subjects: AcademicSubjects/SCI01060, Social Determinants of Health, Scopus, Reviews, Health Informatics, Health records, computer.software_genre, Clinical decision support system, Machine Learning, medicine, Humans, Social determinants of health, information extraction, Social isolation, natural language processing, AcademicSubjects/MED00580, Data Management, business.industry, Information extraction, Systematic review, electronic health records, population health outcomes, Artificial intelligence, medicine.symptom, AcademicSubjects/SCI01530, business, Psychology, computer, Inclusion (education), Natural language processing
Abstract: Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
Published: 2021

46. Characterization of Age and Polarity at Onset in Bipolar Disorder

Author: Aiden Corvin, Maria Grigoroiu-Serbanescu, Mark Hyman Rapaport, Frederike T. Fellendorf, Urs Heilbronner, Mariam M. Al Eissa, John J Nurnberger, Claire O'Donovan, Claire Slaney, Mark A. Frye, Niamh L. O'Brien, Roland Hasler, Michael John Owen, Michael Conlon O'Donovan, Farah Klöhn-Saghatolislam, Markus M. Nöthen, Scott R. Clark, Mikael Landén, Lina Jonsson, Peter Falkai, Qingqin S. Li, Alessio Squassina, Simon Schmitt, Gustavo Turecki, Heike Anderson-Schmidt, Jonathan Repple, Sofie R. Aminoff, Eduard Vieta, Katherine Gordon-Smith, Stéphane Jamain, William Byerley, Hélène Richard-Lepouriel, Fermín Mayoral, Francis J. McMahon, Marin Veldic, Ingrid Melle, Andrew McQuillin, Nicholas Bass, Gloria Roberts, Douglas M. Ruderfer, John B. Vincent, Nelson B. Freimer, Kirov George, Wei Xu, Susan L. McElroy, Ceylan Balaban, Marco P. Boks, Franziska Degenhardt, Paul E. Croarkin, Wade H. Berrettini, Piotr M. Czerski, Fernando S. Goes, Peter P. Zandi, Sabrina K. Schaupp, Barbara W. Schweizer, Andrew M. McIntosh, Marcella Rietschel, Helena Pelin, Evelyn J. Bromet, Marion Leboyer, Ole A. Andreassen, Georgia Panagiotaropoulou, Alessia Fiorentino, Sally I. Sharp, Torbjørn Elvsåshagen, Kristina Adorjan, Monika Budde, Lisa Jones, Thomas G. Schulze, Vihra Milanova, Janos Kalman, Tim B. Bigdeli, Esther Jiménez, Abigail Ortiz, Dolores Malaspina, Kevin S. O’Connell, Sergi Papiol, Mario Maj, Tim Hahn, Frederike Stein, Jordan W. Smoller, Tomas Novak, Katrin Gade, J. Raymond DePaulo, Sarah Kittel-Schneider, Julia-Katharina Pfarr, Martin Alda, James A. Knowles, Sven Cichon, David Craig, Eli A. Stahl, William A. Scheftner, Bruno Etain, Annabel Vreeker, Guy A. Rouleau, Stephan Ripke, Clement C. Zai, Frank Bellivier, Giovanni Severino, Palmiero Monteleone, Ian Jones, Bertram Müller-Myhsok, Mark Adams, John Strauss, Philip B. Mitchell, Andreas Reif, Fabian Streit, Roel A. Ophoff, James L. Kennedy, Nils Opel, Raffaella Ardau, Paul Grof, Peter R. Schofield, Susanne Bengesser, Maria Hake, Loes M. Olde Loohuis, Joanna Pawlak, Daniela Reich-Erkelenz, Ingrid Agartz, Maria Del Zompo, Antonio Benabarre, Bernhard T. Baune, Arianna Di Florio, Ashley L. Comes, Michael Gill, Cristiana Cruceanu, Susanne Meinert, William Coryell, Alessio Maria Monteleone, Fanny Senner, Trine Vik Lagerberg, Jose Guzman-Parra, Erik Pålsson, Elliot S. Gershon, Melvin G. McInnis, Pavla Stopkova, Nikolaos Koutsouleris, René S. Kahn, Janice M. Fullerton, Howard J. Edenberg, Tilo Kircher, Nina Dalkner, Udo Dannlowski, Rolf Adolfsson, Alfonso Tortorella, Jean-Michel Aubry, James B. Potash, Catina Chillotti, Eva Z. Reininghaus, Janet L. Sobell, Thorsten M. Kranz, Tina Meller, Ayman H. Fanous, Kai Ringwald, K Oliver Schubert, Andreas J. Forstner, Till F. M. Andlauer, Helena Medeiros, Mirko Manchia, Joanna M. Biernacka, Katharina Brosch, Julie Garnham, Olav B. Smeland, Eva C. Schulte, Carlos N. Pato, Nicholas John Craddock, Srdjan Djurovic, Pablo Cervantes, Michele T. Pato, John R. Kelsoe, Claudia Pisanu, Joanna Twarowska-Hauser, and William Lawson
Subjects: 0303 health sciences, medicine.medical_specialty, business.industry, Genome-wide association study, Disease, Heritability, medicine.disease, Genetic architecture, 03 medical and health sciences, 0302 clinical medicine, Autism spectrum disorder, Schizophrenia, Internal medicine, medicine, Bipolar disorder, business, 030217 neurology & neurosurgery, 030304 developmental biology, Genetic association
Abstract: BackgroundStudying the phenotypic and genetic characteristics of age and polarity at onset (AAO, PAO) in bipolar disorder (BD) can provide new insights into disease pathology and facilitate the development of screening tools.AimsTo examine the genetic architecture of AAO and PAO and their association with BD disease characteristics.MethodsGenome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (N=12977) and PAO (N=6773) were conducted in BD patients of 34 cohorts and a replication sample (N=2237). The association of onset with disease characteristics was investigated in two of these cohorts.ResultsEarlier AAO was associated with an increased risk of psychotic symptoms, suicidality, and fewer episodes. A depressive onset correlated with lifetime suicidality and a manic onset with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in SNV-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased polygenic scores for autism spectrum disorder (β=-0.34 years, SE=0.08), major depression (β=-0.34 years, SE=0.08), schizophrenia (β=-0.39 years, SE=0.08), and educational attainment (β=-0.31 years, SE=0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.ConclusionsAAO and PAO are associated with indicators of BD severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents, and phenotype definitions introduce significant heterogeneity, affecting analyses.RELEVANCE STATEMENTIn the largest study to systematically characterize age at onset (N=12977) and polarity at onset (N=6773) in bipolar disorder, we describe an association between illness onset characteristics and indicators of severity, confirming their clinical relevance. Our study shows that that early illness onset is associated with genetic liability for a broad range of psychiatric disorders. However, we also highlight systematic differences in age at onset across cohorts, continents, and phenotype definitions. This heterogeneity results in reduced heritability and affects genetic analyses, underscoring the need for the development of standardized phenotype definitions.
Published: 2021
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47. Clinical predictors of non-response to lithium treatment in the Pharmacogenomics of Bipolar Disorder (PGBD) study

