IndexWizard

The goal of ‘IndexWizard’ is to provide a framework for the exploration of effects of complex selection indices on the genetic and phenotypic progress of a population. It allows to construct selection indices based on estimated breeding values in animal and plant breeding and to calculate several analytic measures around. The methodology thereby allows to analyze genetic gain of traits in the breeding goal that are not part of the actual index and automatically computes several analytic measures. It further allows to retrospectively derive realized economic weights from observed genetic trends. See Simianer et al. “How economic weights translate into genetic and phenotypic progress, and vice versa” Genet Sel Evol 55, 38 (2023) for a detailed description of the methodology and a complex example. Also find the pdf of a Poster in this repository.

Installation

You can either install IndexWizard from CRAN the usual way:

install.packages("IndexWizard")

Or directly install the development version of IndexWizard from GitHub with:

# install.packages("devtools")
devtools::install_github("johannesgeibel/IndexWizard", build_vignettes = TRUE)

Note that devtools requires Rtools to be installed on Windows systems.

Example

All calculations are solely performed in the function SelInd(). Its minimal input requirements are a named genetic covariance matrix (G) for all n breeding goal traits, a named vector of economic weights (w) for the n breeding goal traits and a named vector of reliabilities (r2) of the estimated breeding values for the $m \le n$ index traits.

library(IndexWizard)
tn <- c("RZM", "RZN", "RZEo") # trait names of breeding goal traits
G <- matrix(
    c(1.0,0.13,0.13,
    0.13,1.0,0.23,
    0.13,0.23,1.0),
    3, 3, dimnames = list(tn,tn)
    ) * 144 # genetic covariance matrix
w <- c(0.7, 0.3, 0) # economic weights, giving weight only to the first two traits
names(w) <- tn
r2 <- c(0.743, 0.673) # reliabilities for the Index traits
names(r2) <- tn[1:2]
SelInd(
  w = w,
  G = G,
  r2 = r2
)

Read the vignette CaseStudy for a detailed introduction, covering different use cases of the framework.

vignette("CaseStudy", package = "IndexWizard")

Further find the analysis script for Simianer et al. in this repository.