Development of Intermediate Wheatgrass as a Next-Generation Sustainable Crop Using Genomics-Assisted Breeding & Domestication

Prabin Bajgain1, Xiaofei Zhang2, and James A. Anderson1

1Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN2The Alliance of Bioversity International and International Center for Tropical Agriculture, Cali, Colombia

Approach: Traditional BreedingWe evaluate our single-plant selection nursery at two MN locations for 2-3 years for several agronomic and domestication traits.

The best plants are replicated and inter-mated in crossing-blocks to develop synthetic populations which are evaluated for 2-3 years in state-wide variety trials.

Our traditional breeding pipeline is supported by genomic prediction based selection approach. We first carry out association mapping in a breeding population (single plant selection nursery) to identify regions associated with important traits of interest.

Genomic prediction is then carried out in a larger population (~3000 individuals) with genetic information but no field data.

Genomics-assisted Breeding

Genomic Prediction in IWG

Trained models are then applied to a non-phenotyped breeding population to obtain genome estimated breeding values (GEBVs). Plants with the best GEBV estimates are used as parents of next breeding cycle. Prediction models that include genotype by environment (GxE) interaction effects give best predictions, as shown in the figure below.

Genetic gain estimates from the prediction models were relatively high for important traits. Our results confirm that GS can accelerate IWG domestication by increasing genetic gain per breeding cycle and assist in selection of genotypes with promise of better performance in diverse environments.

Our genomic prediction models are trained using 5000-7000 genome-wide markers and phenotypic data obtained on a population evaluated at two locations for 2-3 years.

Funding for this work came from The Forever Green Initiative and The Initiative of Renewable Energy & the Environment at the University of Minnesota, General Mills Foundation, and Minnesota Dept. of Agriculture. We thank the University of Minnesota Supercomputing Institute for providing computational resources and tools for our work. At least 50 TB disk space, 25 TB memory, and 250K SUs were used during 2013-2020 to generate the results presented in this poster.

Acknowledgements

• Reduced soil erosion • Increased carbon sequestration• Reduced N and P contamination of

freshwater and marine ecosystems by lowering runoff of surface nutrients

• Minimal need of tillage, herbicide, fungicide applications

BackgroundIntermediate wheatgrass (IWG, Thinopyrum intermedium) is a novel perennial grain crop. It is a distant relative of wheat and is widely known as Kernza®, the crop’s trade name. In addition to producing nutritious grain, IWG has an extensive root structure and provides year-round soil coverage thereby offering the following benefits that contribute towards sustainable & regenerative agriculture:

Breeding & Domestication

IWG breeding and domestication program at the University of Minnesota was initiated in 2011. The major goal of the program is to improve the crop’s profitability by improving IWG germplasm in following areas:

In order to expedite breeding grains and increase selection efficiency, we rely on genomic tools such as genome-wide molecular markers, genetic mapping of important traits, and genomic selection.

• Grain yield• Larger grain size• Improved spike characteristics• Reduced seed shatter• Higher free grain threshing• Disease resistance• Reduced lodging• Better end-use quality traits

Trait σ2A-A σ2

A-AD σP i rA rAD

Genetic GainA

Genetic GainAD

Spike Weight 0.01 0.01 0.26 1.03 0.39 0.37 0.03 0.02Spike Length 2.97 1.56 3.30 0.83 0.48 0.48 0.20 0.14No. of Spikelets 1.37 1.37 2.44 1.04 0.42 0.42 0.07 0.07Shatter Resistance 0.39 0.23 1.03

1.07 0.63 0.62 0.13 0.10

Threshability 0.26 0.26 0.91 1.17 0.53 0.52 0.13 0.13

Grain Yield 41.92 41.9218.0

60.95 0.40 0.39 1.88 1.82

TKW 0.75 0.67 1.31 1.04 0.64 0.64 0.76 0.72Seed Length 0.09 0.06 0.45 1.01 0.66 0.67 0.12 0.11Seed Width 0.01 0.00 0.14 1.10 0.35 0.40 0.02 0.00

σ2A-A and σ2

A-AD, additive variance calculated using additive only and additive + dominance models, respectively; i, selection intensity; σP, phenotypic distribution standard deviation; rA and rAD, highest predictive abilities obtained in models with additive only and additive + dominance effects, respectively.

Predictions with G×E effects fitted in prediction models MM, MDe, and MDs. Bars with dashed borders in red are the highest predictions obtained when G×E effects were not accounted for.

The world’s first food-grade IWG cultivar, ‘MN-Clearwater’, was released by the University of Minnesota in 2019.