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

• 作者: Xianran Li Tingting Guo Jinyu Wang Wubishet A.Bekele Sivakumar Sukumaran Adam E.Vanous James P.McNellie Laura Tibbs Cortes Marta S.Lopes Kendall R.Lamkey Mark E.Westgate John K.McKay Sotirios V.Archontoulis Matthew P.Reynolds Nicholas A.Tinker Patrick S.Schnable Jianming Yu
• 来源期刊: 分子植物（英文版）
• 年，卷（期）: 2021,（6）
• 所属期刊栏目: Resource Article
• 页码范围: 874-887
• 页数: 14页
• 语种: 英文

摘要

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 com-plex 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 com-bination 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.Interest-ingly,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 per-formance prediction in breeding for future climates,and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.