Experimental evaluation of genomic selection prediction for rust resistance in sugarcane

Abstract The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To inv...

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Autores principales:	Md S. Islam, Per H. McCord, Marcus O. Olatoye, Lifang Qin, Sushma Sood, Alexander Edward Lipka, James R. Todd
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/7d0d41588562430ba7895bb7695953a4
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spelling	oai:doaj.org-article:7d0d41588562430ba7895bb7695953a42021-12-05T07:50:11ZExperimental evaluation of genomic selection prediction for rust resistance in sugarcane1940-337210.1002/tpg2.20148https://doaj.org/article/7d0d41588562430ba7895bb7695953a42021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20148https://doaj.org/toc/1940-3372Abstract The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using fivefold cross‐validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.Md S. IslamPer H. McCordMarcus O. OlatoyeLifang QinSushma SoodAlexander Edward LipkaJames R. ToddWileyarticlePlant 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 Md S. Islam Per H. McCord Marcus O. Olatoye Lifang Qin Sushma Sood Alexander Edward Lipka James R. Todd Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
description	Abstract The total sugarcane (Saccharum L.) production has increased worldwide; however, the rate of growth is lower compared with other major crops, mainly due to a plateauing of genetic gain. Genomic selection (GS) has proven to substantially increase the rate of genetic gain in many crops. To investigate the utility of GS in future sugarcane breeding, a field trial was conducted using 432 sugarcane clones using an augmented design with two replications. Two major diseases in sugarcane, brown and orange rust (BR and OR), were screened artificially using whorl inoculation method in the field over two crop cycles. The genotypic data were generated through target enrichment sequencing technologies. After filtering, a set of 8,825 single nucleotide polymorphic markers were used to assess the prediction accuracy of multiple GS models. Using fivefold cross‐validation, we observed GS prediction accuracies for BR and OR that ranged from 0.28 to 0.43 and 0.13 to 0.29, respectively, across two crop cycles and combined cycles. The prediction ability further improved by including a known major gene for resistance to BR as a fixed effect in the GS model. It also substantially reduced the minimum number of markers and training population size required for GS. The nonparametric GS models outperformed the parametric GS suggesting that nonadditive genetic effects could contribute genomic sources underlying BR and OR. This study demonstrated that GS could potentially predict the genomic estimated breeding value for selecting the desired germplasm for sugarcane breeding for disease resistance.
format	article
author	Md S. Islam Per H. McCord Marcus O. Olatoye Lifang Qin Sushma Sood Alexander Edward Lipka James R. Todd
author_facet	Md S. Islam Per H. McCord Marcus O. Olatoye Lifang Qin Sushma Sood Alexander Edward Lipka James R. Todd
author_sort	Md S. Islam
title	Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
title_short	Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
title_full	Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
title_fullStr	Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
title_full_unstemmed	Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
title_sort	experimental evaluation of genomic selection prediction for rust resistance in sugarcane
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/7d0d41588562430ba7895bb7695953a4
work_keys_str_mv	AT mdsislam experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane AT perhmccord experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane AT marcusoolatoye experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane AT lifangqin experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane AT sushmasood experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane AT alexanderedwardlipka experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane AT jamesrtodd experimentalevaluationofgenomicselectionpredictionforrustresistanceinsugarcane
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Experimental evaluation of genomic selection prediction for rust resistance in sugarcane

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