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  -