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Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancer.

Lin Zhang1, Veerabhadran Baladandayuthapani2, Bani K Mallick1

  • 1Department of Statistics, Texas A&M University, College Station, Texas, U.S.A.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|February 24, 2015
PubMed
Summary

We developed a new Bayesian method to analyze copy number alterations, identifying key genetic markers for cancer risk stratification. This approach improves accuracy in breast cancer studies by considering gene and probe structures.

Keywords:
MCMCMIP datacopy number alterationhierarchical variable selectionlasso

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Area of Science:

  • Genomics
  • Biostatistics
  • Cancer Research

Background:

  • Copy number alterations (CNAs) are crucial genetic markers for cancer.
  • Molecular inversion probes (MIPs) generate high-dimensional data for CNA analysis.
  • Existing methods may not fully leverage the inherent structure of genomic data.

Purpose of the Study:

  • To introduce a novel Bayesian variable selection method, Hierarchical Structured Variable Selection (HSVS).
  • To identify important genes and probes associated with clinically relevant cancer outcomes.
  • To improve risk stratification and treatment guidance using genomic data.

Main Methods:

  • Developed the HSVS method for grouped variable selection, accounting for gene and probe-within-gene architecture.
  • Utilized discrete mixture prior for group selection and Bayesian lasso hierarchies for within-group variable selection.
  • Incorporated Bayesian fused lasso methods to handle serial correlations within groups.

Main Results:

  • Simulations demonstrated that HSVS yields lower model errors compared to other methods when natural grouping structures are present.
  • Applied to a breast cancer MIP study, HSVS successfully identified significant genes and probes.
  • The identified markers are associated with clinically relevant breast cancer subtypes.

Conclusions:

  • The HSVS method provides an effective approach for analyzing high-dimensional genomic data with inherent structures.
  • This method enhances the identification of genetic markers for cancer, particularly in breast cancer.
  • HSVS has the potential to improve patient risk stratification and guide therapeutic strategies.