Author: Paul D. Shilling, John I. Nurnberger, Michael McCarthy, Martin Alda, Megan Ritchey, Wade H. Berrettini, Vigdis Elin Giever Syrstad, Cynthia V. Calkin, Peter P. Zandi, Gloria Harrington, Fernando S. Goes, William Coryell, Elliot S. Gershon, Yokesh Balaraman, Toyomi Goto, Elizabeth Karberg, Abesh Kumar Bhattacharjee, Joseph R. Calabrese, Bruce Tarwater, Caroline M. Nievergelt, Helle K. Schoeyen, Kelly A. Ryan, John R. Kelsoe, Joanna M. Biernacka, Julie Garnham, Masoud Kamali, Kara Glazer, Falk W. Lohoff, Francis M. Mondimore, Gunnar Morken, Yian Lin, Ney Alliey-Rodriguez, Mark A. Frye, Nicole Frazier, Petter Jakobsen, Marth Shaw, Ole A. Andreassen, Melvin G. McInnis, Adam X. Maihofer, Martha Schinagle, Emma K. Stapp, Susan G. Leckband, Keming Gao, Amit Anand, Carrie Fisher, Marisa Kelly, Claire Slaney, Andrea Stautland, Carla Conroy, Ketil J. Oedegaard, Anna DeModena, Nicole D'Arcangelo, and Holli Bertram
Subjects: medicine.medical_specialty, Bipolar Disorder, Lithium (medication), business.industry, Lithium, medicine.disease, Treatment failure, First line treatment, Psychiatry and Mental health, Pharmacotherapy, Treatment Outcome, Prospective trial, Pharmacogenetics, Internal medicine, Partial response, Pharmacogenomics, medicine, Lithium Compounds, Humans, Bipolar disorder, Prospective Studies, business, Biological Psychiatry, medicine.drug
Abstract: Background Lithium is regarded as a first-line treatment for bipolar disorder (BD), but partial response and non-response commonly occurs. There exists a need to identify lithium non-responders prior to initiating treatment. The Pharmacogenomics of Bipolar Disorder (PGBD) Study was designed to identify predictors of lithium response. Methods The PGBD Study was an eleven site prospective trial of lithium treatment in bipolar I disorder. Subjects were stabilized on lithium monotherapy over 4 months and gradually discontinued from all other psychotropic medications. After ensuring a sustained clinical remission (defined by a score of ≤3 on the CGI for 4 weeks) had been achieved, subjects were followed for up to 2 years to monitor clinical response. Cox proportional hazard models were used to examine the relationship between clinical measures and time until failure to remit or relapse. Results A total of 345 individuals were enrolled into the study and included in the analysis. Of these, 101 subjects failed to remit or relapsed, 88 achieved remission and continued to study completion, and 156 were terminated from the study for other reasons. Significant clinical predictors of treatment failure (p < 0.05) included baseline anxiety symptoms, functional impairments, negative life events and lifetime clinical features such as a history of migraine, suicidal ideation/attempts, and mixed episodes, as well as a chronic course of illness. Conclusions In this PGBD Study of lithium response, several clinical features were found to be associated with failure to respond to lithium. Future validation is needed to confirm these clinical predictors of treatment failure and their use clinically to distinguish who will do well on lithium before starting pharmacotherapy. publishedVersion
Published: 2021

