Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics

René Breuer, Manuel Mattheisen, Josef Frank, Bertram Krumm, Jens Treutlein, Layla Kassem, Jana Strohmaier, Stefan Herms, Thomas W. Mühleisen, Franziska Degenhardt, Sven Cichon, Markus M. Nöthen, George Karypis, John Kelsoe, Tiffany Greenwood, Caroline Nievergelt, Paul Shilling, Tatyana Shekhtman, Howard Edenberg, David CraigSzabolcs Szelinger, John Nurnberger, Elliot Gershon, Ney Alliey-Rodriguez, Peter Zandi, Fernando Goes, Nicholas Schork, Erin Smith, Daniel Koller, Peng Zhang, Judith Badner, Wade Berrettini, Cinnamon Bloss, William Byerley, William Coryell, Tatiana Foroud, Yirin Guo, Maria Hipolito, Brendan Keating, William Lawson, Chunyu Liu, Pamela Mahon, Melvin McInnis, Sarah Murray, Evaristus Nwulia, James Potash, John Rice, William Scheftner, Sebastian Zöllner, Francis J. McMahon, Marcella Rietschel, Thomas G. Schulze

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Article number24
JournalInternational Journal of Bipolar Disorders
Volume6
Issue number1
DOIs
StatePublished - Dec 1 2018

Bibliographical note

Funding Information:
RB: study design, analysis, writing the first draft, software programming; MM: study design, analysis, interpretation of data; JF, BK, JT: analysis, interpretation of data; ; LK: study design, phenotyping; JS: phenotyping; SH, TWM, FD: molecular genetic analyses; SC: molecular genetic analyses, interpretation of the data; MMN: study design, interpretation of data, securing funding; GK: method development, study design, interpretation of data; JK, TG, CN, PS, TS, HE, DC, SS, JN, EG, NAR, PZ, FG, NS, ES, DK, PZ, JB, WB, CB, WB, WC, TF, YG, MH, BK, WL, CL, PM, MM, SM, EN, JP, JR, WS, SZ: study design, interpretation of data, securing funding; FJM: study design, interpretation of data, securing funding; MR: study design, interpretation of data, securing funding; TGS: lead PI, overall study design, interpretation of data, securing funding, writing of the manuscript. All authors read and approved the final manuscript. We gratefully acknowledge critical input from Nicholas Martin, Scott Gordon, and Cynthia Bulik. The authors declare that they have no competing interests. The developed open-source software toolset RUDI can be downloaded and used with respect to version 3 of the GNU public licence (GPLv3). Users may distribute and individually adapt the source code. More details including an online tutorial are available at the official web site of RUDI at http://www.rudi-genetics.net. Not applicable. The study was performed under a protocol approved by the ethical committee of the University of Heidelberg (Medizinische Ethikkommission II). This study is part of the Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia (MooDS) network, which is funded by the Federal Ministry of Education and Research (BMBF) through the framework of National Genomic Research Network (NGFN) (Grant 01GS08144 to SC and MMN; Grant 01GS08147 to MR). MR was also supported by the Seventh Framework Program of the European Union (FP7/2007–2011) under grant agreement no. 242257 (ADAMS). MMN also received support from the Alfried Krupp von Bohlen und Halbach-Stiftung. LK, FJM, and TGS were also supported through Intramural Research Program of the National Institute of Mental Health (NIHM) at the National Institutes of Health (NIH) of the US Government. TGS is supported through grants from the Deutsche Forschungsgemeinschaft (DFG; SCHU 1603/4-1 & 5-1) and the Dr. Lisa Oehler Foundation (Kassel, Germany). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Funding Information:
This study is part of the Systematic Investigation of the Molecular Causes of Major Mood Disorders and Schizophrenia (MooDS) network, which is funded by the Federal Ministry of Education and Research (BMBF) through the framework of National Genomic Research Network (NGFN) (Grant 01GS08144 to SC and MMN; Grant 01GS08147 to MR). MR was also supported by the Seventh Framework Program of the European Union (FP7/2007–2011) under grant agreement no. 242257 (ADAMS). MMN also received support from the Alfried Krupp von Bohlen und Halbach‑Stiftung. LK, FJM, and TGS were also supported through Intramural Research Program of the National Institute of Mental Health (NIHM) at the National Institutes of Health (NIH) of the US Government. TGS is supported through grants from the Deutsche Forschun-gsgemeinschaft (DFG; SCHU 1603/4‑1 & 5‑1) and the Dr. Lisa Oehler Foundation (Kassel, Germany).

Publisher Copyright:
© 2018, The Author(s).

Keywords

  • Bipolar disorder
  • Data mining
  • Genotype–phenotype patterns
  • Rule discovery
  • Subphenotypes

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