TY - JOUR A1 - Havik, Bjarte A1 - Degenhardt, Franziska A. A1 - Johansson, Stefan A1 - Fernandes, Carla P. D. A1 - Hinney, Anke A1 - Scherag, André A1 - Lybaek, Helle A1 - Djurovic, Srdjan A1 - Christoforou, Andrea A1 - Ersland, Kari M. A1 - Giddaluru, Sudheer A1 - O'Donovan, Michael C. A1 - Owen, Michael J. A1 - Craddock, Nick A1 - Mühleisen, Thomas W. A1 - Mattheisen, Manuel A1 - Schimmelmann, Benno G. A1 - Renner, Tobias A1 - Warnke, Andreas A1 - Herpertz-Dahlmann, Beate A1 - Sinzig, Judith A1 - Albayrak, Özgür A1 - Rietschel, Marcella A1 - Nöthen, Markus M. A1 - Bramham, Clive R. A1 - Werge, Thomas A1 - Hebebrand, Johannes A1 - Haavik, Jan A1 - Andreassen, Ole A. A1 - Cichon, Sven A1 - Steen, Vidar M. A1 - Le Hellard, Stephanie T1 - DCLK1 Variants Are Associated across Schizophrenia and Attention Deficit/Hyperactivity Disorder JF - PLoS One N2 - Doublecortin and calmodulin like kinase 1 (DCLK1) is implicated in synaptic plasticity and neurodevelopment. Genetic variants in DCLK1 are associated with cognitive traits, specifically verbal memory and general cognition. We investigated the role of DCLK1 variants in three psychiatric disorders that have neuro-cognitive dysfunctions: schizophrenia (SCZ), bipolar affective disorder (BP) and attention deficit/hyperactivity disorder (ADHD). We mined six genome wide association studies (GWASs) that were available publically or through collaboration; three for BP, two for SCZ and one for ADHD. We also genotyped the DCLK1 region in additional samples of cases with SCZ, BP or ADHD and controls that had not been whole-genome typed. In total, 9895 subjects were analysed, including 5308 normal controls and 4,587 patients (1,125 with SCZ, 2,496 with BP and 966 with ADHD). Several DCLK1 variants were associated with disease phenotypes in the different samples. The main effect was observed for rs7989807 in intron 3, which was strongly associated with SCZ alone and even more so when cases with SCZ and ADHD were combined (P-value = 4x10\(^{-5}\) and 4x10\(^{-6}\), respectively). Associations were also observed with additional markers in intron 3 (combination of SCZ, ADHD and BP), intron 19 (SCZ+BP) and the 3'UTR (SCZ+BP). Our results suggest that genetic variants in DCLK1 are associated with SCZ and, to a lesser extent, with ADHD and BP. Interestingly the association is strongest when SCZ and ADHD are considered together, suggesting common genetic susceptibility. Given that DCLK1 variants were previously found to be associated with cognitive traits, these results are consistent with the role of DCLK1 in neurodevelopment and synaptic plasticity. KW - psychosis KW - deficit hyperactivity disorder KW - genome-wide association KW - bipolar disorder KW - VAL66MET polymorphism KW - doublecortine-like KW - genes KW - kinase KW - BDNF KW - endophenotype Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-135285 VL - 7 IS - 4 ER - TY - JOUR A1 - Meier, Sandra M. A1 - Kähler, Anna K. A1 - Bergen, Sarah E. A1 - Sullivan, Patrick F. A1 - Hultman, Christina M. A1 - Mattheisen, Manuel T1 - Chronicity and Sex Affect Genetic Risk Prediction in Schizophrenia JF - Frontiers in Psychiatry N2 - Schizophrenia (SCZ) is a severe mental disorder with immense personal and societal costs; identifying individuals at risk is therefore of utmost importance. Genomic risk profile scores (GRPS) have been shown to significantly predict cases-control status. Making use of a large-population based sample from Sweden, we replicate a previous finding demonstrating that the GRPS is strongly associated with admission frequency and chronicity of SCZ. Furthermore, we were able to show a substantial gap in prediction accuracy between males and females. In sum, our results indicate that prediction accuracy by GRPS depends on clinical and demographic characteristics. KW - schizophrenia KW - polygenic risk score KW - prediction KW - sex KW - course Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-205677 SN - 1664-0640 VL - 11 ER - TY - JOUR A1 - Breuer, René A1 - Mattheisen, Manuel A1 - Frank, Josef A1 - Krumm, Bertram A1 - Treutlein, Jens A1 - Kassem, Layla A1 - Strohmaier, Jana A1 - Herms, Stefan A1 - Mühleisen, Thomas W. A1 - Degenhardt, Franziska A1 - Cichon, Sven A1 - Nöthen, Markus M. A1 - Karypis, George A1 - Kelsoe, John A1 - Greenwood, Tiffany A1 - Nievergelt, Caroline A1 - Shilling, Paul A1 - Shekhtman, Tatyana A1 - Edenberg, Howard A1 - Craig, David A1 - Szelinger, Szabolcs A1 - Nurnberger, John A1 - Gershon, Elliot A1 - Alliey-Rodriguez, Ney A1 - Zandi, Peter A1 - Goes, Fernando A1 - Schork, Nicholas A1 - Smith, Erin A1 - Koller, Daniel A1 - Zhang, Peng A1 - Badner, Judith A1 - Berrettini, Wade A1 - Bloss, Cinnamon A1 - Byerley, William A1 - Coryell, William A1 - Foroud, Tatiana A1 - Guo, Yirin A1 - Hipolito, Maria A1 - Keating, Brendan A1 - Lawson, William A1 - Liu, Chunyu A1 - Mahon, Pamela A1 - McInnis, Melvin A1 - Murray, Sarah A1 - Nwulia, Evaristus A1 - Potash, James A1 - Rice, John A1 - Scheftner, William A1 - Zöllner, Sebastian A1 - McMahon, Francis J. A1 - Rietschel, Marcella A1 - Schulze, Thomas G. T1 - Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics JF - International Journal of Bipolar Disorders N2 - Background Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Results Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. Conclusion Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts. KW - bipolar disorder KW - subphenotypes KW - rule discovery KW - data mining KW - genotype-phenotype patterns Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220509 VL - 6 ER -