Genes predict best bets among millions of seeds

(Credit: CIAT/Flickr)

There are vast numbers of seeds in gene banks around the world. A new approach uses their genomes to predict the best options to grow for specific traits.

“We think it’s possible to use these predictions to guide our breeding and selection decisions,” says first author Xiaoqing Yu, a postdoctoral agronomy research associate at Iowa State University. “We hope it will facilitate better and more precise breeding with the diverse genetic materials.”

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The researchers tested a complex set of genetic tools to predict which traits hundreds of sorghum seeds would possess if cultivated. The team then grew specimens for some of those sorghum accessions— plant material collected from various sites—to gauge the accuracy of their genome-based predictions. Their yield predictions proved accurate over 70 percent of the time.

In theory, plant breeders can access a virtual ocean of data on germplasm, or the genetic material of plants, from all over the world. There are 1,750 gene banks in the world containing 7.4 million plant accessions, but only a small percentage of those possess the specific qualities that plant-breeders prize in producing new cultivars for production needs.

But finding the best accessions among the millions available poses a logistical nightmare for plant scientists, says Jianming Yu, an associate professor of agronomy.

The study shows it’s possible to an extent to predict the traits those accessions possess based on their genetic profile. Yu says the paper takes a step toward “super charging the engine” of a valuable resource allowing sorghum breeders to zero in on valuable accessions with greater ease and speed than is currently possible.

“We all agree on the urgency and challenges to effectively mine the natural heritage stored in gene banks,” he says. “But we need to test different strategies and we need to figure out the way.”

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The researchers selected a set of 962 sorghum accessions from a US Department of Agriculture database and conducted sequencing to obtain the genome-wide fingerprinting data. They field tested a selected training sample and used an assortment of prediction tools to assess various traits. The researchers then cultivated 200 of those accessions to check how their predictions matched reality.

Yield predictions had an accuracy of 76 percent, and predictions for other traits, such as plant height, ranged from 67 to 83 percent.

“By leveraging genomics and data analytics, we certainly can do a better job,” Jianming Yu says.

The new research, published in Nature Plants, focuses on sorghum used for bioenergy but could have ramifications for a range of crops, according to Jianming Yu.

Funding for the research came from the USDA National Institute of Food and Agriculture, the National Science Foundation, Kansas State University Center for Sorghum Improvement, the ISU Raymond F. Baker Center for Plant Breeding, and the ISU Plant Science Institute.

Source: Iowa State University