Genomic prediction of complex traits in forage plants species: Perennial grasses

Authors: BARRE, PHILIPPE - Inrae; ASP, TORBEN - Aarhuis University; BYRNE, STEPHEN - Teagasc (AGRICULTURE AND FOOD DEVELOPMENT AUTHORITY); Casler, Michael; FAVILLE, MARTY - Agresearch; ROGNLI, ODD ARNE - Norwegian University Of Life Sciences; ROLDAN-RUIZ, ISABEL - Flanders Research Institute For Agriculture; SKOT, LEIF - Aberystwyth University; GHESQUIERE, MARC - Inrae

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 1/28/2021
Publication Date: 12/5/2022
Citation: Barre, P., Asp, T., Byrne, S., Casler, M.D., Faville, M., Rognli, O., Roldan-Ruiz, I., Skot, L., Ghesquiere, M. 2022. Genomic prediction of complex traits in forage plants species: Perennial grasses case. Ahmadi, N., Bartholomé, J., editors. Methods in Molecular Biology. Vol 2467. Humana, New York, NY. p. 521-541. https://doi.org/10.1007/978-1-0716-2205-6_19.
DOI: https://doi.org/10.1007/978-1-0716-2205-6_19

Interpretive Summary:

Technical 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 enabled breeders to consider GS also 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 revealed that GS should increase significantly the genetic gain per unit of time for traits with different levels of heritability. Main reasons are (i) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (ii) the reduction of the duration selection cycles, and less importantly (iii) the increase of the selection intensity associated with an increase of 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 (lower than expected <40,000 but on targeted loci) 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 x environment interaction (GxE). Finally, in these outbreeding species, inbreeding should be taken into account to counterbalance the high selection intensity that can be achieved in GS.