enhancer (version 1.1.0)

DT_polyploid: Genotypic and Phenotypic data for a potato polyploid population

Description

This dataset contains phenotpic data for 18 traits measured in 187 individuals from a potato diversity panel. In addition contains genotypic data for 221 individuals genotyped with 3522 SNP markers. Please if using this data for your own research make sure you cite Rosyara's (2015) publication (see References).

Usage

data("DT_polyploid")

Format

The format is: chr "DT_polyploid"

References

If using this data for your own research please cite:

Rosyara Umesh R., Walter S. De Jong, David S. Douches, Jeffrey B. Endelman. Software for genome-wide association studies in autopolyploids and its application to potato. The Plant Genome 2015.

Other 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_polyploid)
DT <- DT_polyploid
GT <- GT_polyploid
MP <- MP_polyploid
## convert markers to numeric format
numo <- atcg1234(data=GT[,1:100], ploidy=4);
numo$M[1:5,1:5];
numo$ref.allele[,1:5]

## plants with both genotypes and phenotypes
common <- intersect(DT$Name,rownames(numo$M))

## get the markers and phenotypes for such inds
marks <- numo$M[common,]; marks[1:5,1:5]
DT2 <- DT[match(common,DT$Name),];
DT2 <- as.data.frame(DT2)
DT2[1:5,]

## Additive relationship matrix, specify ploidy
A <- A.matr(marks)

# \donttest{

##############################################
###############  sommer.     #################
##############################################
if(requireNamespace("sommer")){
library(sommer)
A <- A.mat(marks)
D <- D.mat(marks)
###=========================================####
### run as mixed model
###=========================================####
ans <- mmes(tuber_shape~1,
            random=~vsm(ism(Name), Gu=A),
            data=DT2)
summary(ans)$varcomp
# if using mmes=TRUE provide Gu as inverse
Ai <- solve(A + diag(1e-4,ncol(A),ncol(A)))
Ai <- as(as(as( Ai,  "dMatrix"), "generalMatrix"), "CsparseMatrix")
attr(Ai, 'inverse')=TRUE

}

##############################################
############### lme4breeding #################
##############################################
if(requireNamespace("lme4breeding")){
library(lme4breeding)
## run as mixed model
A <- A + diag(1e-4,ncol(A),ncol(A))
ans <- lmeb(tuber_shape~ (1|Name),
                relmat = list(Name=A),
                data=DT2)
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

}

# }