Targeted engineering of quantitative traits in plants.

我们通过传统分子育种开发新型有益作物性状的能力在过去十年中得到了显著提高，然而，保持这种轨迹的能力受到等位基因多样性的限制。虽然编码序列的遗传变异已被大量用于作物改良，但大多数数量性状存在于非编码调控区的突变中。我的项目旨在对谷物基因组和表观基因组数据进行计算分析，以确定支持理想农艺性状的调控区域。一旦被鉴定，使用多重方法的CRISPR诱变将允许修饰多个靶位点，从而在基因表达中工程化更大的靶向变异，从而产生新的表型。将选择表现出增强的表型变异的理想的农业性状-主要是产量-将调查定义的调控网络的时空调控和性状表现之间的关系。

Our ability to develop novel beneficial crop traits through traditional molecular breeding has significantly improved over the last decade, however, the ability to maintain this trajectory is limited by allelic diversity. While genetic variation at coding sequences has been heavily exploited for crop improvement, most quantitative traits reside in mutations at non-coding regulatory regions. My project aims to perform a computational analysis of cereal genomic and epigenomic data to identify the regulatory regions that underpin desirable agronomic traits. Once identified, CRISPR mutagenesis using a multiplexed approach will allow the modification of multiple target sites to thus engineer greater targeted variation in gene expression resulting in novel phenotypes. Lines displaying enhanced phenotypic variation for desirable agricultural traits -primarily yield- will be selected to investigate the relationship between the temporospatial regulation of defined regulatory networks and trait performance.