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一种综合框架,为作物的 GWAS 和基因组选择重新引入环境维度。

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops.

机构信息

Department of Agronomy, Iowa State University, Ames, IA 50011, USA.

Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada.

出版信息

Mol Plant. 2021 Jun 7;14(6):874-887. doi: 10.1016/j.molp.2021.03.010. Epub 2021 Mar 10.

DOI:10.1016/j.molp.2021.03.010
PMID:33713844
Abstract

Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

摘要

鉴定基因-环境相互作用和表型可塑性所涉及的机制和途径是一个长期存在的挑战。建立一个具有环境维度的综合框架,用于复杂性状的剖析和预测,是非常可取的。关键步骤是确定一个既有生物学意义又可用于新环境估计的环境指数。通过对三种主要作物(玉米、小麦和燕麦)的广泛实地观测的复杂性状、环境特征和全基因组单核苷酸多态性进行研究,我们证明了鉴定这样的环境指数(即环境参数和生长窗口的组合)可以使具有明确环境维度的全基因组关联研究和复杂性状的基因组选择成为可能。有趣的是,从沿环境指数的开花时间值得出的两个反应规范参数(即截距和斜率)的基因的鉴定结果,在一个多样化的玉米群体中比在小麦和燕麦育种群体中较少共定位,这与群体的不同多样性水平和遗传构成一致。此外,我们展示了这个框架用于系统预测不同种质群体在新环境中的表现的有用性。这个通用框架和配套的 CERIS-JGRA 分析软件包应该有助于对复杂性状进行有生物学依据的剖析,增强对未来气候下的育种表现的预测,并协调努力,丰富我们对表型变异机制的理解。

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