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Gene set enrichment analysis for multiple continuous phenotypes.

Xiaoming Wang1, Saumyadipta Pyne, Irina Dinu

  • 1School of Public Health, University of Alberta, Edmonton, AB T6G 1C9, Canada. xiaoming@ualberta.ca.

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Summary
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New methods extend gene set analysis (GSA) for multiple continuous phenotypes, accounting for correlations. These approaches effectively test gene-set associations with complex phenotypes, improving upon univariate analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene set analysis (GSA) methods are widely used for single binary or categorical phenotypes.
  • Existing GSA methods inadequately address continuous phenotypes, especially multiple correlated ones.

Purpose of the Study:

  • To extend the linear combination test (LCT) for analyzing gene sets with multiple continuous phenotypes.
  • To develop a nonlinear version (NLCT) for detecting nonlinear associations between gene sets and multiple phenotypes.
  • To incorporate correlations among gene expressions and multiple phenotypes.

Main Methods:

  • Developed an extended linear combination test (LCT) for multiple continuous phenotypes.
  • Introduced a nonlinear extension (NLCT) to capture nonlinear gene set-phenotype associations.
  • Incorporated gene expression and phenotype correlations into the models.

Main Results:

  • The proposed LCT and NLCT methods effectively control type I errors.
  • The methods demonstrate power in testing associations between gene sets and multiple continuous phenotypes.
  • Simulation studies and a real microarray example validate the practical utility of the methods.

Conclusions:

  • The developed LCT and NLCT approaches are computationally efficient and effective for gene set-phenotype association analysis.
  • Univariate analysis of multiple correlated phenotypes can be misleading.
  • R-codes for LCT and NLCT are publicly available for researchers.