Literature DB >> 33146737

Allelic response of yield component traits to resource availability in spring wheat.

Brittney H Jones1, Nancy K Blake2, Hwa-Young Heo2, John M Martin2, Jessica A Torrion3, Luther E Talbert4.   

Abstract

KEY MESSAGE: Investigation of resource availability on allele effects for four yield component quantitative trait loci provides guidance for the improvement of grain yield in high and low yielding environments. A greater understanding of grain yield (GY) and yield component traits in spring wheat may increase selection efficiency for improved GY in high and low yielding environments. The objective of this study was to determine allelic response of four yield component quantitative trait loci (QTL) to variable resource levels which were manipulated by varying intraspecific plant competition and seeding density. The four QTL investigated in this study had been previously identified as impacting specific yield components. They included QTn.mst-6B for productive tiller number (PTN), WAPO-A1 for spikelet number per spike (SNS), and QGw.mst-3B and TaGW2-A1 for kernel weight (KWT). Near-isogenic lines for each of the four QTL were grown in multiple locations with three competition (border, no-border and space-planted) and two seeding densities (normal 216 seeds m-2 and low 76 seeds m-2). Allele response at QTn.mst-6B was driven by changes in resource availability, whereas allele response at WAPO-A1 and TaGW2-A1 was relatively unaffected by resource availability. The QTn.mst-6B.1 allele at QTn.mst-6B conferred PTN plasticity resulting in significant GY increases in high resource environments. The gw2-A1 allele at TaGW2-A1 significantly increased KWT, SNS and GPC offering a source of GY improvement without negatively impacting end-use quality. QGw.mst-3B allelic variation did not significantly impact KWT but did significantly impact SPS. Treatment effects in both experiments often resulted in significant positive impacts on GY and yield component traits when resource availability was increased. Results provide guidance for leveraging yield component QTL to improve GY performance in high- and low-yield environments.

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Year:  2020        PMID: 33146737     DOI: 10.1007/s00122-020-03717-7

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


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