BICOSS: BICOSS for Gaussian Phenotypes

In marf-at-vt/GWAS.BAYES: Bayesian analysis of Gaussian GWAS data

BICOSS for Gaussian Phenotypes

Description

Performs BICOSS analysis as described in Williams, J., Ferreira, M.A.R. & Ji, T. BICOSS: Bayesian iterative conditional stochastic search for GWAS. BMC Bioinformatics 23, 475 (2022). https://doi.org/10.1186/s12859-022-05030-0.

Usage

BICOSS(
  Y,
  SNPs,
  FDR_Nominal = 0.05,
  kinship = diag(nrow(SNPs)),
  maxiterations = 400,
  runs_til_stop = 40,
  P3D = TRUE
)

Arguments

Y	The observed numeric phenotypes
SNPs	The SNP matrix, where each column represents a single SNP encoded as the numeric coding 0, 1, 2. This is entered as a matrix object.
FDR_Nominal	The nominal false discovery rate for which SNPs are selected from in the screening step.
kinship	The observed kinship matrix, has to be a square positive semidefinite matrix. Defaulted as the identity matrix. The function used to create the kinship matrix used in the BICOSS paper is A.mat() from package rrBLUP.
maxiterations	The maximum iterations the genetic algorithm in the model selection step iterates for. Defaulted at 400 which is the value used in the BICOSS paper simulation studies.
runs_til_stop	The number of iterations at the same best model before the genetic algorithm in the model selection step converges. Defaulted at 40 which is the value used in the BICOSS paper simulation studies.
P3D	Population previous determined, if TRUE BICOSS uses approximated variance parameters estimated from the baseline model when conducting both the screening and the model selection steps. Setting P3D = TRUE is significantly faster. If FALSE, uses exact estimates of the variance parameters all models in both the screening and model selection step.

Value

The column indices of SNPs that were in the best model identified by BICOSS.

Examples

library(GWAS.BAYES)
BICOSS(Y = Y, SNPs = SNPs, kinship = kinship,
    FDR_Nominal = 0.05,P3D = TRUE,
    maxiterations = 400,runs_til_stop = 40)

marf-at-vt/GWAS.BAYES documentation built on Jan. 31, 2023, 4:04 p.m.