Aarhus Universitets segl

Luc Janss

Accuracy of genomic prediction in a commercial perennial ryegrass breeding program

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

  • Dario Fè
  • Bilal H. Ashraf
  • ,
  • Morten G. Pedersen, Research Division, DLF A/S
  • ,
  • Luc Janss
  • Stephen Byrne
  • ,
  • Niels Roulund, DLF A/S
  • ,
  • Ingo Lenk, DLF A/S
  • ,
  • Thomas Didion, DLF A/S
  • ,
  • Torben Asp
  • Christian S. Jensen, Research Division, DLF A/S
  • ,
  • Just Jensen

The implementation of genomic selection (GS) in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses) in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass (Lolium perenne L.) using empirical data from a commercial forage breeding program. Biparental F2 and multiparental synthetic (SYN2) families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E) interaction for some traits, accuracies were high among F2 families and between biparental F2 and multiparental SYN2 families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits.

TidsskriftPlant Genome
Antal sider12
StatusUdgivet - 1 nov. 2016

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