AlphaPart

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Overview

AlphaPart is a sophisticated R package for partitioning genetic trends (Obšteter et al. 2021), facilitating a deeper understanding of genetic gain in breeding programs. This method, rooted in the innovative works by Garcia-Cortes et al. (2008) and recent advancements, allows breeders and researchers to dissect the contributions of various selection paths to overall genetic progress.

Features

Implements cutting-edge methods for partitioning both genetic means and variances.
Includes functions for data manipulation, ensuring compatibility with diverse data sets.
Offers advanced tools for visualizing genetic trends and partitioning results.
Provides robust summarizing capabilities to interpret complex genetic data.

New in This Version

Incorporates recent methods for partitioning genetic variance, providing a more holistic view of breeding values (Oliveira et al. 2022).
Improved algorithms for more accurate and insightful analysis of breeding programs.
New visualization features for a more intuitive understanding of genetic trends.

Instalation

# The easiest way to get AlphaPart
install.packages("AlphaPart")

Development version

To get a bug fix or to use a feature from the development version, you can install the development version of AlphaPart from GitHub.

install.packages("devtools")
devtools::install_github("AlphaGenes/AlphaPart")

Contributing to AlphaPart

Please, follow the contribution guide at Contributing file to know how to start contributing to AlphaPart.

Usage

library(AlphaPart)

## Partition additive genetic values by country and sex
AlphaPart.ped$country.gender <- with(AlphaPart.ped, paste(country, gender, sep="-"))
(res <- AlphaPart(x=AlphaPart.ped, colPath="country.gender", colBV=c("bv1", "bv2")))
#> Size:
#>  - individuals: 8 
#>  - traits: 2 (bv1, bv2)
#>  - paths: 4 (domestic-F, domestic-M, import-F, import-M)
#>  - unknown (missing) values:
#> bv1 bv2 
#>   0   0 

#>  Partitions of breeding values 
#>    - individuals: 8 
#>    - paths: 4 (domestic-F, domestic-M, import-F, import-M)
#>    - traits: 2 (bv1, bv2)

#>  Trait: bv1 

#>   IId FId MId gen  country gender country.gender bv1 bv1_pa bv1_w bv1_domestic-F bv1_domestic-M bv1_import-F bv1_import-M
#> 1   A           1 domestic      F     domestic-F 100    0.0 100.0        100.000            0.0          0.0        0.000
#> 2   B           1   import      M       import-M 105    0.0 105.0          0.000            0.0          0.0      105.000
#> 3   C   B   A   2 domestic      F     domestic-F 104  102.5   1.5         51.500            0.0          0.0       52.500
#> 4   T   B       2   import      F       import-F 102   52.5  49.5          0.000            0.0         49.5       52.500
#> 5   D           2   import      M       import-M 108    0.0 108.0          0.000            0.0          0.0      108.000
#> 6   E   D   C   3 domestic      M     domestic-M 107  106.0   1.0         25.750            1.0          0.0       80.250
#> 7   U   D       3   import      F       import-F 107   54.0  53.0          0.000            0.0         53.0       54.000
#> 8   V   E       4 domestic      F     domestic-F 109   53.5  55.5         68.375            0.5          0.0       40.125

#>  Trait: bv2 

#>   IId FId MId gen  country gender country.gender bv2 bv2_pa bv2_w bv2_domestic-F bv2_domestic-M bv2_import-F bv2_import-M
#> 1   A           1 domestic      F     domestic-F  88    0.0  88.0          88.00           0.00          0.0          0.0
#> 2   B           1   import      M       import-M 110    0.0 110.0           0.00           0.00          0.0        110.0
#> 3   C   B   A   2 domestic      F     domestic-F 100   99.0   1.0          45.00           0.00          0.0         55.0
#> 4   T   B       2   import      F       import-F  97   55.0  42.0           0.00           0.00         42.0         55.0
#> 5   D           2   import      M       import-M 101    0.0 101.0           0.00           0.00          0.0        101.0
#> 6   E   D   C   3 domestic      M     domestic-M  80  100.5 -20.5          22.50         -20.50          0.0         78.0
#> 7   U   D       3   import      F       import-F 102   50.5  51.5           0.00           0.00         51.5         50.5
#> 8   V   E       4 domestic      F     domestic-F 105   40.0  65.0          76.25         -10.25          0.0         39.0

## Summarize population by generation (=trend)
(ret <- summary(res, by="gen"))

#> Summary of partitions of breeding values 
#>   - paths: 4 (domestic-F, domestic-M, import-F, import-M)
#>   - traits: 2 (bv1, bv2)

#> Trait: bv1 

#>   gen N      Sum domestic-F domestic-M import-F import-M
#> 1   1 2 102.5000   50.00000        0.0      0.0   52.500
#> 2   2 3 104.6667   17.16667        0.0     16.5   71.000
#> 3   3 2 107.0000   12.87500        0.5     26.5   67.125
#> 4   4 1 109.0000   68.37500        0.5      0.0   40.125

#> Trait: bv2 

#>   gen N       Sum domestic-F domestic-M import-F import-M
#> 1   1 2  99.00000      44.00       0.00     0.00 55.00000
#> 2   2 3  99.33333      15.00       0.00    14.00 70.33333
#> 3   3 2  91.00000      11.25     -10.25    25.75 64.25000
#> 4   4 1 105.00000      76.25     -10.25     0.00 39.00000

## Plot the partitions
p <- plot(ret, ylab=c("BV for trait 1", "BV for trait 2"), xlab="Generation")
print(p)

AlphaGenes / AlphaPart

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