By Lauren Quinn
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.
Exploring genetic diversity in seed banks
Corn breeder Martin Bohn, professor in the Department of Crop Sciences in the College of Agricultural, Consumer and Environmental Sciences at Illinois, 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 study, "Mean and variance heterogeneity loci impact kernel compositional traits in maize," is published in The Plant Genome.
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.
Innovative methods for genetic analysis
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.
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