Breuer R, Mattheisen M, Frank J, Krumm B, Treutlein J, Kassem L, Strohmaier J, Herms S, Mühleisen TW, Degenhardt F, Cichon S, Nöthen MM, Karypis G, Kelsoe J, Greenwood T, Nievergelt C, Shilling P, Shekhtman T, Edenberg H, Craig D, Szelinger S, Nurnberger J, Gershon E, Alliey-Rodriguez N, Zandi P, Goes F, Schork N, Smith E, Koller D, Zhang P, Badner J, Berrettini W, Bloss C, Byerley W, Coryell W, Foroud T, Guo Y, Hipolito M, Keating B, Lawson W, Liu C, Mahon P, McInnis M, Murray S, Nwulia E, Potash J, Rice J, Scheftner W, Zöllner S, McMahon FJ, Rietschel M, Schulze TG
Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics.
International journal of bipolar disorders. 2018-11-11; 6.1: 24.
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.