## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", tidy = FALSE ) ## ---- warning=FALSE,message=FALSE--------------------------------------------- library(GWAS.BAYES) ## ----------------------------------------------------------------------------- data("vignette_lm_dat") head(vignette_lm_dat[,1:10]) ## ----------------------------------------------------------------------------- Y <- vignette_lm_dat$Phenotype SNPs <- vignette_lm_dat[,-1] ## ----------------------------------------------------------------------------- SNPs <- standardize(SNPs = SNPs,method = "major-minor",number_cores = 1) SNPs[1:6,1:10] ## ----------------------------------------------------------------------------- aggregate_list <- aggregate_SNPs(SNPs = SNPs, Y = Y) SNPs <- aggregate_list$SNPs Y <- aggregate_list$Y ## ----------------------------------------------------------------------------- level_list <- level_function(SNPs = SNPs, MAF = 0.01) SNPs <- level_list$SNPs level_list$SNPs_Dropped dim(SNPs) ## ----------------------------------------------------------------------------- fullPreprocess <- preprocess_SNPs(SNPs = vignette_lm_dat[,-1], Y = vignette_lm_dat$Phenotype,MAF = 0.01,number_cores = 1, na.rm = FALSE) all.equal(fullPreprocess$SNPs,SNPs) all.equal(fullPreprocess$Y,Y) fullPreprocess$SNPs_Dropped;level_list$SNPs_Dropped ## ----fig.cap="Percent Variation in the Response explained by the principal components",fig.wide = TRUE, fig.align="center", fig.width=6.75, fig.height=4.75---- principal_comp <- pca_function(SNPs = SNPs,number_components = 3, plot_it = TRUE) ## ----------------------------------------------------------------------------- principal_comp <- as.matrix(principal_comp[,1]) ## ----------------------------------------------------------------------------- Significant_SNPs_Bonf <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = principal_comp,frequentist = TRUE, controlrate = "bonferroni",threshold = .05,kinship = FALSE, info = FALSE) #Bonferroni Correction sum(Significant_SNPs_Bonf$Significant) #Five Significant SNPs which(Significant_SNPs_Bonf$Significant == 1) ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- resids_diag(Y = Y,SNPs = SNPs, significant = Significant_SNPs_Bonf$Significant, kinship = FALSE,principal_components = principal_comp, plot_it = TRUE) ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- cor_plot(SNPs = SNPs, significant = Significant_SNPs_Bonf$Significant, info = FALSE) ## ----------------------------------------------------------------------------- Significant_SNPs_FDR <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = principal_comp,frequentist = TRUE, controlrate = "BH",threshold = .05,kinship = FALSE,info = FALSE) sum(Significant_SNPs_FDR$Significant) #Five Significant SNPs which(Significant_SNPs_FDR$Significant == 1) ## ----------------------------------------------------------------------------- Significant_SNPs_BFDR <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = principal_comp,frequentist = FALSE,nullprob = .5, alterprob = .5,threshold = .05,kinship = FALSE,info = FALSE) sum(Significant_SNPs_BFDR$Significant) #Five Significant SNPs which(Significant_SNPs_BFDR$Significant == 1) ## ----------------------------------------------------------------------------- GA_results <- postGWAS(Y = Y,SNPs = SNPs,number_cores = 1, significant = Significant_SNPs_Bonf$Significant, principal_components = principal_comp,maxiterations = 100, runs_til_stop = 10,kinship = FALSE,info = FALSE) GA_results ## ----------------------------------------------------------------------------- data("vignette_kinship_dat") head(vignette_kinship_dat[,1:10]) ## ----------------------------------------------------------------------------- Y <- vignette_kinship_dat$Phenotype SNPs <- vignette_kinship_dat[,-1] fullPreprocess <- preprocess_SNPs(SNPs = SNPs, Y = Y, MAF = 0.01, number_cores = 1,na.rm = FALSE) SNPs <- fullPreprocess$SNPs Y <- fullPreprocess$Y fullPreprocess$SNPs_Dropped ## ----------------------------------------------------------------------------- library(rrBLUP,quietly = TRUE) k <- A.mat(SNPs,n.core = 1) dim(k) ## ----------------------------------------------------------------------------- Significant_SNPs_Bonf <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = FALSE,frequentist = TRUE,controlrate = "bonferroni", threshold = .05,kinship = k, info = FALSE) sum(Significant_SNPs_Bonf$Significant)#Four Significant SNPs