Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials

Abstract Sparse testing in genome‐enabled prediction in plant breeding can be emulated throughout different line allocations where some lines are observed in all environments (overlap) and others are observed in only one environment (nonoverlap). We studied three general cases of the composition of...

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Autores principales:	Leonardo Crespo‐Herrera, Reka Howard, Hans‐Peter Piepho, Paulino Pérez‐Rodríguez, Osval Montesinos‐Lopez, Juan Burgueño, Ravi Singh, Suchismita Mondal, Diego Jarquín, Jose Crossa
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/bd11103f902b48adbebe56a030d98097
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spelling	oai:doaj.org-article:bd11103f902b48adbebe56a030d980972021-12-05T07:50:11ZGenome‐enabled prediction for sparse testing in multi‐environmental wheat trials1940-337210.1002/tpg2.20151https://doaj.org/article/bd11103f902b48adbebe56a030d980972021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20151https://doaj.org/toc/1940-3372Abstract Sparse testing in genome‐enabled prediction in plant breeding can be emulated throughout different line allocations where some lines are observed in all environments (overlap) and others are observed in only one environment (nonoverlap). We studied three general cases of the composition of the sparse testing allocation design for genome‐enabled prediction of wheat (Triticum aestivum L.) breeding: (a) completely nonoverlapping wheat lines in environments, (b) completely overlapping wheat lines in all environments, and (c) a proportion of nonoverlapping/overlapping wheat lines allocated in the environments. We also studied several cases in which the size of the testing population was systematically decreased. The study used three extensive wheat data sets (W1, W2, and W3). Three different genome‐enabled prediction models (M1–M3) were used to study the effect of the sparse testing in terms of the genomic prediction accuracy. Model M1 included only main effects of environments and lines; M2 included main effects of environments, lines, and genomic effects; whereas the remaining model (M3) also incorporated the genomic × environment interaction (GE). The results show that the GE component of the genome‐based model M3 captures a larger genetic variability than the main genomic effects term from models M1 and M2. In addition, model M3 provides higher prediction accuracy than models M1 and M2 for the same allocation designs (different combinations of nonoverlapping/overlapping lines in environments and training set sizes). Overlapped sets of 30–50 lines in all the environments provided stable genomic‐enabled prediction accuracy. Reducing the size of the testing populations under all allocation designs decreases the prediction accuracy, which recovers when more lines are tested in all environments. Model M3 offers the possibility of maintaining the prediction accuracy throughout both extreme situations of all nonoverlapping lines and all overlapping lines.Leonardo Crespo‐HerreraReka HowardHans‐Peter PiephoPaulino Pérez‐RodríguezOsval Montesinos‐LopezJuan BurgueñoRavi SinghSuchismita MondalDiego JarquínJose CrossaWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Leonardo Crespo‐Herrera Reka Howard Hans‐Peter Piepho Paulino Pérez‐Rodríguez Osval Montesinos‐Lopez Juan Burgueño Ravi Singh Suchismita Mondal Diego Jarquín Jose Crossa Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
description	Abstract Sparse testing in genome‐enabled prediction in plant breeding can be emulated throughout different line allocations where some lines are observed in all environments (overlap) and others are observed in only one environment (nonoverlap). We studied three general cases of the composition of the sparse testing allocation design for genome‐enabled prediction of wheat (Triticum aestivum L.) breeding: (a) completely nonoverlapping wheat lines in environments, (b) completely overlapping wheat lines in all environments, and (c) a proportion of nonoverlapping/overlapping wheat lines allocated in the environments. We also studied several cases in which the size of the testing population was systematically decreased. The study used three extensive wheat data sets (W1, W2, and W3). Three different genome‐enabled prediction models (M1–M3) were used to study the effect of the sparse testing in terms of the genomic prediction accuracy. Model M1 included only main effects of environments and lines; M2 included main effects of environments, lines, and genomic effects; whereas the remaining model (M3) also incorporated the genomic × environment interaction (GE). The results show that the GE component of the genome‐based model M3 captures a larger genetic variability than the main genomic effects term from models M1 and M2. In addition, model M3 provides higher prediction accuracy than models M1 and M2 for the same allocation designs (different combinations of nonoverlapping/overlapping lines in environments and training set sizes). Overlapped sets of 30–50 lines in all the environments provided stable genomic‐enabled prediction accuracy. Reducing the size of the testing populations under all allocation designs decreases the prediction accuracy, which recovers when more lines are tested in all environments. Model M3 offers the possibility of maintaining the prediction accuracy throughout both extreme situations of all nonoverlapping lines and all overlapping lines.
format	article
author	Leonardo Crespo‐Herrera Reka Howard Hans‐Peter Piepho Paulino Pérez‐Rodríguez Osval Montesinos‐Lopez Juan Burgueño Ravi Singh Suchismita Mondal Diego Jarquín Jose Crossa
author_facet	Leonardo Crespo‐Herrera Reka Howard Hans‐Peter Piepho Paulino Pérez‐Rodríguez Osval Montesinos‐Lopez Juan Burgueño Ravi Singh Suchismita Mondal Diego Jarquín Jose Crossa
author_sort	Leonardo Crespo‐Herrera
title	Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
title_short	Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
title_full	Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
title_fullStr	Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
title_full_unstemmed	Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
title_sort	genome‐enabled prediction for sparse testing in multi‐environmental wheat trials
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/bd11103f902b48adbebe56a030d98097
work_keys_str_mv	AT leonardocrespoherrera genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT rekahoward genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT hanspeterpiepho genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT paulinoperezrodriguez genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT osvalmontesinoslopez genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT juanburgueno genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT ravisingh genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT suchismitamondal genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT diegojarquin genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials AT josecrossa genomeenabledpredictionforsparsetestinginmultienvironmentalwheattrials
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Genome‐enabled prediction for sparse testing in multi‐environmental wheat trials

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