Using UAV-based temporal spectral indices to dissect changes in the stay green trait in wheat

Abstract Stay green (SG) in wheat, a beneficial trait for increasing yield and stress resistance, needs to be supported by analysis of the underlying genetic basis. Spectral reflectance indices (SIs) provide non-destructive tools to evaluate crop temporal senescence. However, few SI-based SG quantification pipelines for analyzing diverse wheat panels in the field are available. Here, we first applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages during two field seasons. Based on over 12,000 SIs data set, four SIs (NDVI, GNDVI, NDRE and OSAVI) were selected to develop relative stay green scores (RSGS) and the senescence of wheat populations occurs mainly at four developmental stages stage 1 (S1) to S4, accounting for the final SG indicators. A RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 SNPs significantly associated with RSGS and 1,085 corresponding candidate genes in the two seasons; 15 QTL overlapped or were adjacent to known SG-related QTL or genes and the remaining QTL were novel. Finally, we selected three superior candidate genes ( TraesCS6B03G0356400 , TraesCS2B03G1299500 , and TraesCS2A03G1081100 ) as examples by transcriptomes, gene annotation, and gene-based association analysis for further analysis and found that utilization of superior SG-related variation in China gradually increased following the Green Revolution. The study provides a useful reference for further SG-related gene discovery of favorable variations in diverse wheat panels.