Texas, USA
October 22, 2020
Texas A&M AgriLife Researchers are collaborating on the creation of genetic tools that will help polyploid plant breeders speed up the hybridization process. Tools could be a game-changer for polyploid specialty crops such as roses and and other food crops. (Texas A&M AgriLife photo)
Texas A&M AgriLife Research plant breeders are collaborating with an international, interdisciplinary group of scientists to enhance a genetics tool set that could be a game-changer for breeding new varieties of polyploid plants such as potatoes, wheat and turfgrass.
David Byrne, Ph.D., AgriLife Research rose breeder and geneticist, and Oscar Riera-Lizarazu, Ph.D., AgriLife Research plant geneticist, both in the Texas A&M University Department of Horticulture Sciences, College Station, received more than $4.3 million from the U.S. Department of Agriculture National Institute of Food and Agriculture to develop tools for genomics-assisted breeding in polyploids.
The researchers said current genomic tools are available for diploid crops such as apples, but the technology for polyploid crops has lagged. “Polyploid” means these plants have more than two sets of chromosomes in their cells. Polyploid specialty crops like roses and food crops like potatoes have an annual value of more than $9 billion in the U.S. and many times greater globally.
Tools to help polyploid breeders
Despite their importance, scientists have yet to create and implement a genomics-assisted tool for polyploids. The major obstacles for using genomic tools, based on extensive researcher and stakeholder input, are the dearth of software suitable for polyploid crops, technical expertise to create a reliable tool, and training to broaden its use.
Byrne said the genomic tools they intend to create will accelerate the rate of genetic gains in a wide range of polyploid breeding programs, which will lead to higher quality, more productive and resilient cultivars. The software will map polyploid genes, essentially locating traits of interest and link those traits to the associated DNA.
The project’s goals are to develop computational tools for quantitative trait locus analyses, genomic selection and haplotype analysis in polyploid crops and then train breeders and geneticists to use the tools for application in public breeding programs for potato, blackberry, turfgrass, kiwi, sweet potato and rose production.
The Polyploid Breeding Community Resource website will serve as the repository for the computational toolsets, genomic information, training datasets and materials.
“We started the process a few years ago and this grant is the culmination of those planning activities,” Byrne said. “The whole concept started with roses and expanded to include polyploid crops in general and changing the fact that all the technology out there is aimed at diploids. There has been some work on polyploids – alfalfa, potatoes and roses – but nobody was really working together. We want to bring in groups working on specific tetraploid crops and talk about developing software to work on a wide range of polyploids.”
Why polyploid genomic tools could be a game-changer
Byrne said producing a reliable genomic tools to identify traits in polyploid crops would be a game-changer for breeders because it would significantly reduce the time it takes to produce hybridized varieties with targeted characteristics like disease resistance or heat and drought tolerance.
Riera-Lizarazu said tools designed for polyploids would allow huge steps for research analysis that can be done easily in diploids today.
“When we look at the rate of gain from selection, there has been a slowdown in polyploid crops actively bred,” Riera-Lizarazu said. “The rate of genetic gain is much lower than diploids, so by improving the tools we use for polyploids we should be able to increase genetic gains and improve the breeding of these crops.”
The traditional process begins by crossing parent plants – typically two or more varieties that express specific desired traits such as color and disease resistance. It takes one year to produce seed of that hybrid and another year to grow plants big enough to transplant in research trial plots.
It takes another two years to select the best plants that express the desired adaptation traits from those field trials at one location, Byrne said. Evaluation cycles typically last four to five years, and in the case of roses, two evaluation cycles are needed to find the best of the best plants that express the desired traits from a few thousand plants in trial plots.
“It takes at least 10 years from the initial cross-hybridization to evaluation,” he said. “So, you’re looking at 16 years to produce a new variety. And that is assuming we can get where we want in one generation. If I am putting several genes together for disease resistance from three to four different parent plants, it could take three to four generations of interbreeding to do that. The tool would accelerate those computational and molecular processes.”
A reliable polyploid computational and molecular tool could essentially cut that time in half, Byrne said.
