Genome-wide association mapping and genomic prediction for kernel color traits in intermediate wheatgrass (Thinopyrum intermedium)

Abstract

Background Intermediate wheatgrass (IWG) is a novel perennial grain crop currently undergoing domestication. It offers important ecosystem benefits while producing grain suitable for human consumption. Several aspects of plant biology and genetic control are yet to be studied in this new crop. To understand trait behavior and genetic characterization of kernel color in IWG breeding germplasm from the University of Minnesota was evaluated for the CIELAB components (L*, a*, b*) and visual differences. Trait values were used in a genome-wide association scan to reveal genomic regions controlling IWG’s kernel color. The usability of genomic prediction in predicting kernel color traits was also evaluated using a four-fold cross validation method. Results A wide phenotypic variation was observed for all four kernel color traits with pairwise trait correlations ranging from − 0.85 to 0.27. Medium to high estimates of broad sense trait heritabilities were observed and ranged from 0.41 to 0.78. A genome-wide association scan with single SNP markers detected 20 significant marker-trait associations in 9 chromosomes and 23 associations in 10 chromosomes using multi-allelic haplotype blocks. Four of the 20 significant SNP markers and six of the 23 significant haplotype blocks were common between two or more traits. Evaluation of genomic prediction of kernel color traits revealed the visual score to have highest mean predictive ability (r2 = 0.53); r2 for the CIELAB traits ranged from 0.29–0.33. A search for candidate genes led to detection of seven IWG genes in strong alignment with MYB36 transcription factors from other cereal crops of the Triticeae tribe. Three of these seven IWG genes had moderate similarities with R-A1, R-B1, and R-D1, the three genes that control grain color in wheat. Conclusions We characterized the distribution of kernel color in IWG for the first time, which revealed a broad phenotypic diversity in an elite breeding germplasm. Identification of genetic loci controlling the trait and a proof-of-concept that genomic selection might be useful in selecting genotypes of interest could help accelerate the breeding of this novel crop towards specific end-use.

Adapting perennial grain and oilseed crops for climate resiliency

“…Methods are being developed, such as ground penetrating radar and light detection and ranging, to understand how to use remote sensing to estimate belowground biomass and understand biotic and abiotic stress (Bellvert et al., 2021; Ferrara et al., 2014; George et al., 2019). In addition, the application of near‐infrared spectroscopy in characterizing forage properties (Norman et al., 2020), digital images as well as machine learning models in measuring grain characteristics (Bajgain & Anderson, 2021; Bajgain et al., 2022), and other phenomics and automation tools (Rubin et al., 2022) have shown promise in expediting domestication, improvement, and adaptation of perennial crops.…”