postGWAS_Haplotype: postGWAS_Haplotype
In GWAS.BAYES: GWAS for Selfing Species

Description Usage Arguments Details Value Examples

Performs GA model selection to identify the best model

1	postGWAS_Haplotype(Y,SNPs,info,size = 10,significant,number_cores,principal_components,maxiterations,runs_til_stop,kinship = FALSE)

`Y`	The phenotype response on the reduced scale (aggregating phenotype by ecotype/taxa), this should be a matrix with 1 column.
`SNPs`	The SNP matrix on the reduced scale (aggregating phenotype by ecotype/taxa).
`info`	A dataframe where the first row is the chromosome info for the SNP's and the second is the location of each SNP represented by its base pair.
`size`	The number of kilobase pairs to search for regions.
`significant`	A vector of 0 and 1's where the 1's indicate what SNP's were found to be significant in the preselection function.
`number_cores`	Number of cores to be passed on to the genetic algorithm to increase computational speed.
`principal_components`	The principal component matrix on the reduced scale (aggregating phenotype by ecotype/taxa).
`maxiterations`	This is the maximum number of iterations the Genetic Search algorithm will run.
`runs_til_stop`	This is the number of consecutive iterations where the BIC is not improved before the genetic algorithm is stopped.
`kinship`	Default is set at FALSE. If kinship model is desired, input a kinship matrix and this will search models with the kinship component.

This function will print out lines corresponding to the convergence of the genetic search algorithm.

A named matrix where the names correspond to the significant SNP's. This will usually out a matrix with a singular row, where the values of this row is 0 or 1 where 1 indicates significance in the final model and 0 indicates non significance in the final model. Sometimes this will output a matrix with multiple columns. This is because there is a SNP or multiple SNPs that when added to the model create rank deficiency issues in the model. Naturally rank deficient columns are forced out but the genetic algorithm is not smart enough to sort these.

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$Y
data("RealDataInfo")
RealDataInfo <- RealDataInfo[,-fullPreprocess$SNPs_Dropped]
data("RealDataKinship")
kinship <- as.matrix(RealDataKinship)
Significant_SNPs <- preselection(Y = log(Y), SNPs = SNPs,number_cores = 1, principal_components = FALSE,frequentist = TRUE,controlrate = "bonferroni",threshold = .05,kinship = kinship, info = RealDataInfo)

#postGWAS_Haplotype(Y = log(Y),SNPs = SNPs,info = RealDataInfo,size = 10,number_cores = 1, significant = Significant_SNPs$Significant,principal_components = FALSE,kinship = kinship)