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

在植物育种和品种试验中，Finlay-Wilkinson回归是分析基因-环境互作的一种常用方法。该方法涉及对环境平均值的回归，该平均值计算为所有基因平均值的平均值。环境平均值是由多种环境因素驱动的环境生产力的指数。越来越多地，使用可观察到的环境协变量来明确地表征环境正变得可行。因此，人们越来越有兴趣用这种可观察到的环境协变量的显式回归来取代环境指数。该项目为这些方法开发了一个框架，并将其用于商业甜菜育种计划中的基因组预测。重点放在简约的模型上，这些模型允许用对合成环境协变量的回归来取代环境指数，这些合成环境协变量形成为大量可观察到的环境协变量的线性组合。将使用不同的方法来推导这些合成协变量。将在十个地点使用传感器评估天气条件和土壤水分供应的环境协变量名册，并用于计算这些合成协变量，这些合成协变量将用于甜菜育种计划中杂交表现的基因组预测。我们还将开发一种方法，允许使用杂交亲本的表型数据来潜在地提高杂交后代的预测准确性。

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.