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Cluster-Localized Sparse Logistic Regression for SNP Data

Author

Listed:
  • Binder Harald

    (Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz)

  • Müller Tina

    (Global Drug Discovery Statistics, Bayer Pharma AG)

  • Schwender Holger

    (Faculty of Statistics, TU Dortmund University)

  • Golka Klaus

    (Department of Toxicology, IfADo - Leibniz Research Centre for Working Environment and Human Factors)

  • Steffens Michael

    (Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz)

  • Hengstler Jan G.

    (Department of Toxicology, IfADo - Leibniz Research Centre for Working Environment and Human Factors)

  • Ickstadt Katja

    (Faculty of Statistics, TU Dortmund)

  • Schumacher Martin

    (Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg)

Abstract

The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, these groups of individuals are identified using a clustering approach, where each group may be defined via different SNPs. This allows for representing complex interaction patterns, such as compositional epistasis, that might not be detected by a single main effects model. In a simulation study, the CLR approach results in improved prediction performance, compared to the main effects approach, and identification of important SNPs in several scenarios. Improved prediction performance is also obtained for an application example considering urinary bladder cancer. Some of the identified SNPs are predictive for all individuals, while others are only relevant for a specific group. Together with the sets of SNPs that define the groups, potential interaction patterns are uncovered.

Suggested Citation

  • Binder Harald & Müller Tina & Schwender Holger & Golka Klaus & Steffens Michael & Hengstler Jan G. & Ickstadt Katja & Schumacher Martin, 2012. "Cluster-Localized Sparse Logistic Regression for SNP Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, August.
  • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:4:n:13
    DOI: 10.1515/1544-6115.1694
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    References listed on IDEAS

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    Cited by:

    1. Stefanie Hieke & Axel Benner & Richard F Schlenk & Martin Schumacher & Lars Bullinger & Harald Binder, 2016. "Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.

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