| 1 | Spectral reflectance indices as proxies for yield potential and heat stress tolerance in spring wheat: heritability estimates and marker-trait associations显示文摘The application of spectral reflectance indices(SRIs) as proxies to screen for yield potential(YP) and heat stress(HS) is emerging in crop breeding programs.Thus, a comparison of SRIs and their associations with grain yield(GY) under YP and HS conditions is important.In this study, we assessed the usefulness of 27 SRIs for indirect selection for agronomic traits by evaluating an elite spring wheat association mapping initiative(WAMI) population comprising 287 elite lines under YP and HS conditions.Genetic and phenotypic analysis identified 11 and 9 SRIs in different developmental stages as efficient indirect selection indices for yield in YP and HS conditions,respectively.We identified enhanced vegetation index(EVI) as the common SRI associated with GY under YP at booting, heading and late heading stages, whereas photochemical reflectance index(PRI) and normalized difference vegetation index(NDVI) were the common SRIs under booting and heading stages in HS.Genomewide association study(GWAS) using 18704 single nucleotide polymorphisms(SNPs) from Illumina i Select90 K identified 280 and 43 marker-trait associations for efficient SRIs at different developmental stages under YP and HS, respectively.Common genomic regions for multiple SRIs were identified in 14 regions in 9 chromosomes: 1 B(60–62 cM), 3 A(15, 85–90, 101–105 cM), 3 B(132–134 cM), 4 A(47–51 cM), 4 B(71–75 cM), 5 A(43–49, 56–60, 89–93 cM), 5 B(124–125 cM),6 A(80–85 cM), and 6 B(57–59, 71 cM).Among them,SNPs in chromosome 5 A(89–93 cM) and 6 A(80–85 cM)were co-located for yield and yield related traits.Overall,this study highlights the utility of SRIs as proxies for GY under YP and HS.High heritability estimates and identification of marker-trait associations indicate that SRIs are useful tools for understanding the genetic basis of agronomic and physiological traits. | Caiyun LIU Francisco PINTO CMariano COSSANI Sivakumar SUKUMARAN Matthew PREYNOLDS | 2019 | Frontiers of Agricultural Science and Engineering2019，6，3： | 0 |
| 2 | An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops显示文摘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. | Xianran Li Tingting Guo Jinyu Wang Wubishet ABekele Sivakumar Sukumaran Adam EVanous James PMcNellie Laura Tibbs Cortes Marta SLopes Kendall RLamkey Mark EWestgate John KMcKay Sotirios VArchontoulis Matthew PReynolds Nicholas ATinker Patrick SSchnable Jianming Yu | 2021 | Molecular Plant2021，14，6： | 0 |
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