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 - TY - JOUR A1 - Biere, Silvia A1 - Kranz, Thorsten M. A1 - Matura, Silke A1 - Petrova, Kristiyana A1 - Streit, Fabian A1 - Chiocchetti, Andreas G. A1 - Grimm, Oliver A1 - Brum, Murielle A1 - Brunkhorst-Kanaan, Natalie A1 - Oertel, Viola A1 - Malyshau, Aliaksandr A1 - Pfennig, Andrea A1 - Bauer, Michael A1 - Schulze, Thomas G. A1 - Kittel-Schneider, Sarah A1 - Reif, Andreas T1 - Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults JF - Frontiers in Psychiatry N2 - Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD. Methods: A final sample of 185 young, high-risk German adults (aged 18–35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes. Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls. Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification. KW - polygenic risk score KW - bipolar disorder KW - genetic phenotypes KW - depression KW - ADHD KW - early recognition Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-214976 VL - 11 ER -