Genetic control of kernel compositional variation in a maize diversity panel. Issue 3 (1st July 2021)

• Record Type: Journal Article
• Authors: Renk, Jonathan S.; Gilbert, Amanda M.; Hattery, Travis J.; O'Connor, Christine H.; Monnahan, Patrick J.; Anderson, Nickolas; Waters, Amanda J.; Eickholt, David P.; Flint‐Garcia, Sherry A.; Yandeau‐Nelson, Marna D.; Hirsch, Candice N.
• Is Part Of: œplant genome. Volume 14:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-01
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.1002/tpg2.20115
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract: Maize ( Zea mays L.) is a multi‐purpose row crop grown worldwide, which, over time, has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants; however, much of the underlying genetic control of these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g., carbohydrates, protein, and starch) based on the output of near‐infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade‐offs among traits in different maize types (e.g., dent, sweetcorn, and popcorn) were explored, and significant differences and meaningful correlations were detected. In total, 22.9–71.0% of the phenotypic variation across these traits could be explained using 2, 386, 666 single nucleotide polymorphism (SNP) markers generated from whole‐genome resequencing data. A genome‐wide association study (GWAS) was conducted using these same markers and found 72 statistically significant SNPs for 11 compositional traits. This study provides valuable insights in the phenotypic variation and genetic control underlying compositional traits that can be used in breeding programs for improving maize grain quality. Core Ideas: Understanding kernel compositional variation is critical for food‐grade corn applications. Genetic and environmental factors account for most of the variation in compositional traits. A broad range in trait heritabilities was observed across compositional traits. Compositional trade‐offs are important to consider when conducting multiple‐trait breeding. Compositional traits are mostly controlled by a large number of small effect loci.