Data from: Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding

Genomics and phenomics have promised to revolutionize the field of plant breeding. The integration of these two fields has just begun and is being driven through big data by advances in next-generation sequencing and developments of field-based high-throughput phenotyping (HTP) platforms. Each year the International Maize and Wheat Improvement Center (CIMMYT) evaluates tens-of-thousands of advanced lines for grain yield across multiple environments. To evaluate how CIMMYT may utilize dynamic HTP data for genomic selection (GS), we evaluated 1170 of these advanced lines in two environments, drought (2014, 2015) and heat (2015). A portable phenotyping system called ‘Phenocart’ was used to measure normalized difference vegetation index and canopy temperature simultaneously while tagging each data point with precise GPS coordinates. For genomic profiling, genotyping-by-sequencing (GBS) was used for marker discovery and genotyping. Several GS models were evaluated utilizing the 2254 GBS markers along with over 1.1 million phenotypic observations. The physiological measurements collected by HTP, whether used as a response in multivariate models or as a covariate in univariate models, resulted in a range of 33% below to 7% above the standard univariate model. Continued advances in yield prediction models as well as increasing data generating capabilities for both genomic and phenomic data will make these selection strategies tractable for plant breeders to implement increasing the rate of genetic gain.

基因组学与表型组学有望为植物育种领域带来革命性变革。二者的整合尚处于起步阶段，其发展由大数据驱动，依托于下一代测序技术的进步以及田间高通量表型（high-throughput phenotyping, HTP）平台的研发。国际玉米小麦改良中心（International Maize and Wheat Improvement Center, CIMMYT）每年都会在多个环境下对数万份育种高级品系开展籽粒产量性状评估。为探究CIMMYT如何利用动态高通量表型数据开展基因组选择（genomic selection, GS），本研究在两种胁迫环境下对其中1170份高级品系进行了评估：干旱胁迫（2014、2015年）与热胁迫（2015年）。研究采用一款名为‘Phenocart’的便携式表型系统，可同步测量归一化差分植被指数（normalized difference vegetation index）与冠层温度，并为每一条数据标记精确的GPS坐标。为开展基因组分型，本研究通过测序分型（genotyping-by-sequencing, GBS）技术进行标记开发与基因分型。本研究共评估了多款基因组选择模型，所用数据包含2254个测序分型标记与超过110万条表型观测值。高通量表型技术采集的生理性状数据，无论是作为多元模型的响应变量，还是作为单变量模型的协变量，其预测精度相较于标准单变量模型，波动范围为低33%至高7%。产量预测模型的持续优化，以及基因组与表型组数据生成能力的不断提升，将使这些选择策略更易于植物育种者落地应用，从而加快遗传增益速率。