Simulated exploration of parameter space and resource allocation for strategic creation of neo-domestication breeding programs

Abstract

Modern domestication efforts have occurred in a wide range of species. These efforts have led to different levels of change from the initial wild populations and market success. In this study, we explore different breeding cycle parameters to understand the rate of change in different potential starting points of neo-domestication breeding programs. The design of a program that will bring a new crop to market requires consideration of cost efficiency and resource allocation. More crop diversity on the market creates an opportunity to design different types of food systems that can be tailored toward regional and local food security. Stochastic simulation of the rate of gain and variance is applied to different parameter combinations through the breeding cycle—crossing, evaluation, and selection—to identify population level changes along the continuum of wild to semi-domestic plant species. The simulated breeding schemes differ in phenotypic gain and variance depending on selection strategy and population type, discovering the largest phenotypic gain of oligogenic traits occurring using phenotypic recurrent selection for landrace and orphan populations while choosing genomic selection for wild populations. There were also differences based on selection strategy, with maximum avoidance consistently leading to lower gains but higher additive variance. Overall, when looking to domesticate a new species, our simulations find phenotypic recurrent selection to be the most cost-effective option and lead to the most gain in early generations of selection, with marker technology being most useful once initial gains have plateaued.