lme4breeding-package {lme4breeding}

Linear mixed equations 4 Breeding

Description

lme4breeding is nice wrapper of the lme4 package that enables the use of especialized plant and animal breeding models that include relationship matrices among individuals (e.g., genomic relationship matrices) and complex covariance structures between factors (e.g., factor analytic structures) accelerated by the use of the eigen decomposition of relationship matrices. It uses all the lme4 machinery for linear and non-linear models, for different response distributions opening a world of possibilities.

It took me several years to develop a package named sommer that allowed many of the desired models but it was impossible to keep up with the needed development. In May of 2024 I realized that few lines of code (exactly 100 lines) would allow to tweak all the lme4 machinery to fit most plant and animal models popular today at a great speed enabled by the lme4 development team. I don't regret having writen the sommer package since it allowed me to learn the little I know about mixed models. I am ashamed it took me so many years to leverage from the lme4 machinery (especially me being a badger). I hope you enjoy it.

The lmebreed function is the core function of the package which is exactly the same function than lmer or glmer but with few added arguments relmat and addmat that allow the user to provide relationship matrices and customized incidence matrices respectively. Also the argument rotation speeds up highly complex models. The lme4 machinery is designed to deal with a big number of records (r) since it works in the r > c problem and inverts a c x c matrix (being c the number of coefficients). There are ranef, fixef, VarCorr functions to obtain variance-covariance components, BLUPs, BLUEs, residuals, fitted values, variances-covariances for fixed and random effects, etc.

Functions for genetic analysis

The package provides kernels to the estimate additive (A.mat) relationship matrix for diploid and polyploid organisms. A good converter from letter code to numeric format is implemented in the function atcg1234, which supports higher ploidy levels than diploid. Additional functions for genetic analysis have been included such as build a genotypic hybrid marker matrix (build.HMM). If you need to use pedigree you need to convert your pedigree into a relationship matrix (use the 'getA' function from the pedigreemm package).

Functions for trial analysis

Recently, spatial modeling has been added added to lme4breeding using the two-dimensional spline tps function.

Keeping lme4breeding updated

The lme4breeding package is updated on CRAN every 4-months due to CRAN policies but you can find the latest source at https://github.com/covaruber/lme4breeding. This can be easily installed typing the following in the R console:

library(devtools)

install_github("covaruber/lme4breeding")

This is recommended if you reported a bug, was fixed and was immediately pushed to GitHub but not in CRAN until the next update.

Tutorials

For tutorials on how to perform different analysis with lme4breeding please look at the vignettes by typing in the terminal:

vignette("lme4breeding.qg")

vignette("lme4breeding.gxe")

Getting started

The package has been equiped with several datasets to learn how to use the lme4breeding package (and almost to learn all sort of quantitative genetic analysis):

* DT_halfdiallel, DT_fulldiallel and DT_mohring datasets have examples to fit half and full diallel designs.

* DT_h2 to calculate heritability

* DT_cornhybrids and DT_technow datasets to perform genomic prediction in hybrid single crosses

* DT_wheat dataset to do genomic prediction in single crosses in species displaying only additive effects.

* DT_cpdata dataset to fit genomic prediction models within a biparental population coming from 2 highly heterozygous parents including additive, dominance and epistatic effects.

* DT_polyploid to fit genomic prediction and GWAS analysis in polyploids.

* DT_gryphon data contains an example of an animal model including pedigree information.

* DT_btdata dataset contains an animal (birds) model.

* DT_legendre simulated dataset for random regression model.

* DT_sleepstudy dataset to know how to translate lme4 models to lme4breeding models.

Models Enabled

The machinery behind the scenes is lme4.

Bug report and contact

If you have any questions or suggestions please post it in https://stackoverflow.com or https://stats.stackexchange.com

I'll be glad to help or answer any question. I have spent a valuable amount of time developing this package. Please cite this package in your publication. Type 'citation("lme4breeding")' to know how to cite it.

Author(s)

Giovanny Covarrubias-Pazaran

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.

Examples

data(DT_example)
DT <- DT_example
A <- A_example

ansMain <- lmebreed(Yield ~ Env + (1|Name),
                        relmat = list(Name = A ),
                        data=DT)
vc <- VarCorr(ansMain); print(vc,comp=c("Variance"))