The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

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...

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Autores principales:	Fatemeh Amini, Felipe Restrepo Franco, Guiping Hu, Lizhi Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a67fbd731d2943b980a01ac66833725c
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spelling	oai:doaj.org-article:a67fbd731d2943b980a01ac66833725c2021-12-02T14:03:59ZThe look ahead trace back optimizer for genomic selection under transparent and opaque simulators10.1038/s41598-021-83567-52045-2322https://doaj.org/article/a67fbd731d2943b980a01ac66833725c2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83567-5https://doaj.org/toc/2045-2322Abstract 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.Fatemeh AminiFelipe Restrepo FrancoGuiping HuLizhi WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fatemeh Amini Felipe Restrepo Franco Guiping Hu Lizhi Wang The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
description	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.
format	article
author	Fatemeh Amini Felipe Restrepo Franco Guiping Hu Lizhi Wang
author_facet	Fatemeh Amini Felipe Restrepo Franco Guiping Hu Lizhi Wang
author_sort	Fatemeh Amini
title	The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
title_short	The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
title_full	The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
title_fullStr	The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
title_full_unstemmed	The look ahead trace back optimizer for genomic selection under transparent and opaque simulators
title_sort	look ahead trace back optimizer for genomic selection under transparent and opaque simulators
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a67fbd731d2943b980a01ac66833725c
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The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

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