Genomic Breeding Tools: Genetic Variance Prediction and Cross-Validation
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations,
from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic
data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all,
or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction
accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>).
A dataset 'think_barley.rda' is included for reference and examples.
To make progress in breeding, populations should have a favorable mean and high genetic variance (Bernardo 2010). These two parameters can be combined into a single measure called the usefulness criterion (Schnell and Utz 1975), visualized in Figure 1.
Ideally, breeders would identify the set of parent combinations that,
when realized in a cross, would give rise to populations meeting these
PopVar is a package that uses phenotypic and genomewide
marker data on a set of candidate parents to predict the mean, genetic
variance, and superior progeny mean in bi-parental or multi-parental
populations. Thre package also contains functionality for performing
cross-validation to determine the suitability of different statistical
models. More details are available in Mohammadi, Tiede, and Smith
(2015). A dataset
think_barley is included for reference and examples.
You can install the released version of PopVar from CRAN with:
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("UMN-BarleyOatSilphium/PopVar")
Below is a description of the functions provided in
||Uses simulations to make predictions in recombinant inbred line populations; can internally perform cross-validation for model selections; can be quite slow.|
||Uses deterministic equations to make predictions in populations of complete or partial selfing and with or without the induction of doubled haploids; is much faster than
||Has the same functionality as
||Performs cross-validation to estimate model performance.|
||Uses deterministic equations to make predictions in 2- or 4-way populations of complete or partial selfing and with or without the induction of doubled haploids; does not perform cross-validation or model selection internally.|
||Has the same functionality as
Examples are outlined in the package vignette.
Functions in PopVar
|mppop.predict||Predict genetic variance and genetic correlations in multi-parent populations using a deterministic equation.|
|pop.predict2||Predict genetic variance and genetic correlations in bi-parental populations using a deterministic model|
|PopVar-package||Genomic breeding tools to 1) predict standard statistics and correlated response in plant populations, and 2) performs cross-validation to estimate genome-wide prediction accuracy|
|x.val||Estimate genome-wide prediction accuracy using cross-validation|
|think_barley.rda||An example barley dataset|
|pop.predict||A genome-wide procedure for predicting genetic variance and correlated response in bi-parental breeding populations|
Vignettes of PopVar
Last month downloads
|Packaged||2021-01-13 19:20:51 UTC; jln54|
|Date/Publication||2021-02-07 20:10:17 UTC|
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