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Int J Bipolar Disord. 2018 Nov 11;6(1):24. doi: 10.1186/s40345-018-0132-x.

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

Author information

1
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.
2
Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.
3
Institute of Human Genetics, University of Bonn, Bonn, Germany.
4
Center for Integrative Sequencing, iSEQ, Department of Biomedicine, Aarhus University, Aarhus, Denmark.
5
Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg, Germany.
6
Department for Biostatistics, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.
7
Human Genetics Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA.
8
Institute of Neuroscience and Medicine (INM-1), Structural and Functional Organisation of the Brain, Genomic Imaging, Research Centre Juelich, Juelich, Germany.
9
Department of Biomedicine, University of Basel, Basel, Switzerland.
10
Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA.
11
Department of Psychiatry, University of California San Diego, San Diego, USA.
12
BGI-Shenzhen, Beishan Industrial Zone, Yantian District, Shenzhen, China.
13
Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, USA.
14
The Translational Genomics Research Institute, Phoenix, USA.
15
Department of Psychiatry, Indiana University School of Medicine, Indianapolis, USA.
16
Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA.
17
Department of Mental Health, John Hopkins Bloomberg School of Public Health, Baltimore, USA.
18
Department of Psychiatry and Behavioral Sciences, John Hopkins School of Medicine, Baltimore, USA.
19
J. Craig Venter Institute, La Jolla, USA.
20
Scripps Genomic Medicine & The Scripps Translational Sciences Institute (STSI), La Jolla, USA.
21
Department of Pediatrics and Rady's Children's Hospital, School of Medicine, University of California San Diego, La Jolla, USA.
22
Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA.
23
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA.
24
Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.
25
University of California, San Diego, La Jolla, USA.
26
Department of Psychiatry, University of California at San Francisco, San Francisco, USA.
27
University of Iowa Hospitals and Clinics, Iowa City, USA.
28
Center for Applied Genomics, Children's Hospital of Philadelphia, Abramson Research Center, Philadelphia, USA.
29
Department of Psychiatry and Behavioral Sciences, Howard University Hospital, Washington, USA.
30
Cardiovascular Institute, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
31
Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
32
Dell Medical School, University of Texas at Austin, Austin, USA.
33
Department of Psychiatry, University of Illinois at Chicago, Chicago, USA.
34
Department of Psychiatry, University of Michigan, Ann Arbor, USA.
35
Department of Pathology, University of California San Diego, La Jolla, USA.
36
Department of Psychiatry, Carver College of Medicine, University of Iowa School of Medicine, Iowa City, USA.
37
Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, USA.
38
Rush University Medical Center, Chicago, USA.
39
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany. tschulze@med.lmu.de.
40
Human Genetics Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA. tschulze@med.lmu.de.
41
Department of Psychiatry and Psychotherapy, University of Göttingen, Göttingen, Germany. tschulze@med.lmu.de.
42
Institute of Psychiatric Phenomics and Genomics (IPPG), Ludwig-Maximilians-University, Munich, Nußbaumstr. 7, 80336, Munich, Germany. tschulze@med.lmu.de.

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.

KEYWORDS:

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

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