Quan Xie1, Debbie L Sparkes2. 1. College of Agriculture, Nanjing Agricultural University, Nanjing, 210 095, Jiangsu, China. quanxie@njau.edu.cn. 2. Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham Sutton Bonington Campus, Loughborough, LE12 5RD, Leicestershire, UK.
Abstract
MAIN CONCLUSION: Principal component and meta-QTL analyses identified genetic loci affecting the trade-off of wheat grain number and size, which could provide opportunities to optimize local breeding strategies for further yield improvement. Grain yield of wheat is complex, and its physiological and genetic bases remain largely unknown. Using the Forno/Oberkulmer recombinant inbred lines, this study validated the negative phenotypic relationships between thousand grain weight (TGW) and grain number components. This trade-off might be alleviated at the population level by early anthesis and at the shoot level by higher shoot biomass. Principal component (PC) analysis revealed three useful PCs, of which both PC1 and PC3 were positively associated with grain yield and grains m-2 through increased spikes m-2 (for PC1) or grains per spike (for PC3), while PC2 primarily reflected the trade-off of grain number and TGW. Quantitative trait locus (QTL) mapping detected eight and seven loci for PC1 and PC2, respectively, on chromosomes 1D, 2A, 3A, 3B, 4A, 4B, 5A and 7B, individually explaining 11.7‒29.3% of phenotypic variations. Using the 1203 QTLs published previously, a meta-analysis was performed to reveal 12, 21, 37 and 54 genomic regions (MQTLs) affecting grains m-2, spikes m-2, grains per spike and TGW, respectively. Moreover, 67 MQTLs (96%) for grain number were coincided with the TGW MQTLs, with reverse phenotypic effects, suggesting intensive genetic trade-off between grain number and size. The AGP2 gene, which encodes ADP-glucose pyrophosphorylase determining TGW, was found by haplotype analysis in the Forno/Oberkulmer population to affect grain number oppositely, indicating this trade-off at the gene level. Appropriate combinations of the QTLs/genes for local breeding targets, such as higher grain number or larger grains, therefore, would be critical to achieve future yield gains.
MAIN CONCLUSION: Principal component and meta-QTL analyses identified genetic loci affecting the trade-off of wheat grain number and size, which could provide opportunities to optimize local breeding strategies for further yield improvement. Grain yield of wheat is complex, and its physiological and genetic bases remain largely unknown. Using the Forno/Oberkulmer recombinant inbred lines, this study validated the negative phenotypic relationships between thousand grain weight (TGW) and grain number components. This trade-off might be alleviated at the population level by early anthesis and at the shoot level by higher shoot biomass. Principal component (PC) analysis revealed three useful PCs, of which both PC1 and PC3 were positively associated with grain yield and grains m-2 through increased spikes m-2 (for PC1) or grains per spike (for PC3), while PC2 primarily reflected the trade-off of grain number and TGW. Quantitative trait locus (QTL) mapping detected eight and seven loci for PC1 and PC2, respectively, on chromosomes 1D, 2A, 3A, 3B, 4A, 4B, 5A and 7B, individually explaining 11.7‒29.3% of phenotypic variations. Using the 1203 QTLs published previously, a meta-analysis was performed to reveal 12, 21, 37 and 54 genomic regions (MQTLs) affecting grains m-2, spikes m-2, grains per spike and TGW, respectively. Moreover, 67 MQTLs (96%) for grain number were coincided with the TGW MQTLs, with reverse phenotypic effects, suggesting intensive genetic trade-off between grain number and size. The AGP2 gene, which encodes ADP-glucose pyrophosphorylase determining TGW, was found by haplotype analysis in the Forno/Oberkulmer population to affect grain number oppositely, indicating this trade-off at the gene level. Appropriate combinations of the QTLs/genes for local breeding targets, such as higher grain number or larger grains, therefore, would be critical to achieve future yield gains.
Entities:
Keywords:
AGP2; Principal component analysis; QTL; Trade-off; Yield; Yield component
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