Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize

Abstract Efficiently exploiting natural genetic diversity captured by accessions stored in genebanks is crucial to genetic improvement of major crops. Selecting accessions of interest from genebanks has traditionally required information from extensive and expensive evaluation; however, low‐cost gen...

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Autores principales:	Matthew J. Dzievit, Tingting Guo, Xianran Li, Jianming Yu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/5ef1082fbf6742f1a6dd0095795c9824
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spelling	oai:doaj.org-article:5ef1082fbf6742f1a6dd0095795c98242021-12-05T07:50:12ZComprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize1940-337210.1002/tpg2.20160https://doaj.org/article/5ef1082fbf6742f1a6dd0095795c98242021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20160https://doaj.org/toc/1940-3372Abstract Efficiently exploiting natural genetic diversity captured by accessions stored in genebanks is crucial to genetic improvement of major crops. Selecting accessions of interest from genebanks has traditionally required information from extensive and expensive evaluation; however, low‐cost genotyping combined with genomic prediction have enabled us to generate predicted genetic merits for the entire set with targeted phenotypic evaluation of representative subsets. To explore this general approach, analytical assessment and empirical validation of the maize (Zea mays L.) association population (MAP) as a training population were conducted in the present study. Cross‐validation within the MAP revealed mostly modest to strong predictive ability for 36 traits, generally in parallel with the square root of heritability. The MAP was then used to train the prediction models to generate genomic estimated breeding values (GEBVs) for the Ames Diversity Panel. Empirical validation conducted for nine traits across two validation populations confirmed the accuracy level indicated by the cross‐validation of the training population. An upper bound for reliability (U value) was calculated for the accessions in the prediction population using genotypic data. The group of accessions with high U values generally had high predictive ability, even though the range of observed trait values was similar to the group of accessions with low U values. Our comprehensive analysis validated the general approach of turbocharging genebanks with genomics and genomic prediction. In addition, breeders and researchers can consider both GEBVs and U values to balance the needs of improving specific traits and broadening genetic diversity when selecting accessions from genebanks.Matthew J. DzievitTingting GuoXianran LiJianming YuWileyarticlePlant 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 Matthew J. Dzievit Tingting Guo Xianran Li Jianming Yu Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
description	Abstract Efficiently exploiting natural genetic diversity captured by accessions stored in genebanks is crucial to genetic improvement of major crops. Selecting accessions of interest from genebanks has traditionally required information from extensive and expensive evaluation; however, low‐cost genotyping combined with genomic prediction have enabled us to generate predicted genetic merits for the entire set with targeted phenotypic evaluation of representative subsets. To explore this general approach, analytical assessment and empirical validation of the maize (Zea mays L.) association population (MAP) as a training population were conducted in the present study. Cross‐validation within the MAP revealed mostly modest to strong predictive ability for 36 traits, generally in parallel with the square root of heritability. The MAP was then used to train the prediction models to generate genomic estimated breeding values (GEBVs) for the Ames Diversity Panel. Empirical validation conducted for nine traits across two validation populations confirmed the accuracy level indicated by the cross‐validation of the training population. An upper bound for reliability (U value) was calculated for the accessions in the prediction population using genotypic data. The group of accessions with high U values generally had high predictive ability, even though the range of observed trait values was similar to the group of accessions with low U values. Our comprehensive analysis validated the general approach of turbocharging genebanks with genomics and genomic prediction. In addition, breeders and researchers can consider both GEBVs and U values to balance the needs of improving specific traits and broadening genetic diversity when selecting accessions from genebanks.
format	article
author	Matthew J. Dzievit Tingting Guo Xianran Li Jianming Yu
author_facet	Matthew J. Dzievit Tingting Guo Xianran Li Jianming Yu
author_sort	Matthew J. Dzievit
title	Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
title_short	Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
title_full	Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
title_fullStr	Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
title_full_unstemmed	Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
title_sort	comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/5ef1082fbf6742f1a6dd0095795c9824
work_keys_str_mv	AT matthewjdzievit comprehensiveanalyticalandempiricalevaluationofgenomicpredictionacrossdiverseaccessionsinmaize AT tingtingguo comprehensiveanalyticalandempiricalevaluationofgenomicpredictionacrossdiverseaccessionsinmaize AT xianranli comprehensiveanalyticalandempiricalevaluationofgenomicpredictionacrossdiverseaccessionsinmaize AT jianmingyu comprehensiveanalyticalandempiricalevaluationofgenomicpredictionacrossdiverseaccessionsinmaize
_version_	1718372557570179072

Comprehensive analytical and empirical evaluation of genomic prediction across diverse accessions in maize

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