Manuel Mattheisen

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Standard

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

In: International Journal of Bipolar Disorders, Vol. 6, No. 1, 24, 01.12.2018.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

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 2018, 'Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics', International Journal of Bipolar Disorders, vol. 6, no. 1, 24. https://doi.org/10.1186/s40345-018-0132-x

APA

Breuer, R., Mattheisen, M., Frank, J., Krumm, B., Treutlein, J., Kassem, L., Strohmaier, J., Herms, S., Mühleisen, T. W., Degenhardt, F., Cichon, S., Nöthen, M. M., Karypis, G., Kelsoe, J., Greenwood, T., Nievergelt, C., Shilling, P., Shekhtman, T., Edenberg, H., ... Schulze, T. G. (2018). Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics. International Journal of Bipolar Disorders, 6(1), [24]. https://doi.org/10.1186/s40345-018-0132-x

CBE

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. 2018. Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics. International Journal of Bipolar Disorders. 6(1):Article 24. https://doi.org/10.1186/s40345-018-0132-x

MLA

Vancouver

Breuer R, Mattheisen M, Frank J, Krumm B, Treutlein J, Kassem L et al. Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics. International Journal of Bipolar Disorders. 2018 Dec 1;6(1). 24. https://doi.org/10.1186/s40345-018-0132-x

Author

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. / Detecting significant genotype–phenotype association rules in bipolar disorder : market research meets complex genetics. In: International Journal of Bipolar Disorders. 2018 ; Vol. 6, No. 1.

Bibtex

@article{1e4b3439b67c4afd9b99d1b4953111c3,
title = "Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics",
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",
author = "Ren{\'e} Breuer and Manuel Mattheisen and Josef Frank and Bertram Krumm and Jens Treutlein and Layla Kassem and Jana Strohmaier and Stefan Herms and M{\"u}hleisen, {Thomas W.} and Franziska Degenhardt and Sven Cichon and N{\"o}then, {Markus M.} and George Karypis and John Kelsoe and Tiffany Greenwood and Caroline Nievergelt and Paul Shilling and Tatyana Shekhtman and Howard Edenberg and David Craig and Szabolcs Szelinger and John Nurnberger and Elliot Gershon and Ney Alliey-Rodriguez and Peter Zandi and Fernando Goes and Nicholas Schork and Erin Smith and Daniel Koller and Peng Zhang and Judith Badner and Wade Berrettini and Cinnamon Bloss and William Byerley and William Coryell and Tatiana Foroud and Yirin Guo and Maria Hipolito and Brendan Keating and William Lawson and Chunyu Liu and Pamela Mahon and Melvin McInnis and Sarah Murray and Evaristus Nwulia and James Potash and John Rice and William Scheftner and Sebastian Z{\"o}llner and McMahon, {Francis J.} and Marcella Rietschel and Schulze, {Thomas G.}",
year = "2018",
month = dec,
day = "1",
doi = "10.1186/s40345-018-0132-x",
language = "English",
volume = "6",
journal = "International Journal of Bipolar Disorders",
issn = "2194-7511",
publisher = "Springer Open",
number = "1",

}

RIS

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.

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 - Journal article

C2 - 30415424

AN - SCOPUS:85056473948

VL - 6

JO - International Journal of Bipolar Disorders

JF - International Journal of Bipolar Disorders

SN - 2194-7511

IS - 1

M1 - 24

ER -