Getting Started with selection.index

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Introduction

The selection.index package provides an optimized suite of tools to calculate multi-trait phenotypic, genomic, and multistage selection indices. It simplifies plant and animal breeding workflows by enabling breeders to accurately rank genotypes based on user-defined economic weights and experimental designs.

Setup

First, load the package and the built-in phenotypic dataset. While the package supports any standard multi-environment phenotypic data, we will use the included seldata dataset (a built-in multi-environment trial dataset for genotypes) for this quick-start guide.

library(selection.index)

# Load the built-in phenotypic dataset
data("seldata")

# Inspect the structure of the dataset
head(seldata)
#>   rep treat   sypp     dtf    rpp    ppr     ppp    spp     pw
#> 1   1    G1 5.4306 42.5000 2.8333 2.0085  7.5833 2.7020 0.5523
#> 2   2    G1 5.4583 42.5000 3.2000 3.7179  7.8000 2.5152 0.7431
#> 3   3    G1 5.5278 43.3333 3.1250 4.2023  7.6111 3.0976 0.7473
#> 4   1    G2 6.3250 43.3333 1.7500 3.0897  3.1000 2.6515 0.4824
#> 5   2    G2 5.8333 43.3333 3.0500 3.7692 14.6500 3.2121 0.6804
#> 6   3    G2 7.9074 43.3333 3.2778 3.6752 12.0000 3.0640 0.6471

The Basics

To calculate a selection index, you must specify the traits of interest, assign economic weights, and compute the genotypic and phenotypic variance-covariance matrices.

# Define economic weights for the 7 traits of interest
weights <- c(10, 8, 6, 4, 2, 1, 1)

# Calculate genotypic and phenotypic variance-covariance matrices
# Traits: columns 3:9, Genotypes: column 2, Replication: column 1
gmat <- gen_varcov(data = seldata[, 3:9], genotypes = seldata[, 2], replication = seldata[, 1])
pmat <- phen_varcov(data = seldata[, 3:9], genotypes = seldata[, 2], replication = seldata[, 1])

Execution

With the matrices and weights defined, pass them into the primary selection function. We use lpsi() to compute the Combinatorial Linear Phenotypic Selection Index for all traits. Here, setting ncomb = 7 calculates the index considering all 7 traits simultaneously.

# Calculate the combinatorial selection index for all 7 traits
index_results <- lpsi(
  ncomb = 7,
  pmat = pmat,
  gmat = gmat,
  wmat = as.matrix(weights),
  wcol = 1
)

Results

Once the index is computed, review the genetic advance metrics and extract the final selection scores to identify the top-performing genotypes. index_results is a data frame containing the index coefficients (b columns) and various genetic metrics (GA, PRE, Delta_G, rHI).

# View the calculated index metrics for our 7-trait combination
head(index_results)
#>                    ID    b.1    b.2    b.3    b.4    b.5    b.6     b.7      GA
#> 1 1, 2, 3, 4, 5, 6, 7 1.0966 1.7309 7.0056 3.2702 0.0596 6.5862 11.5951 21.3233
#>        PRE Delta_G    rHI    hI2 Rank
#> 1 2132.327 21.3233 0.5412 0.2689    1

# Extract the final selection scores to rank the genotypes
scores <- predict_selection_score(
  index_results,
  data = seldata[, 3:9],
  genotypes = seldata[, 2]
)

# View the top ranked genotypes based on their selection scores
head(scores)
#>   Genotypes I_1_2_3_4_5_6_7 I_1_2_3_4_5_6_7_Rank
#> 1        G1        138.8605                   14
#> 2        G2        139.8725                   12
#> 3        G3        134.0505                   21
#> 4        G4        142.2628                    8
#> 5        G5        141.1228                   10
#> 6        G6        136.8680                   18

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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