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")
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.