enhancer (version 1.1.0)

DT_rice: Rice lines dataset

Description

Information from a collection of 413 rice lines. The DT_rice data set is from Rice Diversity Org. Program. The lines are genotyped with 36,901 SNP markers and phenotyped for more than 30 traits. This data set was included in the package to play with it. If using it for your research make sure you cite the original publication from Zhao et al.(2011).

Usage

data(DT_rice)

Format

RicePheno contains the phenotypes RiceGeno contains genotypes letter code RiceGenoN contains the genotypes in numerical code using atcg1234 converter function

References

Keyan Zhao, Chih-Wei Tung, Georgia C. Eizenga, Mark H. Wright, M. Liakat Ali, Adam H. Price, Gareth J. Norton, M. Rafiqul Islam, Andy Reynolds, Jason Mezey, Anna M. McClung, Carlos D. Bustamante & Susan R. McCouch (2011). Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Comm 2:467 DOI: 10.1038/ncomms1467, Published Online 13 Sep 2011.

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_rice)
DT <- DT_rice
GT <- GT_rice
GTn <- GTn_rice
head(DT)
M <- atcg1234(GT)

# \donttest{

##############################################
############### sommer.      #################
##############################################
if(requireNamespace("sommer")){
library(sommer)
A <- A.mat(M$M)
mix <- mmes(Protein.content~1,
            random = ~vsm(ism(geno), Gu=A) + geno,
            rcov=~units,
            data=DT)
summary(mix)$varcomp
# if using henderson=TRUE provide Gu as inverse
Ai <- solve(A + diag(1e-6,ncol(A),ncol(A)))
Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
attr(Ai, 'inverse')=TRUE



## MULTI-TRAIT MODEL
## reshape in long format the dataset
traits <- c("Protein.content","Flag.leaf.length")
DTL <- reshape(DT[,c("geno", traits)], idvar = "geno", varying = traits,
        v.names = "value", direction = "long",
        timevar = "trait", times = traits )
DTL <- DTL[with(DTL, order(trait)), ]
head(DTL)

M <- DTLM <- atcg1234(GT)
A <- A.mat(M$M)
mix <- mmes(value~trait,
            random = ~vsm(usm(trait),ism(geno), Gu=A) ,
            rcov=~vsm(dsm(trait), ism(units)),
            data=DTL)
summary(mix)$varcomp
cov2cor(mix$theta$`vsm(usm(trait), ism(geno), Gu = A`)

}

##############################################
############### lme4breeding #################
##############################################
if(requireNamespace("lme4breeding")){
library(lme4breeding)
### univariate model
A <- A.matr(M$M)
A <- A + diag(1e-4, ncol(A), ncol(A))
mix <- lmeb(Protein.content ~ (1|geno),
                relmat = list(geno=A),
                data=DT)
vc <- VarCorr(mix); print(vc,comp=c("Variance"))
sigma(mix)^2 # error variance

### multi-trait model
traits <- c("Flowering.time.at.Arkansas" ,"Seed.volume", "Protein.content")
for(iTrait in traits){DT[,iTrait] <- scale(DT[,iTrait])}
DTL <- reshape(DT[,c("geno", traits)], idvar = "geno", varying = traits,
        v.names = "value", direction = "long",
        timevar = "trait", times = traits )
DTL <- DTL[with(DTL, order(trait, geno)), ]
head(DTL)

system.time(
  mix <- lmeb(value ~ (0+trait|geno),
                  relmat = list(geno=A),
                  rotation = TRUE,
                  data=DTL)
)
vc <- VarCorr(mix); print(vc,comp=c("Variance"))
vc$geno
sigma(mix)^2 # error variance

BLUP <- ranef(mix, condVar=TRUE)
condVAR <- lapply(BLUP, function(x){attr(x, which="postVar")}) # take sqrt() for SEs

}

# }