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Leveraging the genomics revolution with high-throughput phenotyping for crop improvement of abiotic stresses.

机译:利用高通量表型的基因组学革命,改善非生物胁迫的作物。

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摘要

A major challenge for 21st century plant geneticists is to predict plant performance based on genetic information. This is a daunting challenge, especially when there are thousands of genes that control complex traits as well as the extreme variation that results from the environment where plants are grown. Rapid advances in technology are assisting in overcoming the obstacle of connecting the genotype to phenotype. Next generation sequencing has provided a wealth of genomic information resulting in numerous completely sequenced genomes and the ability to quickly genotype thousands of individuals.;The ability to pair the dense genotypic data with phenotypic data, the observed plant performance, will culminate in successfully predicting cultivar performance. While genomics has advanced rapidly, phenomics, the science and ability to measure plant phenotypes, has slowly progressed, resulting in an imbalance of genotypic to phenotypic data. The disproportion of high-throughput phenotyping (HTP) data is a bottleneck to many genetic and association mapping studies as well as genomic selection (GS).;To alleviate the phenomics bottleneck, an affordable and portable phenotyping platform, Phenocart, was developed and evaluated. The Phenocart was capable of taking multiple types of georeferenced measurements including normalized difference vegetation index and canopy temperature, throughout the growing season. The Phenocart performed as well as existing manual measurements while increasing the amount of data exponentially. The deluge of phenotypic data offered opportunities to evaluate lines at specific time points, as well as combining data throughout the season to assess for genotypic differences. Finally in an effort to predict crop performance, the phenotypic data was used in GS models. The models combined molecular marker data from genotyping-by-sequencing with high-throughput phenotyping for plant phenotypic characterization. Utilizing HTP data, rather than just the often measured yield, increased the accuracy of GS models.;Achieving the goal of connecting genotype to phenotype has direct impact on plant breeding by allowing selection of higher yielding crops as well as selecting crops that are adapted to local environments. This will allow for a faster rate of improvement in crops, which is imperative to meet the growing global population demand for plant products.
机译:21世纪植物遗传学家的主要挑战是根据遗传信息预测植物的生长性能。这是一个艰巨的挑战,尤其是当成千上万的基因控制复杂性状以及植物生长环境导致的极端变异时。技术的飞速发展正在帮助克服将基因型连接到表型的障碍。下一代测序已提供了丰富的基因组信息,从而导致了许多完全测序的基因组以及快速对成千上万的个体进行基因分型的能力;将密集的基因型数据与表型数据配对(观察到的植物性能)的能力将最终成功预测品种性能。尽管基因组学发展迅速,但表型学,测量植物表型的科学和能力却在缓慢发展,导致基因型与表型数据之间的失衡。高通量表型(HTP)数据的不均衡是许多遗传和关联作图研究以及基因组选择(GS)的瓶颈;为了缓解表型瓶颈,开发并评估了一种价格合理且可移植的表型平台Phenocart。 。在整个生长季节,Phenocart能够进行多种类型的地理参考测量,包括归一化植被指数和冠层温度。 Phenocart执行和现有的手动测量一样,同时以指数方式增加了数据量。大量的表型数据提供了在特定时间点评估品系的机会,并提供了整个季节的数据组合以评估基因型差异。最后,为了预测农作物的生长性能,在GS模型中使用了表型数据。该模型将来自基因分型的分子标记数据与高通量表型相结合,用于植物表型表征。利用HTP数据而不仅仅是经常测量的产量,可以提高GS模型的准确性。;实现将基因型与表型联系起来的目标通过允许选择高产作物以及选择适合作物的作物,直接影响了植物育种。当地环境。这将使作物的改良速度更快,这对于满足全球人口对植物产品日益增长的需求势在必行。

著录项

  • 作者

    Crain, Jared Levi.;

  • 作者单位

    Kansas State University.;

  • 授予单位 Kansas State University.;
  • 学科 Plant sciences.;Agriculture.;Genetics.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 144 p.
  • 总页数 144
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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