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.
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")
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 are outlined in the package vignette.
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