sommer (version 4.4.3)

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.

See Also

The core functions of the package mmes

Examples

####=========================================####
#### For CRAN time limitations most lines in the 
#### examples are silenced with one '#' mark, 
#### remove them and run the examples using
#### command + shift + C |OR| control + shift + C
####=========================================####
data(DT_rice)
# DT <- DT_rice
# GT <- GT_rice
# GTn <- GTn_rice
# head(DT)
# M <- atcg1234(GT)
# 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`)