Literature DB >> 33587151

Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding.

Kai P Voss-Fels1, Xianming Wei2, Elizabeth M Ross1, Matthias Frisch3, Karen S Aitken4, Mark Cooper1, Ben J Hayes5.   

Abstract

KEY MESSAGE: Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

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Year:  2021        PMID: 33587151     DOI: 10.1007/s00122-021-03785-3

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


  29 in total

1.  Bayesian estimation of marker dosage in sugarcane and other autopolyploids.

Authors:  Peter Baker; Phillip Jackson; Karen Aitken
Journal:  Theor Appl Genet       Date:  2010-02-25       Impact factor: 5.699

2.  Genetic Variance Partitioning and Genome-Wide Prediction with Allele Dosage Information in Autotetraploid Potato.

Authors:  Jeffrey B Endelman; Cari A Schmitz Carley; Paul C Bethke; Joseph J Coombs; Mark E Clough; Washington L da Silva; Walter S De Jong; David S Douches; Curtis M Frederick; Kathleen G Haynes; David G Holm; J Creighton Miller; Patricio R Muñoz; Felix M Navarro; Richard G Novy; Jiwan P Palta; Gregory A Porter; Kyle T Rak; Vidyasagar R Sathuvalli; Asunta L Thompson; G Craig Yencho
Journal:  Genetics       Date:  2018-03-07       Impact factor: 4.562

3.  A combination of AFLP and SSR markers provides extensive map coverage and identification of homo(eo)logous linkage groups in a sugarcane cultivar.

Authors:  K S Aitken; P A Jackson; C L McIntyre
Journal:  Theor Appl Genet       Date:  2005-02-08       Impact factor: 5.699

Review 4.  Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product.

Authors:  Mark Cooper; Carla Gho; Roger Leafgren; Tom Tang; Carlos Messina
Journal:  J Exp Bot       Date:  2014-03-04       Impact factor: 6.992

5.  Long-term genomic selection for heterosis without dominance in multiplicative traits: case study of bunch production in oil palm.

Authors:  David Cros; Marie Denis; Jean-Marc Bouvet; Leopoldo Sánchez
Journal:  BMC Genomics       Date:  2015-08-29       Impact factor: 3.969

6.  A comprehensive genetic map of sugarcane that provides enhanced map coverage and integrates high-throughput Diversity Array Technology (DArT) markers.

Authors:  Karen S Aitken; Meredith D McNeil; Scott Hermann; Peter C Bundock; Andrzej Kilian; Katarzyna Heller-Uszynska; Robert J Henry; Jingchuan Li
Journal:  BMC Genomics       Date:  2014-02-24       Impact factor: 3.969

7.  Genomic Prediction of Autotetraploids; Influence of Relationship Matrices, Allele Dosage, and Continuous Genotyping Calls in Phenotype Prediction.

Authors:  Ivone de Bem Oliveira; Marcio F R Resende; Luis Felipe V Ferrão; Rodrigo R Amadeu; Jeffrey B Endelman; Matias Kirst; Alexandre S G Coelho; Patricio R Munoz
Journal:  G3 (Bethesda)       Date:  2019-04-09       Impact factor: 3.154

8.  SNP genotyping allows an in-depth characterisation of the genome of sugarcane and other complex autopolyploids.

Authors:  Antonio A F Garcia; Marcelo Mollinari; Thiago G Marconi; Oliver R Serang; Renato R Silva; Maria L C Vieira; Renato Vicentini; Estela A Costa; Melina C Mancini; Melissa O S Garcia; Maria M Pastina; Rodrigo Gazaffi; Eliana R F Martins; Nair Dahmer; Danilo A Sforça; Claudio B C Silva; Peter Bundock; Robert J Henry; Glaucia M Souza; Marie-Anne van Sluys; Marcos G A Landell; Monalisa S Carneiro; Michel A G Vincentz; Luciana R Pinto; Roland Vencovsky; Anete P Souza
Journal:  Sci Rep       Date:  2013-12-02       Impact factor: 4.379

9.  Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghum.

Authors:  Samuel B Fernandes; Kaio O G Dias; Daniel F Ferreira; Patrick J Brown
Journal:  Theor Appl Genet       Date:  2017-12-07       Impact factor: 5.699

Review 10.  Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE.

Authors:  Rex Bernardo
Journal:  Heredity (Edinb)       Date:  2020-04-15       Impact factor: 3.821

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  4 in total

1.  Genomic prediction with allele dosage information in highly polyploid species.

Authors:  Lorena G Batista; Victor H Mello; Anete P Souza; Gabriel R A Margarido
Journal:  Theor Appl Genet       Date:  2021-11-20       Impact factor: 5.699

Review 2.  Genomic Selection in Sugarcane: Current Status and Future Prospects.

Authors:  Channappa Mahadevaiah; Chinnaswamy Appunu; Karen Aitken; Giriyapura Shivalingamurthy Suresha; Palanisamy Vignesh; Huskur Kumaraswamy Mahadeva Swamy; Ramanathan Valarmathi; Govind Hemaprabha; Ganesh Alagarasan; Bakshi Ram
Journal:  Front Plant Sci       Date:  2021-09-27       Impact factor: 5.753

Review 3.  Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits.

Authors:  Mintu Ram Meena; Chinnaswamy Appunu; R Arun Kumar; R Manimekalai; S Vasantha; Gopalareddy Krishnappa; Ravinder Kumar; S K Pandey; G Hemaprabha
Journal:  Front Genet       Date:  2022-08-03       Impact factor: 4.772

Review 4.  Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane.

Authors:  Karansher Singh Sandhu; Aalok Shiv; Gurleen Kaur; Mintu Ram Meena; Arun Kumar Raja; Krishnapriya Vengavasi; Ashutosh Kumar Mall; Sanjeev Kumar; Praveen Kumar Singh; Jyotsnendra Singh; Govind Hemaprabha; Ashwini Dutt Pathak; Gopalareddy Krishnappa; Sanjeev Kumar
Journal:  Plants (Basel)       Date:  2022-08-17
  4 in total

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