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Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.
Barre, Philippe; Asp, Torben; Byrne, Stephen; Casler, Michael; Faville, Marty; Rognli, Odd Arne; Roldan-Ruiz, Isabel; Skøt, Leif; Ghesquière, Marc.
Afiliação
  • Barre P; INRAE, UR P3F, Lusignan, France. philippe.barre@inrae.fr.
  • Asp T; Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark.
  • Byrne S; Teagasc, Crop Science Department, Oak Park, Carlow, Ireland.
  • Casler M; U.S. Dairy Forage Research Center, USDA-ARS, Madison, WI, USA.
  • Faville M; AgResearch Ltd , Grasslands Research Centre, Palmerston North, New Zealand.
  • Rognli OA; Department of Plant Sciences, Faculty of Biosciences, Norwegian, University of Life Sciences (NMBU), Ås, Norway.
  • Roldan-Ruiz I; Flanders Research Institute for Agriculture, Fisheries and Food (ILVO)-Plant Sciences Unit, Melle, Belgium.
  • Skøt L; IBERS, Aberystwyth University, Ceredigion, UK.
  • Ghesquière M; INRAE, UR P3F, Lusignan, France.
Methods Mol Biol ; 2467: 521-541, 2022.
Article em En | MEDLINE | ID: mdl-35451789
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Lolium / Panicum Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Lolium / Panicum Idioma: En Ano de publicação: 2022 Tipo de documento: Article