Development of whole-genome prediction models to increase the rate of genetic gain in intermediate wheatgrass (Thinopyrum intermedium) breeding.

Abstract

The development of perennial grain crops is driven by the vision of simultaneous food production and enhanced ecosystem services. Typically, perennial crops like intermediate wheatgrass (IWG)[Thinopyrum intermedium (Host) Barkworth & D.R Dewey] have low seed yield and other detrimental traits. Next-generation sequencing has made genomic selection (GS) a tractable and viable breeding method. To investigate how an IWG breeding program may use GS, we evaluated 3,658 genets over 2 yr for 46 traits to build a training population. Six statistical models were used to evaluate the non-replicated data, and a model using autoregressive order 1 (AR1) spatial correction for rows and columns combined with the genomic relationship matrix provided the highest estimates of heritability. Genomic selection models were built from 18,357 single nucleotide polymorphism markers via genotyping-by-sequencing, and a 20-fold cross-validation showed high predictive ability for all traits (r>.80). Predictive abilities improved with increased training population size and marker numbers, even with larger amounts of missing data per marker. On the basis of these results, we propose a GS breeding method that is capable of completing one cycle per year compared with a minimum of 2 yr per cycle with phenotypic selection. We estimate that this breeding approach can increase the rate of genetic gain up to 2.6× above phenotypic selection for spike yield in IWG, allowing GS to enable rapid domestication and improvement of this crop. These breeding methods should be transferable to other species with similar long breeding cycles or limited capacity for replicated observations.