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Multiple Testing for SNP-SNP Interactions

Author

Listed:
  • Boulesteix Anne-Laure

    (Sylvia Lawry Centre and Institute for Medical Statistics and Epidemiology, Technical University of Munich)

  • Strobl Carolin

    (Department of Statistics, University of Munich)

  • Weidinger Stefan

    (Department of Dermatology and Allergy Biederstein, Technical University of Munich)

  • Wichmann H.-Erich

    (Department of Epidemiology, GSF)

  • Wagenpfeil Stefan

    (Institute for Medical Statistics and Epidemiology, Technical University of Munich)

Abstract

Most genetic diseases are complex, i.e. associated to combinations of SNPs rather than individual SNPs. In the last few years, this topic has often been addressed in terms of SNP-SNP interaction patterns given as expressions linked by logical operators. Methods for multiple testing in high-dimensional settings can be applied when many SNPs are considered simultaneously. However, another less well-known multiple testing problem arises within a fixed subset of SNPs when the logic expression is chosen optimally. In this article, we propose a general asymptotic approach for deriving the distribution of the maximally selected chi-square statistic in various situations. We show how this result can be used for testing logic expressions - in particular SNP-SNP interaction patterns - while controlling for multiple comparisons. Simulations show that our method provides multiple testing adjustments when the logic expression is chosen such as to maximize the statistic. Its benefit is demonstrated through an application to a real dataset from a large population-based study considering allergy and asthma in KORA. An implementation of our method is available from the Comprehensive R Archive Network (CRAN) as R package 'SNPmaxsel'.

Suggested Citation

  • Boulesteix Anne-Laure & Strobl Carolin & Weidinger Stefan & Wichmann H.-Erich & Wagenpfeil Stefan, 2007. "Multiple Testing for SNP-SNP Interactions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-24, December.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:37
    DOI: 10.2202/1544-6115.1315
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    Citations

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    Cited by:

    1. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, January.
    2. Elsäßer Amelie & Victor Anja & Hommel Gerhard, 2011. "Multiple Testing in Candidate Gene Situations: A Comparison of Classical, Discrete, and Resampling-Based Procedures," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-21, November.
    3. Nunkesser, Robin & Bernholt, Thorsten & Schwender, Holger & Ickstadt, Katja & Wegener, Ing, 2007. "Detecting high-order interactions of single nucleotide polymorphisms using genetic programming," Technical Reports 2007,24, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Malina Magdalena & Ickstadt Katja & Schwender Holger & Posch Martin & Bogdan Małgorzata, 2014. "Detection of epistatic effects with logic regression and a classical linear regression model," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 83-104, February.

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