Literature DB >> 27491433

Advances and Challenges in Genomic Selection for Disease Resistance.

Jesse Poland1, Jessica Rutkoski2,3.   

Abstract

Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races. In contrast, breeding for minor-gene quantitative resistance tends to produce more durable varieties but is a more challenging breeding objective. As the genetic architecture of resistance shifts from single major R genes to a diffused architecture of many minor genes, the best approach for molecular breeding will shift from marker-assisted selection to genomic selection. Genomics-assisted breeding for quantitative resistance will therefore necessitate whole-genome prediction models and selection methodology as implemented for classical complex traits such as yield. Here, we examine multiple case studies testing whole-genome prediction models and genomic selection for disease resistance. In general, whole-genome models for disease resistance can produce prediction accuracy suitable for application in breeding. These models also largely outperform multiple linear regression as would be applied in marker-assisted selection. With the implementation of genomic selection for yield and other agronomic traits, whole-genome marker profiles will be available for the entire set of breeding lines, enabling genomic selection for disease at no additional direct cost. In this context, the scope of implementing genomics selection for disease resistance, and specifically for quantitative resistance and quarantined pathogens, becomes a tractable and powerful approach in breeding programs.

Keywords:  genomic selection; plant breeding; quantitative disease resistance

Mesh:

Year:  2016        PMID: 27491433     DOI: 10.1146/annurev-phyto-080615-100056

Source DB:  PubMed          Journal:  Annu Rev Phytopathol        ISSN: 0066-4286            Impact factor:   13.078


  34 in total

Review 1.  Navigating complexity to breed disease-resistant crops.

Authors:  Rebecca Nelson; Tyr Wiesner-Hanks; Randall Wisser; Peter Balint-Kurti
Journal:  Nat Rev Genet       Date:  2017-11-07       Impact factor: 53.242

Review 2.  Advances in Multi-Omics Approaches for Molecular Breeding of Black Rot Resistance in Brassica oleracea L.

Authors:  Ranjan K Shaw; Yusen Shen; Jiansheng Wang; Xiaoguang Sheng; Zhenqing Zhao; Huifang Yu; Honghui Gu
Journal:  Front Plant Sci       Date:  2021-12-06       Impact factor: 5.753

Review 3.  Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

Authors:  C Anilkumar; N C Sunitha; Narayana Bhat Devate; S Ramesh
Journal:  Planta       Date:  2022-09-23       Impact factor: 4.540

4.  Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice.

Authors:  Blaise Pascal Muvunyi; Wenli Zou; Junhui Zhan; Sang He; Guoyou Ye
Journal:  Front Genet       Date:  2022-06-22       Impact factor: 4.772

5.  An experimental approach for estimating the genomic selection advantage for Fusarium head blight and Septoria tritici blotch in winter wheat.

Authors:  Cathérine Pauline Herter; Erhard Ebmeyer; Sonja Kollers; Viktor Korzun; Thomas Miedaner
Journal:  Theor Appl Genet       Date:  2019-05-29       Impact factor: 5.699

6.  Molecular tracking of multiple disease resistance in a winter wheat diversity panel.

Authors:  Thomas Miedaner; Wessam Akel; Kerstin Flath; Andreas Jacobi; Mike Taylor; Friedrich Longin; Tobias Würschum
Journal:  Theor Appl Genet       Date:  2019-11-13       Impact factor: 5.699

7.  A deep convolutional neural network approach for predicting phenotypes from genotypes.

Authors:  Wenlong Ma; Zhixu Qiu; Jie Song; Jiajia Li; Qian Cheng; Jingjing Zhai; Chuang Ma
Journal:  Planta       Date:  2018-08-12       Impact factor: 4.116

Review 8.  Rapid gene cloning in cereals.

Authors:  Jan Bettgenhaeuser; Simon G Krattinger
Journal:  Theor Appl Genet       Date:  2018-10-19       Impact factor: 5.699

9.  Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height.

Authors:  Philomin Juliana; Xinyao He; Jesse Poland; Krishna K Roy; Paritosh K Malaker; Vinod K Mishra; Ramesh Chand; Sandesh Shrestha; Uttam Kumar; Chandan Roy; Navin C Gahtyari; Arun K Joshi; Ravi P Singh; Pawan K Singh
Journal:  Theor Appl Genet       Date:  2022-04-13       Impact factor: 5.574

Review 10.  Biotechnological Resources to Increase Disease-Resistance by Improving Plant Immunity: A Sustainable Approach to Save Cereal Crop Production.

Authors:  Valentina Bigini; Francesco Camerlengo; Ermelinda Botticella; Francesco Sestili; Daniel V Savatin
Journal:  Plants (Basel)       Date:  2021-06-04
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.