@article{BreuerMattheisenFranketal.2018,
  author    = {Breuer, Ren{\´e} and Mattheisen, Manuel and Frank, Josef and Krumm, Bertram and Treutlein, Jens and Kassem, Layla and Strohmaier, Jana and Herms, Stefan and M{\"u}hleisen, Thomas W. and Degenhardt, Franziska and Cichon, Sven and N{\"o}then, Markus M. and Karypis, George and Kelsoe, John and Greenwood, Tiffany and Nievergelt, Caroline and Shilling, Paul and Shekhtman, Tatyana and Edenberg, Howard and Craig, David and Szelinger, Szabolcs and Nurnberger, John and Gershon, Elliot and Alliey-Rodriguez, Ney and Zandi, Peter and Goes, Fernando and Schork, Nicholas and Smith, Erin and Koller, Daniel and Zhang, Peng and Badner, Judith and Berrettini, Wade and Bloss, Cinnamon and Byerley, William and Coryell, William and Foroud, Tatiana and Guo, Yirin and Hipolito, Maria and Keating, Brendan and Lawson, William and Liu, Chunyu and Mahon, Pamela and McInnis, Melvin and Murray, Sarah and Nwulia, Evaristus and Potash, James and Rice, John and Scheftner, William and Z{\"o}llner, Sebastian and McMahon, Francis J. and Rietschel, Marcella and Schulze, Thomas G.},
  title     = {Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics},
  series = {International Journal of Bipolar Disorders},
  volume    = {6},
  journal   = {International Journal of Bipolar Disorders},
  doi       = {10.1186/s40345-018-0132-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220509},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}