PopVar v1.3.0

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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.

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PopVar

CRAN
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Introduction

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.

Figure 1. Visualization of the mean, genetic variance, and superior
progeny mean of a single population.

Ideally, breeders would identify the set of parent combinations that, when realized in a cross, would give rise to populations meeting these requirements. 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.

Installation

You can install the released version of PopVar from CRAN with:

install.packages("PopVar")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("UMN-BarleyOatSilphium/PopVar")

Functions

Below is a description of the functions provided in PopVar:

Function Description
pop.predict Uses simulations to make predictions in recombinant inbred line populations; can internally perform cross-validation for model selections; can be quite slow.
pop.predict2 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 pop.predict; does not perform cross-validation or model selection internally.
pop_predict2 Has the same functionality as pop.predict2, but accepts genomewide marker data in a simpler matrix format.
x.val Performs cross-validation to estimate model performance.
mppop.predict 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.
mpop_predict2 Has the same functionality as mppop.predict, but accepts genomewide marker data in a simpler matrix format.

Examples

Examples are outlined in the package vignette.

References

Bernardo, Rex. 2010. Breeding for Quantitative Traits in Plants. 2nd ed. Woodbury, Minnesota: Stemma Press.
Mohammadi, Mohsen, Tyler Tiede, and Kevin P. Smith. 2015. “PopVar: A Genome-Wide Procedure for Predicting Genetic Variance and Correlated Response in Biparental Breeding Populations.” Crop Science 55 (5): 2068–77. https://doi.org/10.2135/cropsci2015.01.0030.
Schnell, F. W., and H. F. Utz. 1975. “F1-leistung und elternwahl euphyder züchtung von selbstbefruchtern.” In Bericht über Die Arbeitstagung Der Vereinigung Österreichischer Pflanzenzüchter, 243–48. Gumpenstein, Austria: BAL Gumpenstein.

Functions in PopVar

Name Description
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
internal Internal functions
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
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Vignettes of PopVar

Name
figures/figure1.png
popvar_walkthrough.Rmd
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Details

License GPL-3
Encoding UTF-8
LazyData true
RoxygenNote 7.1.1
VignetteBuilder knitr
NeedsCompilation no
Packaged 2021-01-13 19:20:51 UTC; jln54
Repository CRAN
Date/Publication 2021-02-07 20:10:17 UTC

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