Literature DB >> 27898764

Genetic Gain and Inbreeding from Genomic Selection in a Simulated Commercial Breeding Program for Perennial Ryegrass.

Zibei Lin, Noel O I Cogan, Luke W Pembleton, German C Spangenberg, John W Forster, Ben J Hayes, Hans D Daetwyler.   

Abstract

Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass () improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD) in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot). Genomic estimated breeding values (GEBVs) for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively). Higher accuracy of GEBVs was obtained for flowering time (up to 0.7), partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy.
Copyright © 2016 Crop Science Society of America.

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Year:  2016        PMID: 27898764     DOI: 10.3835/plantgenome2015.06.0046

Source DB:  PubMed          Journal:  Plant Genome        ISSN: 1940-3372            Impact factor:   4.089


  21 in total

1.  Influence of Pollen Dispersal and Mating Pattern in Domestication of Intermediate Wheatgrass, a Novel Perennial Food Crop.

Authors:  Prabin Bajgain; Yaniv Brandvain; James A Anderson
Journal:  Front Plant Sci       Date:  2022-04-28       Impact factor: 6.627

Review 2.  Accelerating crop genetic gains with genomic selection.

Authors:  Kai Peter Voss-Fels; Mark Cooper; Ben John Hayes
Journal:  Theor Appl Genet       Date:  2018-12-19       Impact factor: 5.699

3.  Mitigation of inbreeding while preserving genetic gain in genomic breeding programs for outbred plants.

Authors:  Zibei Lin; Fan Shi; Ben J Hayes; Hans D Daetwyler
Journal:  Theor Appl Genet       Date:  2017-03-31       Impact factor: 5.699

4.  Genomic prediction of starch content and chipping quality in tetraploid potato using genotyping-by-sequencing.

Authors:  Elsa Sverrisdóttir; Stephen Byrne; Ea Høegh Riis Sundmark; Heidi Øllegaard Johnsen; Hanne Grethe Kirk; Torben Asp; Luc Janss; Kåre L Nielsen
Journal:  Theor Appl Genet       Date:  2017-07-13       Impact factor: 5.699

Review 5.  Toward Genomics-Based Breeding in C3 Cool-Season Perennial Grasses.

Authors:  Shyamal K Talukder; Malay C Saha
Journal:  Front Plant Sci       Date:  2017-07-26       Impact factor: 5.753

6.  Genomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing.

Authors:  Xiangyu Guo; Fabio Cericola; Dario Fè; Morten G Pedersen; Ingo Lenk; Christian S Jensen; Just Jensen; Lucas L Janss
Journal:  Front Plant Sci       Date:  2018-08-15       Impact factor: 5.753

7.  Validation of Genotyping by Sequencing Using Transcriptomics for Diversity and Application of Genomic Selection in Tetraploid Potato.

Authors:  B M Caruana; L W Pembleton; F Constable; B Rodoni; A T Slater; N O I Cogan
Journal:  Front Plant Sci       Date:  2019-05-29       Impact factor: 5.753

8.  Historical Datasets Support Genomic Selection Models for the Prediction of Cotton Fiber Quality Phenotypes Across Multiple Environments.

Authors:  Washington Gapare; Shiming Liu; Warren Conaty; Qian-Hao Zhu; Vanessa Gillespie; Danny Llewellyn; Warwick Stiller; Iain Wilson
Journal:  G3 (Bethesda)       Date:  2018-05-04       Impact factor: 3.154

9.  Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial.

Authors:  Alem Gebremedhin; Pieter Badenhorst; Junping Wang; Fan Shi; Ed Breen; Khageswor Giri; German C Spangenberg; Kevin Smith
Journal:  Front Plant Sci       Date:  2020-05-28       Impact factor: 5.753

10.  Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

Authors:  Luke W Pembleton; Courtney Inch; Rebecca C Baillie; Michelle C Drayton; Preeti Thakur; Yvonne O Ogaji; German C Spangenberg; John W Forster; Hans D Daetwyler; Noel O I Cogan
Journal:  Theor Appl Genet       Date:  2018-06-02       Impact factor: 5.699

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