enhancer (version 1.1.0)

DT_cornhybrids: Corn crosses and markers

Description

This dataset contains phenotpic data for plant height and grain yield for 100 out of 400 possible hybrids originated from 40 inbred lines belonging to 2 heterotic groups, 20 lines in each, 1600 rows exist for the 400 possible hybrids evaluated in 4 locations but only 100 crosses have phenotypic information. The purpose of this data is to show how to predict the other 300 crosses.

The data contains 3 elements. The first is the phenotypic data and the parent information for each cross evaluated in the 4 locations. 1200 rows should have missing data but the 100 crosses performed were chosen to be able to estimate the GCA and SCA effects of everything.

The second element of the data set is the phenotypic data and other relevant information for the 40.

The third element is the genomic relationship matrix for the 40 inbred lines originated from 511 SNP markers and calculated using the A.mat function.

Usage

data("DT_cornhybrids")

Format

The format is: chr "DT_cornhybrids"

References

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

Giovanny Covarrubias-Pazaran (2024). lme4breeding: enabling genetic evaluation in the age of genomic data. To be submitted to Bioinformatics.

Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48.

Examples

data(DT_cornhybrids)
DT <- DT_cornhybrids
GT <- GT_cornhybrids
A <- GT
K1 <- A[levels(DT$GCA1), levels(DT$GCA1)]; dim(K1)
K2 <- A[levels(DT$GCA2), levels(DT$GCA2)]; dim(K2)

# \donttest{

S <- kronecker(K1, K2) ; dim(S)
rownames(S) <- colnames(S) <- levels(DT$SCA)

# }

# \donttest{

################ sommer ###################

if(requireNamespace("sommer")){

library(sommer)
ans <- mmes(Yield ~ Location,
             random = ~ vsm(ism(GCA1),Gu=K1) + vsm(ism(GCA2),Gu=K2), # + vsm(ism(SCA),Gu=S),
             rcov=~units,
             data=DT)
summary(ans)$varcomp

## mmec uses the inverse of the relationship matrix
K1i <- solve(K1 + diag(1e-4,ncol(K1),ncol(K1)))
K1i <- as(as(as( K1i,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
attr(K1i, 'inverse')=TRUE
K2i <- solve(K2 + diag(1e-4,ncol(K2),ncol(K2)))
K2i <- as(as(as( K2i,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
attr(K2i, 'inverse')=TRUE
Si <- solve(S + diag(1e-4,ncol(S),ncol(S)))
Si <- as(as(as( Si,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
attr(Si, 'inverse')=TRUE
ans2 <- mmes(Yield ~ Location,
             random = ~ vsm(ism(GCA1),Gu=K1i) + vsm(ism(GCA2),Gu=K2i), # + vsm(ism(SCA),Gu=Si),
             henderson=TRUE,
             rcov=~units,
             data=DT)
summary(ans2)$varcomp

}

################ lme4breeding ###################

if(requireNamespace("lme4breeding")){
library(lme4breeding)

ans <- lmeb(Yield ~ Location + (1| GCA1) + (1|GCA2),
                relmat = list(GCA1=K1,
                              GCA2=K2),
                data=DT)
vc <- VarCorr(ans); print(vc,comp=c("Variance"))
sigma(ans)^2 # error variance
BLUP <- ranef(ans, condVar=TRUE)
condVAR <- lapply(BLUP, function(x){attr(x, which="postVar")}) # take sqrt() for SEs

}

# }