Making sense of a universe of maize genetics

Seed banks around the world store and preserve the genetic diversity of millions of crop varieties. This massive collection of genetic material grants crop breeders access to a wide range of genetics with which to grow crops that yield better or resist stress and disease.

But, with a world of maize genetics at their disposal, how do growers know which varieties are worth studying and which are not? For most of history, this has required cultivating the varieties and studying their performance in the real world. But innovative data analysis and genomics could help plant growers predict the performance of new varieties without having to commit to growing them.

Jianming Yu, professor of agronomy at Iowa State University and Pioneer Distinguished Chair in Maize Breeding, has devoted much of his research to the “turbo charge” of the seemingly infinite amount of genetic stocks contained in the world’s seed banks. Yu and his colleagues published an article in Journal of Plant Biotechnology, a scientific publication detailing their latest efforts to predict maize traits based on genomics and data analysis.

Plant growers looking for strains to test may feel lost in a sea of ​​genomic material. Yu said applying advanced data analytics to all of those genomes can help breeders narrow down the number of varieties they’re interested in much more quickly and efficiently.

“We are always looking for the best genetic combinations and research the various combinations to see which strains we want to test,” said Xiaoqing Yu (no relationship), a former postdoctoral research fellow in Yu’s lab and the first author of the study. “Having these predictions can guide our research process.”

The study focused on predicting eight corn traits based on the apical shoot meristem (SAM), a niche of microscopic stem cells that generates all of the plant’s aboveground organs. The researchers used their analytical approach to predict traits in 2,687 different varieties of inbred maize based on a model developed by studying 369 inbred varieties that had been grown and had their apical sprout meristems pictured and measured under the microscope.

The researchers then validated their predictions with data obtained from 488 blood relatives to determine that the accuracy of their prediction ranged from 37% to 57% across the eight traits studied.

“We wanted to link research into the fundamental biological mechanisms of cell growth and differentiation with the agronomic improvement of corn,” said Mike Scanlon, professor of developmental biology at Cornell University and lead researcher on the multi-institutional team behind the study. “The morphometric measurements of SAM in maize seedlings allow for rapid completion of the study cycle. Not only does it enable such a connection, but also extends the practice of genomic prediction into the microfenotypic space.”

Jianming Yu said plant growers can increase the accuracy of those genomic predictions by increasing the number of plants per inbreeding for measurement and prediction algorithms enhanced by the results. More importantly, plant growers can refine their selection process for which inbreeds to study closely by leveraging “U values,” a statistical concept that takes into account the reliability of estimates. Yu said the study shows that implementing a selection process that takes prediction and statistical reliability into account can help growers focus more quickly on desirable crop genetics.

For example, analytical models might predict that a particular blood relative has modest potential for a given trait, but the U-value, or the upper bound for reliability, could indicate a high degree of unreliability in those predictions. So plant growers may choose to test inbreeds that don’t perform as well in the predictive model simply because of their genetic uniqueness, being less related to those used in building the prediction models.

“We found that there can be a balance between selection to optimize short-term gain and mining diversity,” said Yu. “It’s a difficult balance for plant growers. These considerations sometimes go in different directions. Genetic improvement can be seen as a space exploration, either of the huge amount of genetic material existing in seed banks or the countless reproductive progeny. constantly generated. We want to develop better tools to guide those decisions in the process. ”