Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat-Crop Breeding, Genetics and Genomics-CSCIED科技核心评价数据库-手机版

Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat

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DOI	10.20900/cbgg20200016
刊名	Crop Breeding, Genetics and Genomics CBGG
年，卷(期)	2020, 2(4)
作者	Jemanesh K. Haile*,Amidou N’Diaye,Ehsan Sari,Sean Walkowiak,Jessica E. Rutkoski,Hadley R. Kutcher,Curtis J. Pozniak,
作者单位	Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada ; Grain Research Laboratory, Canadian Grain Commission, Winnipeg, MB R3C 3G8, Canada ; Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA ;
Abstract	Diseases are among the most important limiting factors for wheat production. Breeding for fungal diseases of wheat, primarily for rusts and Fusarium head blight (FHB), are major resource consuming activities in most breeding programs which prevent breeders from focusing entirely on improving yield. Breeding for these diseases is challenging because resistance is inherited mostly in a quantitative fashion and is greatly influenced by weather conditions. Recent advances in genomics, phenomics and big-data analysis provide opportunities for accelerating the development of low-cost and efficient selection methods for such complex traits. Genomic selection (GS) may provide opportunities for reducing the time and cost of making selections. By appropriately integrating GS in the breeding workflow, it is possible to select new parents purely based on genomic estimated breeding values before breeding materials are entered into nurseries and field trials. Due to reduced selection cycle time, annual genetic gain for GS is predicted to be two to threefold greater than for a conventional phenotypic selection program. In this paper, we review the recent GS studies focusing on the prediction of resistance to rusts and FHB including those that benefits from modeling multiple phenological traits correlated with the resistance. In addition, we discuss the potential of integrating phenomics and machine learning for evaluating plant disease and the integration of multiple “omics” data in genomic prediction to improve the applicability of GS for disease resistance breeding in wheat.
KeyWord	disease resistance; genomic selection; genetic gain; genotyping; machine learning; “omics” data; phenomics; wheat
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引用本文

Jemanesh K. Haile*,Amidou N’Diaye,Ehsan Sari,Sean Walkowiak,Jessica E. Rutkoski,Hadley R. Kutcher,Curtis J. Pozniak. Potential of Genomic Selection and Integrating “Omics” Data for Disease Evaluation in Wheat, Crop Breeding, Genetics and Genomics. 2020; 2; (4). https://doi.org/10.20900/cbgg20200016.

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