TY - JOUR
T1 - Detecting significant genotype–phenotype association rules in bipolar disorder
T2 - market research meets complex genetics
AU - Breuer, René
AU - Mattheisen, Manuel
AU - Frank, Josef
AU - Krumm, Bertram
AU - Treutlein, Jens
AU - Kassem, Layla
AU - Strohmaier, Jana
AU - Herms, Stefan
AU - Mühleisen, Thomas W.
AU - Degenhardt, Franziska
AU - Cichon, Sven
AU - Nöthen, Markus M.
AU - Karypis, George
AU - Kelsoe, John
AU - Greenwood, Tiffany
AU - Nievergelt, Caroline
AU - Shilling, Paul
AU - Shekhtman, Tatyana
AU - Edenberg, Howard
AU - Craig, David
AU - Szelinger, Szabolcs
AU - Nurnberger, John
AU - Gershon, Elliot
AU - Alliey-Rodriguez, Ney
AU - Zandi, Peter
AU - Goes, Fernando
AU - Schork, Nicholas
AU - Smith, Erin
AU - Koller, Daniel
AU - Zhang, Peng
AU - Badner, Judith
AU - Berrettini, Wade
AU - Bloss, Cinnamon
AU - Byerley, William
AU - Coryell, William
AU - Foroud, Tatiana
AU - Guo, Yirin
AU - Hipolito, Maria
AU - Keating, Brendan
AU - Lawson, William
AU - Liu, Chunyu
AU - Mahon, Pamela
AU - McInnis, Melvin
AU - Murray, Sarah
AU - Nwulia, Evaristus
AU - Potash, James
AU - Rice, John
AU - Scheftner, William
AU - Zöllner, Sebastian
AU - McMahon, Francis J.
AU - Rietschel, Marcella
AU - Schulze, Thomas G.
N1 - 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).
PY - 2018/12/1
Y1 - 2018/12/1
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.
AB - 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 - Data mining
KW - Genotype–phenotype patterns
KW - Rule discovery
KW - Subphenotypes
UR - http://www.scopus.com/inward/record.url?scp=85056473948&partnerID=8YFLogxK
U2 - 10.1186/s40345-018-0132-x
DO - 10.1186/s40345-018-0132-x
M3 - Article
C2 - 30415424
AN - SCOPUS:85056473948
SN - 2194-7511
VL - 6
JO - International Journal of Bipolar Disorders
JF - International Journal of Bipolar Disorders
IS - 1
M1 - 24
ER -