genomicEBV.w.codata {CodataGS}

Performs genomic prediction based on SNP codata.

Description

The main function of the package. The input includes response values, a design matrix for the fixed effects, a matrix with SNP genotype data and a design matrix for the SNP codata.

Usage

genomicEBV.w.codata(y, X, Z, X.SNPcodata, Z.test = NULL, max.iter = 100, conv.crit = 1e-5)

Arguments

y

Response values

X

Design matrix for the fixed effects

Z

Genotype matrix with element values of 0, 1 or 2

X.SNPcodata

Design matrix for the linear predictor of the SNP variances.

Z.test

An optional genotype matrix for a test data set.

max.iter

The maximum number of iterations

conv.crit

The value of the convergence criterion.

Details

By specifying the matrix Z.test in the input, the function computes predicted genomic breeding values for an out-of-sample data set.

Value

gEBV

Genomic breeding values

predicted.gEBV

Genomic breeding values based on the genotypes in Z.test

w

Computed SNP weights

u

Fitted SNP effects

beta

Fitted fixed effects

disp.beta

Fitted coefficients in the linear predictor for the SNP variance model

Converge

Shows whether the algorithm has converged or not

iter

The number of iterations used

Author(s)

Lars Ronnegard

Examples

#######
#Simulation part
set.seed(1234)
N <- 200 #Number of individuals
k <- 300 #Number of SNPs with all marker positions including a QTL
Z1 <- matrix(0, N, k )
Z2 <- matrix(0, N, k )
Z1[1:N, 1] <- rbinom(N, 1, 0.5) #Simulated phased SNP matrices
Z2[1:N, 1] <- rbinom(N, 1, 0.5) 
LD.par <- 0.2 #A parameter to simulate LD. 0 gives full LD, and 0.5 no LD
for (j in 2:k) {
  Z1[1:N, j] <- abs( Z1[1:N, j-1] - rbinom(N, 1, LD.par) )
  Z2[1:N, j] <- abs( Z2[1:N, j-1] - rbinom(N, 1, LD.par) )
} 
Z <- Z1 + Z2 #Genotypic SNP matrix
x1 <- c(rep(1,k/2), rep(0,k/2)) #An indicator for the SNPs. 
#The first k/2 SNPs and the last k/2 have different variances
#Simulate linear predictor for the random effect variance
lin.pred <- 0 + 2*x1 
X.snp <- model.matrix( ~ x1 ) #Corresponding design matrix
u <- rnorm(k, 0 , sqrt( exp(lin.pred) )) 
#Took the square root here because it is the SD that is specified.
#and exp() because we are modelling a log link.
u.scaled <- u/as.numeric( sqrt( var( crossprod(t(Z), u) )) ) 
#Scaled by the variance of the breeding values
e <- rnorm(N) #A residual variance
mu <- 0
y <- mu + crossprod(t(Z),u.scaled) + e
######
#Estimation part
mod1 <- genomicEBV.w.codata(y = as.numeric(y), 
          X = matrix(1, N, 1), Z = Z, X.SNPcodata = X.snp)
#To fit gBLUP just specify X.SNPcodata = matrix(1, k, 1)
cat("Correlation between true and estimated BV for the codata model:")
cat(cor(crossprod(t(Z),u.scaled), mod1$gEBV), "\n")