Review: Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension

Yoonsuh Jung, Jianhua Hu

Research output: Contribution to journalReview article


A general modeling procedure for analyzing genetic data is reviewed. We review ANOVA type model that can handle both the continuous and discrete genetic variables in one modeling framework. Unlike the regression type models which typically set the phenotype variable as a response, this ANOVA model treats the phenotype variable as an explanatory variable. By reversely treating the phenotype variable, usual high dimensional problem is turned into low dimension. Instead, the ANOVA model always includes interaction term between the genetic locations and phenotype variable to find potential association between them. The interaction term is designed to be low rank with the multiplication of bilinear terms so that the required number of parameters is kept in a manageable degree. We compare the performance of the reviewed ANOVA model to the other popular methods via microarray and SNP data sets.

Original languageEnglish
Pages (from-to)169-178
Number of pages10
JournalJournal of the Korean Statistical Society
Issue number2
Publication statusPublished - 2019 Jun 1



  • BIC
  • High dimension
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability

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