Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach
Brown rot, caused by Monilinia spp., is one of the most important postharvest diseases of stone fruits worldwide. Brown rot resistance in peach is a polygenic trait controlled by multiple genes with a small effect. In this study, we assessed the feasibility of genomic prediction (GP) for brown rot r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Maximum Academic Press
2022-01-01
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| Series: | Fruit Research |
| Subjects: | |
| Online Access: | https://www.maxapress.com/article/doi/10.48130/FruRes-2022-0002 |
| _version_ | 1797293141842198528 |
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| author | Wanfang Fu Cassia da Silva Linge John Mark Lawton Ksenija Gasic |
| author_facet | Wanfang Fu Cassia da Silva Linge John Mark Lawton Ksenija Gasic |
| author_sort | Wanfang Fu |
| collection | DOAJ |
| description | Brown rot, caused by Monilinia spp., is one of the most important postharvest diseases of stone fruits worldwide. Brown rot resistance in peach is a polygenic trait controlled by multiple genes with a small effect. In this study, we assessed the feasibility of genomic prediction (GP) for brown rot resistance in peach using eight contrasting methods (GBLUP, rrBLUP, BayesA, BayesB, BayesC, Bayesian Ridge Regression, Bayesian Lasso and RKHS). A testing panel of 38 cultivars/advanced selections and 288 F1 individuals from 27 pedigree-related breeding families with 'Bolinha' and/or 'Contender' or almond source of resistance was phenotyped over six seasons (2015 to 2020). GP models outperformed MAS models under five-fold cross validation, and low to moderate predictive accuracy (PA) was achieved by fitting GP model for wounded (W) (0.092−0.449) and low PA for non-wounded (NW) disease severity index (0.129−0.295). An alternative cross validation approach using disease severity index recorded in lab to predict field disease incidence (FDI) in unphenotyped accessions revealed moderate correlation (0.548−0.553). Genomic predicted breeding value distinguished accessions with low FDI from those with high FDI. The results presented here demonstrated feasibility of incorporating GP in peach breeding. |
| first_indexed | 2024-03-07T20:08:35Z |
| format | Article |
| id | doaj.art-0efdf771eab54b5bb83cdc42ac63617d |
| institution | Directory Open Access Journal |
| issn | 2769-4615 |
| language | English |
| last_indexed | 2024-03-07T20:08:35Z |
| publishDate | 2022-01-01 |
| publisher | Maximum Academic Press |
| record_format | Article |
| series | Fruit Research |
| spelling | doaj.art-0efdf771eab54b5bb83cdc42ac63617d2024-02-28T03:19:34ZengMaximum Academic PressFruit Research2769-46152022-01-012111210.48130/FruRes-2022-0002FruRes-2022-0002Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peachWanfang Fu0Cassia da Silva Linge1John Mark Lawton2Ksenija Gasic3Department of Plant and Environmental Sciences, Clemson University, Clemson, SC 29634-0002, USADepartment of Plant and Environmental Sciences, Clemson University, Clemson, SC 29634-0002, USADepartment of Plant and Environmental Sciences, Clemson University, Clemson, SC 29634-0002, USADepartment of Plant and Environmental Sciences, Clemson University, Clemson, SC 29634-0002, USABrown rot, caused by Monilinia spp., is one of the most important postharvest diseases of stone fruits worldwide. Brown rot resistance in peach is a polygenic trait controlled by multiple genes with a small effect. In this study, we assessed the feasibility of genomic prediction (GP) for brown rot resistance in peach using eight contrasting methods (GBLUP, rrBLUP, BayesA, BayesB, BayesC, Bayesian Ridge Regression, Bayesian Lasso and RKHS). A testing panel of 38 cultivars/advanced selections and 288 F1 individuals from 27 pedigree-related breeding families with 'Bolinha' and/or 'Contender' or almond source of resistance was phenotyped over six seasons (2015 to 2020). GP models outperformed MAS models under five-fold cross validation, and low to moderate predictive accuracy (PA) was achieved by fitting GP model for wounded (W) (0.092−0.449) and low PA for non-wounded (NW) disease severity index (0.129−0.295). An alternative cross validation approach using disease severity index recorded in lab to predict field disease incidence (FDI) in unphenotyped accessions revealed moderate correlation (0.548−0.553). Genomic predicted breeding value distinguished accessions with low FDI from those with high FDI. The results presented here demonstrated feasibility of incorporating GP in peach breeding.https://www.maxapress.com/article/doi/10.48130/FruRes-2022-0002genomic selectiondisease resistancebreedingfruit tree |
| spellingShingle | Wanfang Fu Cassia da Silva Linge John Mark Lawton Ksenija Gasic Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach Fruit Research genomic selection disease resistance breeding fruit tree |
| title | Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach |
| title_full | Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach |
| title_fullStr | Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach |
| title_full_unstemmed | Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach |
| title_short | Feasibility of genomic prediction for brown rot (Monilinia spp.) resistance in peach |
| title_sort | feasibility of genomic prediction for brown rot monilinia spp resistance in peach |
| topic | genomic selection disease resistance breeding fruit tree |
| url | https://www.maxapress.com/article/doi/10.48130/FruRes-2022-0002 |
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