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
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")
::install_github("UMN-BarleyOatSilphium/PopVar") devtools
```

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.

Bernardo, Rex. 2010. *Breeding for Quantitative Traits in
Plants*. 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 Sci.* 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.