A look-ahead approach to maximizing present value of genetic gains in genomic selection
Date
2022-06-02
Authors
Zhang, Zerui
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Oxford University Press
Abstract
Look-ahead selection (LAS) is a sophisticated yet effective algorithm for genomic selection, which optimizes not only selection of breeding parents but also mating strategy and resource allocation by anticipating the implications of crosses in a pre-specified future target generation. Simulation results using maize datasets have suggested that LAS is able to significantly accelerate genetic gain in the target generation while maintaining genetic diversity. In this paper, we propose a new algorithm to address the limitations of LAS, including the difficulty in specifying a meaningful deadline in a continuous breeding process and slow growth of genetic gain in early generations. This new algorithm uses the present value (PV) of genetic gains as the breeding objective, converting genetic gains realized in different generations to the current generation using a discount rate, similar to using the interest rate to measure the time value of cash flows incurred at different time points. By using the look-ahead techniques to anticipate the future gametes and thus PV of future genetic gains, this algorithm yields a better trade-off between short-term and long-term benefits. Results from simulation experiments showed that the new algorithm can achieve higher genetic gains in early generations and a continuously growing trajectory as opposed to the LAS algorithm, which features a slow progress in early generations and a growth spike right before the deadline.
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This is the version of record for the article Zhang, Zerui, and Lizhi Wang. "A look-ahead approach to maximizing present value of genetic gains in genomic selection." G3 Genes| Genomes| Genetics 12, no. 8 (2022): jkac136. Available online at DOI: 10.1093/g3journal/jkac136. Copyright 2022 The Author(s). CC BY 4.0.
Posted with permission.