Enhancing Crop Domestication Through Genomic Selection, a Case Study of Intermediate Wheatgrass

Abstract

Perennial grains could simultaneously provide food for humans and a host of ecosystem services, including reduced erosion, minimized nitrate leaching, and increased carbon capture. Yet most of the world's food and feed is supplied by annual grains. Efforts to domesticate intermediate wheatgrass (Thinopyrumn intermedium, IWG) as a perennial grain crop have been ongoing since the 1980's. Currently, there are several breeding programs within North America and Europe working toward developing IWG into a viable crop. As new breeding efforts are established to provide a widely adapted crop, questions of how genomic and phenotypic data can be used among sites and breeding programs have emerged. Utilizing five cycles of breeding data that span 8 years and two breeding programs, University of Minnesota, St. Paul, MN, and The Land Institute, Salina, KS, we developed genomic selection (GS) models to predict IWG traits. Seven traits were evaluated with free-threshing seed, seed mass, and non-shattering being considered domestication traits while agronomic traits included spike yield, spikelets per inflorescence, plant height, and spike length. We used 6,199 genets -unique, heterozygous, individual plants -that had been profiled with genotyping-by-sequencing, resulting in 23,495 SNP markers to develop GS models. Within cycles, the predictive ability of GS was high, ranging from 0.11 to 0.97. Acrosscycle predictions were generally much lower, ranging from −0.22 to 0.76. The prediction ability for domestication traits was higher than agronomic traits, with non-shattering and free threshing prediction abilities ranging from 0.27 to 0.75 whereas spike yield had prediction abilities ranging from −0.22 to 0.26. These results suggest that progress to reduce shattering and increase the percent free-threshing grain can be made irrespective of the location and breeding program. While site-specific programs may be required for agronomic traits, synergies can be achieved in rapidly improving key domestication traits for IWG. As other species are targeted for domestication, these results will aid in rapidly domesticating new crops.

Genomic prediction enables rapid selection of high‐performing genets in an intermediate wheatgrass breeding program

“…The low prediction value for spike yield could be due to the amount of environmental variance and genotype-byenvironment that impact this trait from year to year. Crain et al (2020a) showed similar results that traits with less genotypeby-environment interaction-free threshing and shattering-T A B L E 3 TLI-Cycle 7 traits measured in 2019 for two groups: validation population and the selected parents. The mean for each group is reported along with p value for group effect in the model.…”

‘MN‐Clearwater’, the first food‐grade intermediate wheatgrass (Kernza perennial grain) cultivar

“…This collective effort to improve IWG also has resulted in a complete genome sequence ( Thinopyrum intermedium v2.1 DOE‐JGI, https://phytozome-next.jgi.doe.gov/info/Tintermedium_v2_1/). Both TLI and UMN researchers have been leveraging the genome sequence to significantly reduce their labor‐intensive phenotypic breeding schemes with genomic selection, which is a DNA marker‐ and statistics‐based selection approach (Bajgain et al., 2019a, 2020; Crain et al., 2020; Zhang et al., 2016).…”

New Food Crop Domestication in the Age of Gene Editing: Genetic, Agronomic and Cultural Change Remain Co-evolutionarily Entangled

“…Forage varieties were included along with wild accessions in the starting population from which the Rodale Institute began its selections (Wagoner and Schauer, 1990). In the early 2000s, The Land Institute revived the intermediate wheatgrass domestication program and has since performed nine cycles of recurrent selection; three cycles have been completed at the University of Minnesota (Crain et al, 2020). Recent cycles of selection use genomic selection to accelerate genetic gains for domestication traits and cereal grain yield (Zhang et al, 2016).…”