DT_rice {lme4breeding}

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

Source

Rice Diversity Organization http://www.ricediversity.org/data/index.cfm.

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.

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



### univariate model
A <- A.mat(M$M)
A <- A + diag(1e-4, ncol(A), ncol(A))
mix <- lmebreed(Protein.content ~ (1|geno),
                relmat = list(geno=A),
                control = lmerControl(
                  check.nobs.vs.nlev = "ignore",
                  check.nobs.vs.rankZ = "ignore",
                  check.nobs.vs.nRE="ignore"
                ),
                data=DT)
vc <- VarCorr(mix); print(vc,comp=c("Variance"))

### multi-trait model
DT2 <- stackTrait(data=DT, traits = colnames(DT)[3:12])$long
DT2$trait <- as.factor(DT2$trait)
head(DT2)
system.time(
  mix <- lmebreed(valueS ~ (0+trait|geno),
                  relmat = list(geno=A),
                  control = lmerControl(
                    check.nobs.vs.nlev = "ignore",
                    check.nobs.vs.rankZ = "ignore",
                    check.nobs.vs.nRE="ignore"
                  ), rotation = TRUE,
                  data=DT2)
)
vc <- VarCorr(mix); print(vc,comp=c("Variance"))
vc$geno