蒙彼利埃大学Franois Tardieu研究小组取得一项新突破。他们的最新研究提出了全欧洲环境条件下玉米产量的基因组预测。 这一研究成果发表在2019年出版的国际学术期刊《Nature Genetics》上。

传感器网络的发展为成千上万领域的环境表征开辟了新的途径。在基因与环境互作条件下，小组提出一个新的种质评价方法。产量分析包括粒重和粒数以及基因环境交互被建模为对环境驱动因素的基因型敏感性。环境的特征在于根据每个田地中的传感器数据计算的基因型特异性指数以及在表型平台上针对每种基因型校准的物候学的进展。采用全基因组回归方法进行基因型敏感性分析，可以准确预测基因型与环境交互条件下的产量,超过基准的方法。

开发适应气候变化的种质资源是保证粮食安全的重要手段。基因组预测是一个强大的工具来评估许多基因型，但由于基因型和环境的交互作用，基因组预测在环境差异较大时往往表现不佳，尽管基因组预测在开花时间预测上已经有了可喜的成果。

附：英文原文

Title: Genomic prediction of maize yield across European environmental conditions

Author: Emilie J. Millet, Willem Kruijer, Aude Coupel-Ledru, Santiago Alvarez Prado, Lloren Cabrera-Bosquet, Sbastien Lacube, Alain Charcosset, Claude Welcker, Fred van Eeuwijk, Franois Tardieu

Issue&Volume: 2019-05-20

Abstract: The development of germplasm adapted to changing climate is required to ensure food security. Genomic prediction is a powerful tool to evaluate many genotypes but performs poorly in contrasting environmental scenarios37 (genotypeenvironment interaction), in spite of promising results for flowering time. New avenues are opened by the development of sensor networks for environmental characterization in thousands of fields. We present a new strategy for germplasm evaluation under genotypeenvironment interaction. Yield was dissected in grain weight and number and genotypeenvironment interaction in these components was modeled as genotypic sensitivity to environmental drivers. Environments were characterized using genotype-specific indices computed from sensor data in each field and the progression of phenology calibrated for each genotype on a phenotyping platform. A whole-genome regression approach for the genotypic sensitivities led to accurate prediction of yield under genotypeenvironment interaction in a wide range of environmental scenarios, outperforming a benchmark approach.

DOI: 10.1038/s41588-019-0414-y

Source:https://www.nature.com/articles/s41588-019-0414-y