which(Significant_SNPs_Bonf$Significant == 1) ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- resids_diag(Y = Y,SNPs = SNPs,significant = Significant_SNPs_Bonf$Significant, kinship = k,principal_components = FALSE,plot_it = TRUE) ## ----------------------------------------------------------------------------- Significant_SNPs_FDR <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = FALSE,frequentist = TRUE,controlrate = "BH", threshold = .05,kinship = k, info = FALSE) sum(Significant_SNPs_FDR$Significant) #Six Significant SNPs which(Significant_SNPs_FDR$Significant == 1) ## ----------------------------------------------------------------------------- Significant_SNPs_BFDR <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = FALSE,frequentist = FALSE,nullprob = .5, alterprob = .5,threshold = .05,kinship = k, info = FALSE) sum(Significant_SNPs_BFDR$Significant) #4 Significant SNPs which(Significant_SNPs_BFDR$Significant == 1) ## ----------------------------------------------------------------------------- GA_results <- postGWAS(Y = Y,SNPs = SNPs,number_cores = 1, significant = Significant_SNPs_FDR$Significant, principal_components = FALSE,kinship = k, info = FALSE) GA_results ## ----------------------------------------------------------------------------- data("RealDataSNPs_Y") Y <- RealDataSNPs_Y$Phenotype SNPs <- subset(RealDataSNPs_Y,select = -c(Phenotype)) fullPreprocess <- preprocess_SNPs(SNPs = SNPs,Y = Y, MAF = 0.01,number_cores = 1,na.rm = FALSE) SNPs <- fullPreprocess$SNPs Y <- fullPreprocess$Y fullPreprocess$SNPs_Dropped ## ----------------------------------------------------------------------------- data("RealDataInfo") head(RealDataInfo[,1:6]) ## ----------------------------------------------------------------------------- RealDataInfo <- RealDataInfo[,-fullPreprocess$SNPs_Dropped] ## ----------------------------------------------------------------------------- data("RealDataKinship") kinship <- as.matrix(RealDataKinship) ## ----------------------------------------------------------------------------- Significant_SNPs <- preselection(Y = Y, SNPs = SNPs,number_cores = 1, principal_components = FALSE,frequentist = TRUE,controlrate = "bonferroni", threshold = .05,kinship = kinship, info = RealDataInfo) sum(Significant_SNPs$Significant)#11 Significant SNPs Significant_SNPs[Significant_SNPs$Significant == 1,c(1,2)] ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- resids_diag(Y = Y, SNPs = SNPs, significant = Significant_SNPs$Significant, kinship = kinship) ## ----------------------------------------------------------------------------- Significant_SNPs <- preselection(Y = log(Y), SNPs = SNPs,number_cores = 1, principal_components = FALSE,frequentist = TRUE,controlrate = "bonferroni", threshold = .05,kinship = kinship,info = RealDataInfo) sum(Significant_SNPs$Significant)#4 Significant SNPs Significant_SNPs[Significant_SNPs$Significant == 1,c(1,2)] ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- resids_diag(Y = log(Y), SNPs = SNPs, significant = Significant_SNPs$Significant,kinship = kinship) ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75,warning=FALSE,message=FALSE---- library(qqman,quietly = TRUE) Significant_SNPs <- cbind(Significant_SNPs,paste0("rs",1:nrow(Significant_SNPs))) colnames(Significant_SNPs) <- c(colnames(Significant_SNPs)[1:4],"SNPs") manhattan(Significant_SNPs,chr = "Chromosomes",bp = "Positions", p = "P_values",snp = "SNPs",suggestiveline = FALSE, genomewideline = -log10(.05/nrow(Significant_SNPs))) ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- qq(Significant_SNPs$P_values) ## ----------------------------------------------------------------------------- GA_results <- postGWAS(Y = log(Y),SNPs = SNPs,number_cores = 1, significant = Significant_SNPs$Significant,principal_components = FALSE, kinship = kinship,info = RealDataInfo) GA_results ## ---- fig.wide = TRUE,fig.align="center", fig.width=6.75, fig.height=4.75----- cor_plot(SNPs = SNPs[,Significant_SNPs$Significant == 1], significant = c(1,1,1,1), info = RealDataInfo[,Significant_SNPs$Significant == 1]) ## ----------------------------------------------------------------------------- 1 - colMeans(SNPs[,Significant_SNPs$Significant == 1]) ## ----------------------------------------------------------------------------- which(Significant_SNPs$Significant == 1) ## ----------------------------------------------------------------------------- summary(1 - colMeans(SNPs[,801:852]))