preselection: preselection

In GWAS.BAYES: GWAS for Selfing Species

Description

Finds significant SNP's

Usage

preselection(Y,SNPs,number_cores,principal_components,frequentist,controlrate,threshold,nullprob,alterprob,kinship = FALSE,info = FALSE)

Arguments

Y	The reduced matrix of response values
SNPs	The reduced SNP matrix where the columns are either 1's or 0's.
number_cores	The number of cores on which you would like to parallize this procedure
principal_components	The reduced matrix of the principal components.
frequentist	A logical value to see whether one would like to use a frequentist multiple comparison test or Bayesian False Discovery based on BIC's. The value of this affects whether values of the next parameters are needed.
controlrate	Only used when frequentist = TRUE. This is for which multiple comparison method you would like to use. Examples are "bonferroni" and "BH". See p.adjust for a full list of methods.
threshold	The value at which type 1 error rate is held at. .05 in most common literature. Used when frequentist is TRUE or FALSE
nullprob	Used when frequentist = FALSE, the probability that is assigned to the null hypothesis.
alterprob	Used when frequentist = FALSE, the probability that is assigned to the alternate hypothesis.
kinship	The kinship matrix if a model with a kinship component is desired. If not set kinship = FALSE.
info	An information matrix where the first row is the chromosome information and the second row in the position information for the SNP's.

Value

Frequentist Matrix	The matrix of results when Frequentist = TRUE. The results are formatted as a data.frame with the column Significant being 1 or 0 depending on if the SNP was significant (1 for significant). The P_values column will be the p-values that were calculated for each SNP.
Bayesian Matrix	The matrix of results when Frequentist = FALSE. The results are formatted as a data.frame with the column Significant being 1 or 0 depending on if the SNP was significant (1 for significant). The ApprPosteriorProbs column will be the Approximate Posterior Probabilities that were calculated for each SNP.

Examples

data("vignette_lm_dat")
Y <- vignette_lm_dat$Phenotype
SNPs <- vignette_lm_dat[,-1]
fullPreprocess <- preprocess_SNPs(SNPs = SNPs,Y = Y,MAF = 0.01,number_cores = 1)
SNPs <- fullPreprocess$SNPs
Y <- fullPreprocess$Y
fullPreprocess$SNPs_Dropped
principal_comp <- pca_function(SNPs = SNPs,number_components = 1,plot_it = FALSE)

preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = principal_comp,frequentist = TRUE,controlrate = "bonferroni",threshold = .05,kinship = FALSE,info = FALSE)

GWAS.BAYES documentation built on Nov. 8, 2020, 7:47 p.m.