The University of Minnesota has been domesticating the perennial forage intermediate wheatgrass (IWG) since 2011 using a combination of conventional methods and modern breeding tools such as genomic selection. Globally, most IWG selection nurseries are spaced-planted individuals of several hundred genotypes whereas commercial fields established for grain production are row-planted panmictic populations. This study evaluated genomic prediction models and estimated genetic gain in yield and agronomic performance of row-planted IWG half-sib families assessed over 3 years and 2 locations, Lamberton and St. Paul, MN, USA. The strongest trait correlation was negative (r = −0.49) between 2023 St. Paul height and 2022 St. Paul seed size. The three St. Paul environments were more similar for plant height and seed size and so were the Lamberton environments yet no specific trend was observed for grain yield. Evaluation of different univariate and multivariate genomic prediction models showed that multivariate models outperformed the best univariate models by 23 percentage points, yet no single multivariate model was the best predictor of all traits. Cross-environment predictions were the best among St. Paul environments and no single environment was the best predictor of the remaining environments. Genetic gain estimates indicated a 20 kg ha-1 increase in grain yield and 3 cm reduction in plant height per breeding cycle. While no single model predicted all traits with high accuracy, results obtained in this study suggest that evaluating IWG sibs in row plots followed by genomic trait predictions could lead to desired breeding progress for desired traits.
Breeding spring wheat for stable pre-harvest sprouting (PHS) resistance is constrained by the environmental sensitivity of seed dormancy and the need to maintain agronomic performance. This study integrated multi-environment phenotyping and multi-model GWAS to identify breeding resources in Kazakh spring wheat. A panel...
A. Amalova, Y. Genievskaya, Chudinov Vladimir et al.· Agriculture· 0 citations
Abstract Switchgrass (Panicum virgatum L.) improvement requires selection methods that remain effective across environments. Biomass yield is strongly influenced by genotype‐by‐environment (G × E) interaction. We evaluated genomic prediction models for biomass yield, spring emergence (SE), and flowering time (FT) in ha...
Jazib Ali Irfan, C. Abeyratne, H. B. Chhetri et al.· The Plant Genome· 0 citations
Perennial grains represent a promising frontier for sustainable agriculture, but breeding progress is constrained by the accessibility of genotyping and the difficulty of evaluating complex traits expressed for multiple years after establishment across heterogeneous environments. Phenomic selection may help address the...
Zachary N. Harris, Jackson Braley, Eric Cassetta et al.· bioRxiv· 0 citations
Abstract Recurrent selection methods are most suitable for improving quantitative traits in plant breeding. This study estimated genetic parameters and assessed the genetic diversity of black common bean progenies from the fourth cycle of recurrent selection. Thirty-five S0:4 progenies and four cultivars were evaluated...
E. Alves, P. G. Melo, A. G. S. Coelho et al.· Crop Breeding and Applied Bi...· 0 citations
Abstract Genomic selection (GS) is a powerful tool for accelerating genetic gain in potato (Solanum tuberosum L.) breeding, particularly for complex traits. In this study, three practical aspects of GS implementation in a potato breeding program were examined. First, the predictive ability of GS models was evaluated fo...
R. Dhakal, M. A. Peixoto, Leo Hoffmann et al.· The Plant Genome· 0 citations
The results suggest that, in elite wheat germplasm characterized by long-range linkage disequilibrium and strong realized genomic relationships, medium-density targeted genotyping platforms can retain most of the predictability achieved by higher-density systems.