48. POLYGENIC RISK OF COMORBID ANXIETY AND DEPRESSION ACROSS HEALTH SYSTEMS

Author: Myrna M. Weissman, Brandon J. Coombes, Nicolas A. Nunez, J. John Mann, Alexander W. Charney, Jyotishman Pathak, Isotta Landi, Mark Olfson, Priya Wickramaratne, Yanshan Wang, Euijung Ryu, Joanna M. Biernacka, Anthony Batzler, Gregory D. Jenkins, and Colin L. Colby
Subjects: Pharmacology, medicine.medical_specialty, Comorbid anxiety, business.industry, Psychiatry and Mental health, Neurology, medicine, Pharmacology (medical), Polygenic risk score, Neurology (clinical), Psychiatry, business, Biological Psychiatry, Depression (differential diagnoses), Healthcare system
Published: 2021
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49. TU86. GENETIC CORRELATION BETWEEN TESTOSTERONE LEVELS AND ALCOHOL DEPENDENCE IN FEMALES AND MALES

Author: Victor M. Karpyak, Stacey J. Winham, Sofia Pozsonyiova, Joanna M. Biernacka, T. Cameron Waller, and Ada Man Choi Ho
Subjects: Pharmacology, medicine.medical_specialty, Alcohol dependence, Testosterone (patch), Biology, Genetic correlation, Psychiatry and Mental health, Endocrinology, Neurology, Internal medicine, medicine, Pharmacology (medical), Neurology (clinical), Biological Psychiatry
Published: 2021
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50. TH18. POLYGENIC PREDICTION OF BIPOLAR DISORDER IN A LATIN AMERICAN SAMPLE

Author: Brandon J. Coombes, Mark A. Frye, Nicolas A. Nunez, Joanna M. Biernacka, Miguel L. Prieto, Alfredo B. Cuellar-Barboza, Manuel Gardea, and Susan L. McElroy
Subjects: Pharmacology, Psychiatry and Mental health, Latin Americans, Neurology, medicine, Pharmacology (medical), Sample (statistics), Neurology (clinical), Bipolar disorder, medicine.disease, Psychology, Biological Psychiatry, Clinical psychology
Published: 2021
Full Text: View/download PDF

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