Literature DB >> 35451789

Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.

Philippe Barre1, Torben Asp2, Stephen Byrne3, Michael Casler4, Marty Faville5, Odd Arne Rognli6, Isabel Roldan-Ruiz7, Leif Skøt8, Marc Ghesquière9.   

Abstract

The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Forage grasses; GBS; Genomic selection; Outbreeding; Perennial ryegrass; Sward; Switchgrass; Synthetics; Timothy

Mesh:

Year:  2022        PMID: 35451789     DOI: 10.1007/978-1-0716-2205-6_19

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  31 in total

1.  Genome size and GC content evolution of Festuca: ancestral expansion and subsequent reduction.

Authors:  Petr Smarda; Petr Bures; Lucie Horová; Bruno Foggi; Graziano Rossi
Journal:  Ann Bot       Date:  2007-12-24       Impact factor: 4.357

2.  The Plant DNA C-values database (release 7.1): an updated online repository of plant genome size data for comparative studies.

Authors:  Jaume Pellicer; Ilia J Leitch
Journal:  New Phytol       Date:  2019-11-08       Impact factor: 10.151

3.  A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species.

Authors:  Robert J Elshire; Jeffrey C Glaubitz; Qi Sun; Jesse A Poland; Ken Kawamoto; Edward S Buckler; Sharon E Mitchell
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

4.  Temporal changes in genetic diversity and forage yield of perennial ryegrass in monoculture and in combination with red clover in swards.

Authors:  Christophe Verwimp; Tom Ruttink; Hilde Muylle; Sabine Van Glabeke; Gerda Cnops; Paul Quataert; Olivier Honnay; Isabel Roldán-Ruiz
Journal:  PLoS One       Date:  2018-11-08       Impact factor: 3.240

5.  High-Throughput Genome-Wide Genotyping To Optimize the Use of Natural Genetic Resources in the Grassland Species Perennial Ryegrass (Lolium perenne L.).

Authors:  Thomas Keep; Jean-Paul Sampoux; José Luis Blanco-Pastor; Klaus J Dehmer; Matthew J Hegarty; Thomas Ledauphin; Isabelle Litrico; Hilde Muylle; Isabel Roldán-Ruiz; Anna M Roschanski; Tom Ruttink; Fabien Surault; Evelin Willner; Philippe Barre
Journal:  G3 (Bethesda)       Date:  2020-09-02       Impact factor: 3.154

6.  Genome wide allele frequency fingerprints (GWAFFs) of populations via genotyping by sequencing.

Authors:  Stephen Byrne; Adrian Czaban; Bruno Studer; Frank Panitz; Christian Bendixen; Torben Asp
Journal:  PLoS One       Date:  2013-03-04       Impact factor: 3.240

7.  Genetic-geographic correlation revealed across a broad European ecotypic sample of perennial ryegrass (Lolium perenne) using array-based SNP genotyping.

Authors:  T Blackmore; I Thomas; R McMahon; W Powell; M Hegarty
Journal:  Theor Appl Genet       Date:  2015-06-21       Impact factor: 5.699

8.  Association studies using family pools of outcrossing crops based on allele-frequency estimates from DNA sequencing.

Authors:  Bilal H Ashraf; Just Jensen; Torben Asp; Luc L Janss
Journal:  Theor Appl Genet       Date:  2014-03-26       Impact factor: 5.699

9.  Optimized Use of Low-Depth Genotyping-by-Sequencing for Genomic Prediction Among Multi-Parental Family Pools and Single Plants in Perennial Ryegrass (Lolium perenne L.).

Authors:  Fabio Cericola; Ingo Lenk; Dario Fè; Stephen Byrne; Christian S Jensen; Morten G Pedersen; Torben Asp; Just Jensen; Luc Janss
Journal:  Front Plant Sci       Date:  2018-03-21       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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