“When you look at plants in the field you don’t always know what genes they have. You know what traits they have because you see them, but you really don’t know why,” Byrne said. “This will give us the ability to know why, and therefore we can make better decisions with respect to collecting the best seedlings or parents to make future generations.”
Putting the tools in the hands of breeders
Byrne said tools are available and are being rolled out to train breeders and then receive feedback to improve the usability. Some of the funding is directed at training the national and international pool of breeders.
“The training will be extensive,” he said. “But breeder input will ultimately take it from the programming and computational language to a format that is user-friendly for them.”
In the planning meeting held several years ago, the team determined what is lacking and identified gaps in the existing software and genomic tools used in diploid and polyploid research.
Now that the project secured funding, computational experts Jeff Endelman, Ph.D., University of Wisconsin; Marcelo Mollinari, Ph.D., and Zhao-Bang Zeng, Ph.D., North Carolina State University; Chris Maliepaard, Ph.D., and Roeland Voorrips, Ph.D., the University of Wageningen, Netherlands; and Susan Thomson, Ph.D., Plant and Food Research in New Zealand, will configure software to close those gaps and begin the validation process with the project breeders.
Breeding and plant genetics collaborators include Isabel Vales, Ph.D., and Patricia Klein, Ph.D., Texas A&M; Laura Shannon, Ph.D., University of Minnesota; Walter de Jong, Ph.D., Cornell University; Greg Porter, Ph.D., and Han Tan, Ph.D., University of Maine; and Sagar Sathuvalli, Oregon State University – all for potatoes; Margaret Worthington, Ph.D., University of Arkansas – blackberries; David Huff, Ph.D., Penn State – turfgrass; Susan Thomson, Ph.D., Plant and Food Research in New Zealand – kiwifruit; and Craig Yencho, Ph.D., North Carolina State University – sweet potatoes.
“We want to validate it by breeders in our training pool using it,” Byrne said. “We can input those datasets and figure out any changes that need to be made on the computational side and work through the process of proving the technology works.”
Limiting training to virtual classes due to COVID-19 will complicate the process, Byrne said. But the feedback via questionnaires and subsequent troubleshooting is necessary to make sure the software works.
“Genotyping technology has been improving rapidly,” he said. “It’s applicable to many crops, but one big improvement would be polyploid crops. If we can be remotely successful, then the returns could be incredible.”
The end product will be available on a community resource website managed by Washington State University, including software and manuals, training datasets and tutorials.
Facing challenges to meet global challenges
Byrne said the computation side of the tools’ development faced more obstacles because polyploids have twice as many chromosomes and genes as diploid plants to calculate probabilities.
Riera-Lizarazu said the challenge will be to finetune a tool and make it user-friendly. But incorporating collaborators with different perspectives on the analytical potential of a tool will maximize the benefits the software ultimately represents for users.
Ongoing computational work on polyploidy is being done in Wisconsin, North Carolina, the Netherlands and New Zealand, said Riera-Lizarazu. The AgriLife Research team, working in collaboration with teams from the University of Arkansas and Penn State, is working with datasets from rose, blackberry and turfgrass research trials and will be interacting with the other researchers and breeders working on potato, sweet potato and kiwi fruit to fine-tune the tools.
Riera-Lizarazu and Byrne hope a meeting in January will draw at least 75 of the 200 national and 100 international collaborators who have signed up to give input on the project.
Next year, developers will work to standardize phenotypic data and DNA sequencing and how the information is coded to make it more user-friendly, Byrne said.
Training of breeders and graduate and undergraduate students and colleagues will continue as the validation team and feedback to developers continue each year, Byrne said.
Byrne and Riera-Lizarazu said the receptiveness for the project is indicative of its importance to polyploid breeding programs around the globe. There is a high level of confidence that these tools will enable the development of polyploid plants with high quality, high yields and climate resiliency to meet the demands and challenges of climate change. They said its application in breeding programs will have far-reaching impacts on polyploid crops and their role in global economics, food security and sustainability.
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