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

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...

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Published inInternational Journal of Bipolar Disorders Vol. 6; no. 1; pp. 24 - 10
Main Authors Breuer, René, Mattheisen, Manuel, Frank, Josef, Krumm, Bertram, Treutlein, Jens, Kassem, Layla, Strohmaier, Jana, Herms, Stefan, Mühleisen, Thomas W., Degenhardt, Franziska, Cichon, Sven, Nöthen, Markus M., Karypis, George, Kelsoe, John, Greenwood, Tiffany, Nievergelt, Caroline, Shilling, Paul, Shekhtman, Tatyana, Edenberg, Howard, Craig, David, Szelinger, Szabolcs, Nurnberger, John, Gershon, Elliot, Alliey-Rodriguez, Ney, Zandi, Peter, Goes, Fernando, Schork, Nicholas, Smith, Erin, Koller, Daniel, Zhang, Peng, Badner, Judith, Berrettini, Wade, Bloss, Cinnamon, Byerley, William, Coryell, William, Foroud, Tatiana, Guo, Yirin, Hipolito, Maria, Keating, Brendan, Lawson, William, Liu, Chunyu, Mahon, Pamela, McInnis, Melvin, Murray, Sarah, Nwulia, Evaristus, Potash, James, Rice, John, Scheftner, William, Zöllner, Sebastian, McMahon, Francis J., Rietschel, Marcella, Schulze, Thomas G.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 11.11.2018
Springer Nature B.V
SpringerOpen
Subjects
Behavioral Therapy
Bipolar disorder
Clinical Psychology
Data mining
Genetic research
Genomes
Genotype & phenotype
Genotype–phenotype patterns
Medicin och hälsovetenskap
Medicine
Medicine & Public Health
Neurology
Psychiatry
Psychopharmacology
Psychotherapy
Rule discovery
Subphenotypes
Rule discovery
Bipolar disorder
Genotype–phenotype patterns
Subphenotypes
Data mining
Online AccessGet full text

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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.
AbstractList 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.
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.
BackgroundDisentangling 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.ResultsTwo 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.ConclusionOur 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.
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.
ArticleNumber 24
Author Gershon, Elliot
Goes, Fernando
Kelsoe, John
Strohmaier, Jana
Greenwood, Tiffany
Breuer, René
Edenberg, Howard
Frank, Josef
Kassem, Layla
Hipolito, Maria
Alliey-Rodriguez, Ney
Treutlein, Jens
Bloss, Cinnamon
Keating, Brendan
McInnis, Melvin
Shekhtman, Tatyana
Herms, Stefan
Rice, John
Scheftner, William
Berrettini, Wade
Mattheisen, Manuel
Zandi, Peter
Nwulia, Evaristus
Degenhardt, Franziska
Krumm, Bertram
McMahon, Francis J.
Nievergelt, Caroline
Nurnberger, John
Guo, Yirin
Zöllner, Sebastian
Szelinger, Szabolcs
Zhang, Peng
Karypis, George
Shilling, Paul
Foroud, Tatiana
Mühleisen, Thomas W.
Mahon, Pamela
Lawson, William
Smith, Erin
Murray, Sarah
Rietschel, Marcella
Koller, Daniel
Cichon, Sven
Schork, Nicholas
Liu, Chunyu
Byerley, William
Coryell, William
Potash, James
Nöthen, Markus M.
Craig, David
Badner, Judith
Schulze, Thomas G.
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Issue 1
Keywords Rule discovery
Bipolar disorder
Genotype–phenotype patterns
Subphenotypes
Data mining
Language English
License Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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PublicationTitle International Journal of Bipolar Disorders
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Snippet Background Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide...
Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association...
BackgroundDisentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide...
BACKGROUNDDisentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide...
Abstract Background Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several...
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StartPage 24
SubjectTerms Behavioral Therapy
Bipolar disorder
Clinical Psychology
Data mining
Genetic research
Genomes
Genotype & phenotype
Genotype–phenotype patterns
Medicin och hälsovetenskap
Medicine
Medicine & Public Health
Neurology
Psychiatry
Psychopharmacology
Psychotherapy
Rule discovery
Subphenotypes
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Title Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics
URI https://link.springer.com/article/10.1186/s40345-018-0132-x
https://www.ncbi.nlm.nih.gov/pubmed/30415424
https://www.proquest.com/docview/2131650397/abstract/
https://search.proquest.com/docview/2132258856
https://pubmed.ncbi.nlm.nih.gov/PMC6230336
http://kipublications.ki.se/Default.aspx?queryparsed=id:139546663
https://doaj.org/article/eebc34bafd184ebf9661b11b72caa06e
Volume 6
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