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      Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA

      Jung, Yoonsuh; Huang, Jianhua; Hu, Jianhua
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      01621459%2E2014%2E928217.pdf
      Accepted version, 1.731Mb
      DOI
       10.1080/01621459.2014.928217
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      Jung, Y., Huang, J., & Hu, J. (2014). Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA. Journal of the American Statistical Association, 109(508), 1355–1367. http://doi.org/10.1080/01621459.2014.928217
      Permanent Research Commons link: https://hdl.handle.net/10289/8899
      Abstract
      In genome-wide association studies, the primary task is to detect biomarkers in the form of Single Nucleotide Polymorphisms (SNPs) that have nontrivial associations with a disease phenotype and some other important clinical/environmental factors. However, the extremely large number of SNPs comparing to the sample size inhibits application of classical methods such as the multiple logistic regression. Currently the most commonly used approach is still to analyze one SNP at a time. In this pa- per, we propose to consider the genotypes of the SNPs simultaneously via a logistic analysis of variance (ANOVA) model, which expresses the logit transformed mean of SNP genotypes as the summation of the SNP effects, effects of the disease phenotype and/or other clinical variables, and the interaction effects. We use a reduced-rank representation of the interaction-effect matrix for dimensionality reduction, and employ the L1-penalty in a penalized likelihood framework to filter out the SNPs that have no associations. We develop a Majorization-Minimization algorithm for computational implementation. In addition, we propose a modified BIC criterion to select the penalty parameters and determine the rank number. The proposed method is applied to a Multiple Sclerosis data set and simulated data sets and shows promise in biomarker detection.
      Date
      2014-06-12
      Type
      Journal Article
      Publisher
      Taylor & Francis
      Rights
      This is an author’s accepted version of an article published in the journal: Journal of the American Statistical Association. © 2014 Taylor & Francis.
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      • Computing and Mathematical Sciences Papers [1452]
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