blupga_GBLUP: Standard GBLUP

In dkainer/BLUPGA: BLUP|GA genomic prediction model

Description

This function runs straightforward GBLUP using the rrBLUP package. This is the equivalent of BLUP|GA where W=0 (i.e. no SNPs are weighted)

Usage

blupga_GBLUP(G, phenodata, valset, verbose = T)

Arguments

G	GRM constructed using all available SNPs and all samples Defaults to NULL. Use make_GRM() to get this.
phenodata	data frame with 2 or 3 columns. One col must be named 'ID' and contain sample IDs. Another col must be named 'y' and contain the phenotypes. If fixed effects are included then a 3rd col called 'FE' should contain the categorical effects. Defaults to NULL.
valset	vector of indices that defines which rows in phenodata will be set to NA and used for cross-validation Defaults to NULL.

Value

a data frame containing the correlation between the predicted phenotype and the true phenotype of the individuals in the valset.

W

omega weighting for selected SNPS in candidate genes (0.0–1.0)

COR

cross validation predictive ability (0.0–1.0)

Examples

# get example genotype matrix and phenotype data
data(M)
data(pheno)

G <- make_GRM(M)

# choose a validation set of 20 random individuals
val <- sample(1:nrow(pheno), 20)
results <- blupga_GBLUP(G, pheno, val)

dkainer/BLUPGA documentation built on Jan. 3, 2020, 1:09 a.m.