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By Marcia MacNeil
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  • School of Integrative Plant Science
  • Plant Breeding and Genetics Section

The ultimate challenge for crop breeders is to increase genetic gain of a crop: literally, to increase the crop’s yield on farmers’ fields. Wheat and maize breeders from Cornell University, the International Maize and Wheat Improvement Center (CIMMYT) and partner institutions are working to achieve this in record time, developing new varieties tailored for farmers’ needs that are also pest- and disease-resistant, climate-resilient and nutritious.

This work is part of the Accelerating Genetic Gain in Maize and Wheat for Improved Livelihoods (AGG) project. Among other methods, breeders are using state-of-the-art novel tools such as genomic selection to achieve this ambitious goal.

In genomic selection, breeders use information about a plant’s genetic makeup along with data on its visible and measurable traits, known as phenotypic data, to “train” a model to predict how a cross will turn out — information known as “genomic estimated breeding values (GEBV)” — without having to plant seeds, wait for them to grow, and physically measure their traits. In this way, they save time and costs by reducing the number of selection cycles.

However, research is still ongoing about the best way to use genomic selection that results in the most accurate predictions and ultimately reduces selection cycle time. A recent publication by senior author Kelly Robbins, assistant professor in the Plant Breeding Section of the School of Integrative Plant Science and lead author Sikiru Atanda of CIMMYT has identified an optimal genomic selection strategy that maximizes the efficiency of this novel technology. Although this research studied CIMMYT’s maize breeding programs, AGG scientists working on wheat genetic gain and zinc nutritional content see cross-crop impacts.

“It has been a great experience working with CIMMYT as part of the Genomic Open-source Breeding Informatics Initiative (GOBii) and the Excellence in Breeding Platform (EiB),” Robbins said. “In 2017 the CIMMYT Maize Breeding Program made the decision to move forward and fully commit to routine implementation of genomic selection. The strategy was to start with implementations that improved the efficiency of the breeding programs with relatively low risks and low barriers to implementation. Since this initial implementation there has been ongoing work to continually improve the process.”

Shortening a lengthy process

In the typical breeding stages, breeders evaluate parental lines to create new crosses, and advance these lines through preliminary and elite yield trials. In the process, thousands of lines are sown, grown and analyzed, requiring considerable resources. In the traditional CIMMYT maize breeding scheme, for example, breeders conduct five stages of testing to identify parental lines for the next breeding cycle and develop high yielding hybrids that meet farmers’ needs.

In the current scheme using genomic selection, breeders phenotype 50% of a bi-parental population to predict the GEBVs of the remaining un-tested 50%. Though this reduces the cost of phenotyping, the researchers suggest it is not optimal because the breeder has to wait three to four months for the plant to grow before collecting the phenotypic data needed to calibrate the predictive model for the un-tested 50%.

The findings specify how to calibrate a model based on existing historical phenotypic and genotypic data. They also offer a method for creating “experimental” sets to generate phenotypic information when the models don’t work due to low genetic connectedness between the new population and historical data.

This presents a way forward for breeders to accelerate the early yield testing stage based on genomic information, reduce the breeding cycle time and budget, and ultimately increase genetic gain.

“This new research lays the foundation for a significant shift in the CIMMYT genomic selection strategy, moving from implementations focused on increasing selection intensity to implementations that reduce the generation interval, a key component in genetic gain achieved per year,” Robbins said. “This will have a significant impact on genetic gains moving forward.”

Effective for maize and wheat

Atanda, who now works on the use of novel breeding methods to enhance grain zinc content in CIMMYT’s wheat breeding program, believes these findings apply to wheat breeding as well.

“The implications of the research in maize are the same in wheat: accelerating early testing stage and reducing the breeding budget, which ultimately results in increasing genetic gain,” he said.

This article is adapted from a story on the CIMMYT website

Top photo: Experimental wheat varieties grow under severe drought stress near Njoro, Kenya. Photo by Chris Knight for the Borlaug Global Rust Initiative 

Kelly Robbins

Kelly Robbins is an assistant professor in the Plant Breeding and Genetics Section of the School of Integrative Plant Science. Robbins is the lead of the Breeding Informatics component of the Feed the Future Innovation Lab for Crop Improvement and is the lead of the Bioinformatics, Biometrics and Data Management Module of the Excellence in Breeding Platform. His expertise is in quantitative genetics, plant breeding and animal breeding with 7 years of private sector experience working in seeds and traits R&D.

Head shot of Kelly Robbins

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