DT_cpdata {lme4breeding}

Genotypic and Phenotypic data for a CP population

Description

A CP population or F1 cross is the designation for a cross between 2 highly heterozygote individuals; i.e. humans, fruit crops, bredding populations in recurrent selection.

This dataset contains phenotpic data for 363 siblings for an F1 cross. These are averages over 2 environments evaluated for 4 traits; color, yield, fruit average weight, and firmness. The columns in the CPgeno file are the markers whereas the rows are the individuals. The CPpheno data frame contains the measurements for the 363 siblings, and as mentioned before are averages over 2 environments.

Usage

data("DT_cpdata")

Format

The format is: chr "DT_cpdata"

Source

This data was simulated for fruit breeding applications.

References

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.

See Also

The core function of the package lmebreed

Examples

data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
## create the variance-covariance matrix
A <- A.mat(GT) # additive relationship matrix
A <- A + diag(1e-4, ncol(A), ncol(A))
## look at the data and fit the model
head(DT)



mix1 <- lmebreed(Yield~ (1|id) + (1|Rowf) + (1|Colf),
                 relmat=list(id=A),
                 control = lmerControl(
                   check.nobs.vs.nlev = "ignore",
                   check.nobs.vs.rankZ = "ignore",
                   check.nobs.vs.nRE="ignore"
                 ),
                 data=DT)
vc <- VarCorr(mix1); print(vc,comp=c("Variance"))
# run one last iteration with imputed data
# to make sure you get predictions for everyone
DT2 <- DT
DT2$Yield <- imputev(DT2$Yield)
mix1b <- update(mix1, data=DT2,
                control=lmerControl(
  check.nobs.vs.nlev = "ignore",
  check.nobs.vs.rankZ = "ignore",
  check.nobs.vs.nRE="ignore",
  optCtrl=list(maxfun=1)
))
rex <- ranef(mix1b)