Optimization of breeding program design through stochastic simulation with evolutionary algorithms

TL;DR

An optimization framework is proposed that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization of a breeding program.

Abstract

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, considering the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameterizations of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parametrization to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake pipeline to allow for efficient scaling on large distributed computing platforms. The algorithm achieved convergence to the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization pipeline leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Figures (9)

• Figure 1: Procedure proposed for optimization via evolutionary algorithm.
• Figure 2: A dairy cattle breeding scheme.
• Figure 3: Example visualization of the Snakemake workflow. Shown are the rule names defined and their input-output relationships.
• Figure 4: Realization of expected outcome of the formulated objective function for Scenario 1 based on 100 replicates in (\ref{['fig:figureS_1a']}) for genetic gain ($\sigma_a$), (\ref{['fig:figureS_1b']}) for average kinship (Based on IBD). The red dashed line represents the mean value, while the blue shaded area shows the probability density.
• Figure 5: Suggested optima for the individual parameters of the breeding program design for the number of test daughters (\ref{['fig:figure3_1']}), test bulls (\ref{['fig:figure3_2']}), and selected sires (\ref{['fig:figure3_3']}), as well as the binary variable (\ref{['fig:figure3_4']}). The black horizontal line represents the estimated optima through comprehensive exploration, achieved by conducting over 100,000 simulations utilizing kernel regression hassanpour2023optimization