Efficient Breeding by Genomic Mating

TL;DR

This paper introduces a genomic mating approach for breeding that optimizes long-term breeding values using genetic algorithms, outperforming traditional truncation selection for complex traits.

Contribution

It presents a novel genomic mating method that incorporates estimated breeding values, risk, and inbreeding, optimized via genetic algorithms for improved breeding efficiency.

Findings

Genomic mating outperforms truncation selection in simulations.

The method effectively improves breeding values for complex traits.

Optimization via genetic algorithms is computationally feasible.

Abstract

In this article, we propose an approach to breeding which focuses on mating instead of truncation selection, our method uses genome-wide marker information in a similar fashion to genomic selection so we refer it to as genomic mating. Using concepts of estimated breeding values, risk (usefulness) and inbreeding, an efficient mating approach is formulated for improvement of breeding values in the long run. We have used a genetic algorithm to find solutions to this optimization problem. Results from our simulations point to the efficiency of genomic mating for breeding complex traits compared to truncation selection.