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Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics
Journal article   Open access   Peer reviewed

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 Mühleisen, Franziska Degenhardt, …
International Journal of Bipolar Disorders, Vol.6(1), pp.1-10
12/2018
DOI: 10.1186/s40345-018-0132-x
PMCID: PMC6230336
PMID: 30415424
url
https://doi.org/10.1186/s40345-018-0132-xView
Published (Version of record) Open Access

Abstract

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.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.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.
Behavioral Therapy Clinical Psychology Data Mining Neurology Psychiatry Psychopharmacology Psychotherapy Rule discovery Medicine & Public Health Bipolar disorder Genotype–phenotype patterns Subphenotypes

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