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A Two-Stage Random Forest-Based Pathway Analysis Method

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  • Ren-Hua Chung
  • Ying-Erh Chen

Abstract

Pathway analysis provides a powerful approach for identifying the joint effect of genes grouped into biologically-based pathways on disease. Pathway analysis is also an attractive approach for a secondary analysis of genome-wide association study (GWAS) data that may still yield new results from these valuable datasets. Most of the current pathway analysis methods focused on testing the cumulative main effects of genes in a pathway. However, for complex diseases, gene-gene interactions are expected to play a critical role in disease etiology. We extended a random forest-based method for pathway analysis by incorporating a two-stage design. We used simulations to verify that the proposed method has the correct type I error rates. We also used simulations to show that the method is more powerful than the original random forest-based pathway approach and the set-based test implemented in PLINK in the presence of gene-gene interactions. Finally, we applied the method to a breast cancer GWAS dataset and a lung cancer GWAS dataset and interesting pathways were identified that have implications for breast and lung cancers.

Suggested Citation

  • Ren-Hua Chung & Ying-Erh Chen, 2012. "A Two-Stage Random Forest-Based Pathway Analysis Method," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-6, May.
    • Handle: RePEc:plo:pone00:0036662
    DOI: 10.1371/journal.pone.0036662
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    References listed on IDEAS

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    1. Hariklia Eleftherohorinou & Victoria Wright & Clive Hoggart & Anna-Liisa Hartikainen & Marjo-Riitta Jarvelin & David Balding & Lachlan Coin & Michael Levin, 2009. "Pathway Analysis of GWAS Provides New Insights into Genetic Susceptibility to 3 Inflammatory Diseases," PLOS ONE, Public Library of Science, vol. 4(11), pages 1-11, November.
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