SGER: Isolating Quantitative Kernel Traits With Near Infrared Spectroscopy

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This Small Grant for Exploratory Research will provide proof of concept funding for the use of single-kernel near infrared reflectance (NIR) and transmittance (NIT) spectroscopy for high-throughput identification and genetic analysis of seed composition mutants of corn. This is a high-risk project because seed composition is highly variable, and the genetic basis of seed composition is poorly understood. This is in part because the biosynthetic pathways and physiological process that influence seed development are complex. In addition, these phenotypes are highly sensitive to environmental influence and therefore difficult to study. The approach is potentially of high payoff both because of high scientific interest in the processes that control seed composition, and because these traits have very high commercial value.Inbred Uniform Mu corn mutant populations will be screened for changes in oil, protein, carbohydrate and caloric content using NIR. Mutants will be tested for heritability. To gauge the relative effects of genotype versus environment, all putative mutants will be sampled from lines grown in two field seasons. Novel statistical methods will be developed for analyzing the complex data sets derived from NIR analysis.This project takes an interdisciplinary approach that incorporates novel engineering, plantgenetics, and statistical methods to isolate single-locus quantitative traits of agricultural importance. Because these traits are likely to be Mutator transposon-tagged, there is the potential to rapidly identify genes that affect quantitative compositional traits in corn seeds.