URBANA, Ill. — University of Illinois Urbana-Champaign corn breeders know profitability is about more than yield. By tweaking kernel composition, they can tailor corn for lucrative biotech applications , industrial products, overseas markets, and more. But to efficiently unlock these valuable traits, breeders must first understand their genetic underpinnings.
Traditional corn breeding usually takes years and requires acres of replicated trials, not to mention federal funding to support the research. But tapping into public genebanks and shared data, along with modern computational tools, can dramatically speed up the process.
Corn breeder Martin Bohn, professor in the Department of Crop Sciences in the College of Agricultural, Consumer and Environmental Sciences at Illinois, recently led a project exploring kernel composition in nearly 1,000 diverse maize inbred lines from the USDA-ARS North Central Regional Plant Introduction Station in Ames, Iowa. The collection is part of the nation's system of seed banks — including two major collections housed at Illinois — representing many thousands of high-quality crop genotypes that are freely accessible to researchers.
Using near-infrared spectroscopy and publicly available genomic data, the team, which included undergraduate researcher Stephen Gray, identified genetic regions influencing both the average values and the variability of key kernel composition traits.
"Seed banks contain an incredible amount of genetic diversity, but they are often underused for quantitative genetics and breeding," Bohn said. "Our results show that these resources can be used effectively to generate meaningful genetic insights, even before launching large, multi-year field experiments."
Because seed bank accessions are typically available only in small quantities, often as packets of 100 seeds from a single genotype, the study relied on unreplicated seed samples, a situation traditionally viewed as a major limitation in scientific studies. To address this challenge, the researchers validated their findings by comparing their results with large, replicated field studies conducted by other research groups. Strong agreement between studies confirmed that the unreplicated data captured real genetic signals.
"We compared our estimates with a huge replicated field experiment by colleagues in Minnesota that overlapped with 200-300 of the lines we used from the NCRPIS collection," Bohn said. "We found that the correlation between their kernel data and ours was actually pretty high, so it gave us confidence that our data is actually meaningful and can be trusted."
The team applied genome-wide association studies, variance-based genetic analyses, and genomic prediction models to identify both well-known and previously unreported genomic regions associated with kernel composition traits.
"Many of the signals we found were in regions where genes had already been identified for the traits that we were interested in — protein, starch, oil, and some others — so it confirmed that our analysis was on the right track," Bohn said. "But we also found new ones. This is cool because these are new candidate genes we can explore further."
The fact that the study uncovered new breeding targets is just one reason Bohn and his colleagues are excited about the research.
Doctoral student Christopher Mujjabi, a co-author on the study, said the work highlights a shift in how breeding research can begin. "Instead of starting with years of replicated field trials, researchers can first explore what's already stored in gene banks," he said. "That helps prioritize the most promising material and makes breeding programs more efficient."
The findings demonstrate how public germplasm collections, high-throughput phenotyping, and shared data can be combined to accelerate crop improvement, particularly for traits tied to nutrition, processing quality, and emerging specialty markets.
Bohn added, "We have developed a pipeline that allows researchers to utilize the genetic treasures that are located in our gene banks. You don't always have to do an elaborate experiment as a first step. You can get an idea of what is in that collection, dive into the really interesting materials, and then utilize these for further studies."
The study, "Mean and variance heterogeneity loci impact kernel compositional traits in maize," is published in The Plant Genome [DOI: 10.1002/tpg2.70131 ].
Research in the College of ACES is made possible in part by Hatch funding from USDA's National Institute of Food and Agriculture. This study was also supported by two competitive NIFA grants [2020-51300-32180 and 2017-51300-27115].
