Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

Abstract Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genet...

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Autores principales:	Lance F. Merrick, Arron H. Carter
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/ab8ab90773c74fa8bc5d80b2d3a9ad3d
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spelling	oai:doaj.org-article:ab8ab90773c74fa8bc5d80b2d3a9ad3d2021-12-05T07:50:11ZComparison of genomic selection models for exploring predictive ability of complex traits in breeding programs1940-337210.1002/tpg2.20158https://doaj.org/article/ab8ab90773c74fa8bc5d80b2d3a9ad3d2021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20158https://doaj.org/toc/1940-3372Abstract Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat (Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement genetic selection (GS) models to predict a complex trait. Seventeen GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel phenotyped from 2015 to 2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA, with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross‐validations. However, the consistent moderate accuracy of the parametric models indicates little advantage of using nonparametric models within individual years, but the nonparametric models show a slight increase in accuracy when combing years for complex traits. There was an increase in accuracy using cross‐validations from 0.40 to 0.41 using diversity panels lines to breeding lines. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using nonparametric machine learning models within their own breeding programs with increased accuracy as they combine training populations over the years.Lance F. MerrickArron H. CarterWileyarticlePlant 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 Lance F. Merrick Arron H. Carter Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
description	Abstract Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat (Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement genetic selection (GS) models to predict a complex trait. Seventeen GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel phenotyped from 2015 to 2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA, with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross‐validations. However, the consistent moderate accuracy of the parametric models indicates little advantage of using nonparametric models within individual years, but the nonparametric models show a slight increase in accuracy when combing years for complex traits. There was an increase in accuracy using cross‐validations from 0.40 to 0.41 using diversity panels lines to breeding lines. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using nonparametric machine learning models within their own breeding programs with increased accuracy as they combine training populations over the years.
format	article
author	Lance F. Merrick Arron H. Carter
author_facet	Lance F. Merrick Arron H. Carter
author_sort	Lance F. Merrick
title	Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
title_short	Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
title_full	Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
title_fullStr	Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
title_full_unstemmed	Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
title_sort	comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/ab8ab90773c74fa8bc5d80b2d3a9ad3d
work_keys_str_mv	AT lancefmerrick comparisonofgenomicselectionmodelsforexploringpredictiveabilityofcomplextraitsinbreedingprograms AT arronhcarter comparisonofgenomicselectionmodelsforexploringpredictiveabilityofcomplextraitsinbreedingprograms
_version_	1718372576071254016

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

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