Set association analysis of SNP case-control and microarray data

ABSTRACT

Common heritable diseases ("complex traits") are assumed to be due to multiple underlying susceptibility genes. While genetic mapping methods for mendelian disorders have been very successful, the search for genes underlying complex traits has been difficult and often disappointing. One of the reasons may be that most current gene mapping approaches are still based on conventional methodology of testing one or a few SNPs at a time. Here we demonstrate a simple strategy that allows for the joint analysis of multiple disease-associated SNPs in different genomic regions. Our set-association method combines information over SNPs by forming sums of relevant single-marker statistics. This approach successfully addresses the "curse of dimensionality" problem - too many variables should be estimated with a comparatively small number of observations. We also extend our method to microarray expression data, where expression levels for large numbers of genes should be compared between two tissue types. In applications to experimental expression data our approach turned out to be highly efficient.

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Probabilistic algorithms (including Monte Carlo)

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Statistical computing

General Terms:
Algorithms, Theory

Keywords:
association, case control study, expression, microarray