SGER: Isolating Quantitative Kernel Traits With Near Infrared Spectroscopy

这项用于探索性研究的小额赠款将为使用单粒近红外反射(NIR)和透射率(NIT)光谱对玉米种子成分突变进行高通量鉴定和遗传分析提供概念验证资金。这是一个高风险的项目，因为种子成分是高度可变的，而且种子成分的遗传基础知之甚少。这部分是因为影响种子发育的生物合成途径和生理过程是复杂的。此外，这些表型对环境影响高度敏感，因此很难研究。这种方法具有潜在的高回报，这既是因为对控制种子组成的过程有很高的科学兴趣，也是因为这些性状具有非常高的商业价值。近红外光谱将对近亲交配的统一Mu玉米突变群体进行筛选，以了解其脂肪、蛋白质、碳水化合物和热量含量的变化。将对突变者进行遗传性测试。为了衡量基因对环境的相对影响，所有可能的突变体都将从两个田间季节生长的品系中取样。将开发新的统计方法来分析来自近红外分析的复杂数据集。该项目采用跨学科的方法，结合新的工程学、植物遗传学和统计方法来分离对农业重要的单基因座数量性状。因为这些性状很可能是突变转座子标记的，所以有可能快速识别影响玉米种子数量组成性状的基因。

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.