The design of selection experiments using a model-based approach

TL;DR

This paper presents a flexible, model-based approach for designing optimal selection experiments in plant breeding, improving genetic gain through efficient resource allocation and robust statistical models.

Contribution

It introduces a novel design methodology for selection experiments that aligns with analysis models and enhances resource efficiency in plant breeding programs.

Findings

Designs optimized for the proposed model improve selection accuracy.

Resource allocation strategies enhance experimental efficiency.

Simulation studies demonstrate the approach's advantages over traditional methods.

Abstract

Plant breeding programs use data obtained from multi-environment selection experiments to produce improved varieties with the ultimate aim of maintaining high levels of genetic gain. Selection accuracy can be improved with the use of advanced statistical analytical methods that use informative and parsimonious variance models for the set of genotype by environment interaction effects, include information on genetic relatedness and appropriately accommodate non-genetic sources of variation within the framework of a single step estimation and prediction algorithm. Maximal gains from using these advanced techniques are more likely to be achieved if the designs used match the aims of the selection experiment and make full use of the available resources. In this paper we present an approach for constructing designs for selection experiments which are optimal or near optimal against a robust and sensible linear mixed model. This model reflects the models used for analysis. The approach is flexible and introduces an additional step to accommodate efficient resource allocation of replication status to genotypes, which is undertaken prior to the allocation of plots to genotypes. A motivating example is used to illustrate the approach, two illustrative examples are presented one each for single and multiple environment selection experiments and several in-silico simulation studies are used to demonstrate the advantages of these approaches.