Establishment of genomic prediction models combined with weather and soil water information in sugar beet

结合天气和土壤水分信息的甜菜基因组预测模型的建立

• 批准号: 529673439
• 负责人: Professor Dr. Hans-Peter Piepho
• 金额: --
• 依托单位: Institut für Kulturpflanzenwissenschaften (340)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/529673439?language=en
• 关键词: Establishment; genomic; prediction; models; combined

Finlay-Wilkinson regression is a popular method for analysing genotype-environment interaction in series of plant breeding and variety trials. The method involves a regression on the environmental mean, computed as the average of all genotype means. The environmental mean indexes the productivity of an environment, which is driven by a wide array of environmental factors. Increasingly, it is becoming feasible to characterize environments explicitly using observable environmental covariates. Hence, there is mounting interest to replace the environmental index with an explicit regression on such observable environmental covariates. This project develops a framework for such methods and implements this for genomic prediction in a commercial sugar beet breeding programme. The focus is on parsimonious models that allow replacing the environmental index by regression on synthetic environmental covariates formed as linear combinations of a larger number of observable environmental covariates. Different method will be employed to derive such synthetic covariates. A roster of environmental covariates for weather conditions and soil water supply will be assessed at ten locations using sensors and used to compute these synthetic covariates, which will be used for genomic prediction of hybrid performance in a sugar beet breeding programme. We will also develop an approach that allows using phenotypic data on the hybrid’s parents to potentially enhance the predictive accuracy for the hybrids.

Finlay-Wilkinson回归是植物育种和品种试验中分析基因型与环境互作的常用方法。该方法涉及对环境平均值的回归，计算为所有基因型平均值的平均值。环境平均值是环境生产力的指数，它由一系列广泛的环境因素驱动。越来越多的是，它变得可行的环境明确使用可观察的环境协变量。因此，人们越来越有兴趣用对这些可观察到的环境协变量的明确回归来取代环境指数。该项目为这些方法开发了一个框架，并在商业甜菜育种计划中实现了基因组预测。重点是简约的模型，允许取代环境指数的回归合成环境协变量形成的线性组合的大量可观察到的环境协变量。将采用不同的方法推导此类合成协变量。气候条件和土壤水供应的环境协变量名册将在10个地点使用传感器进行评估，并用于计算这些合成协变量，这将用于在甜菜育种计划的杂交性能的基因组预测。我们还将开发一种方法，允许使用杂交种亲本的表型数据，以潜在地提高杂交种的预测准确性。