Campus Units

Industrial and Manufacturing Systems Engineering, Electrical and Computer Engineering, Bioeconomy Institute (BEI)

Document Type

Article

Publication Version

Published Version

Publication Date

2-18-2021

Journal or Book Title

Scientific Reports

Volume

11

First Page

4124

Research Focus Area(s)

​Operations Research, Information Engineering

DOI

10.1038/s41598-021-83567-5

Abstract

Recent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

Comments

This article is published as Amini, Fatemeh, Felipe Restrepo Franco, Guiping Hu, and Lizhi Wang. "The look ahead trace back optimizer for genomic selection under transparent and opaque simulators." Scientific Reports 11 (2021): 4124. DOI: 10.1038/s41598-021-83567-5. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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