Brooke L Fridley, PhD

Department of Biostatistics

Software

Analysis code and software for genomic studies

• Code from Fridley BL. Bayesian variable and model selection methods for genetic association studies. Genetic Epidemiololgy 2009;33:27.
  • Code for stochastic search variable selection
    SSVS_WinBUGS.txt
  • Code for Bayesian variable selection using reversible JUMP MCMC
    BVS_WinBUGS.txt
• Code for Bayesian hierarchical nonlinear model as outlined in Fridley BL, et al. A Bayesian hierarchical nonlinear model for assessing the association between genetic variation and drug cytotoxicity. Statistics in Medicine. 2009;28;2709.
  • HNLM_WinBUGS.txt
• Code for Bayesian hierarchical models for association of multiple markers within a gene to localize the "putative" allele: Fridley BL, et al. Localizing putative markers in genetic association studies by incorporating linkage disequilibrium into bayesian hierarchical models. Human Heredity. 2010;70:63.
  • Hierarchical_NoLD_WinBUGS.txt (Model 1)
  • Hierarchical_NoLD_gene_WinBUGS.txt (Model 2)
  • Hierarchical_LD_WinBUGS.txt (Model 3)
  • Hierarchical_LD_gene_WinBUGS.txt (Model 4)
• Code for Bayesian mixture model outlined in Fridley BL, et al. Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies. Genetic Epidemiology. 2010;34:418.
  • Mixture_Model_WinBUGS.txt
• Documentation and R code for SCCA with SCAD penalty as described in Chalise P, et al. Comparison of penalty functions for sparse canonical correlation analysis. Computational Statistics and Data Analysis. 2012;56:245.
  • SCAD_Documentation.pdf
• GsaPCgamma is an R package that contains the function PCgamma, which completes a self-contained gene set analysis for SNP data using this two-step procedure. 1. Principal components analysis for SNPs within a gene is completed with the components needed to explain 80 percent of the variation retained. Using these components, a gene-level association test is completed to determine the association of the gene with the phenotype. 2. The gene-level p values for genes within a given gene set are combined using the Gamma Method, a variation of Fisher's Method, to determine the association of the gene set with the phenotype. In using the Gamma Method, a soft truncation threshold (STT) must be specified (that is, shape parameter for gamma distribution). For combining p values using Fisher's method, set STT to 1/e. Based on simulation studies, we have found that STT between 0.10 and 0.20 achieve optimal power for a variety of situations. Empirical p values for the gene set association are determined via permutations. Biernacka JM, et al. Use of the gamma method for self-contained gene-set analysis of SNP data. European Journal of Human Genetics. 2011 (in press).
  • Windows_gsaPCgamma_1.0.zip
  • UNIX_gsaPCgamma.tar.gz
• Code for Bayesian models for incorporation of pathway topology into association studies involving mRNA expression measured for genes within a pathway with a quantitative phenotype. Brisbin A, et al. Bayesian genomic models for the incorporation of pathway topology knowledge into association studies. (Manuscript under revision.)
  • Pathway Prior Model
• Aggregating of information across multiple variants in a gene or region can improve power for rare variant association testing. This package contains the difference in minor allele frequency (DMAF) test for a region. The DMAF test allows combined analysis of common and rare variants and makes no assumptions about the direction of effects. In addition, it contains a sliding-window based approach, which allows focused analysis to localize the association signal. A step-down permutation approach is used as a multiple test correction to control the type I error with the testing of multiple windows. (Manuscript under review.)
  • DMAF_1.0.zip
  • DMAF_1.0.tar.gz