Delaying or Delivering: Identification of novel NAM-1 alleles which delay senescence to extend grain fill duration of wheat

Chapman, Elizabeth A; Orford, Simon; Griffiths, Simon

doi:10.1101/2020.09.23.307785

Cited by 4 publications

(31 citation statements)

References 73 publications

(130 reference statements)

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“…In 2017 and 2018 we conducted time-course senescence scoring of leaf and peduncle tissue (Figure 3). We identified peduncle senescence is initiated after flag leaf senescence, with senescence profiles found to reinforce one another, aiding differentiation of lines (Figure 3B; Chapman et al . (2020)).…”

Section: Resultsmentioning

confidence: 61%

“…Previously, grain filling experiments were performed for Staygreen A, Staygreen B and Non-staygreen parental cv. Paragon, in which a significant grain fill extension was reported for both staygreen lines (Chapman et al ., 2020). To identify any potential association between grain filling and senescence phenotypes, grain maturity of RIL populations was scored according to the Zadoks scale with reference to the ‘Wheat Growth Guide’ (Zadoks et al ., 1974; AHDB, 2018).…”

Section: Methodsmentioning

confidence: 93%

“…Paragon EMS staygreen mutants and associated Recombinant Inbred Line (RIL) populations. ‘Staygreen A’ and ‘Staygreen B’ refers to mutant lines 1189a and 2316b, identified as encoding missense mutations in known senescence regulators NAM-A1 (T159I) and NAM-D1 (G151), respectively (Chapman et al ., 2020). ‘Non-staygreen’ refers to the parental Triticum aestivum cv.…”

Section: Methodsmentioning

confidence: 99%

“…Modelling of wheat ideotypes using 2050 climate change predictions weights staygreen traits highly, estimating associated yield benefits of 28-37 % and 10-23 % for Spanish, and Central and Eastern European growing regions respectively (Senapati et al ., 2019). Under stress, Chapman et al . (2020) reported a positive relationship between delayed senescence and grain weight improvement of NAM-1 EMS mutants, which could relate to elevated ABA levels enhancing carbon remobilisation (Distelfeld, Avni and Fischer, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…We also identified the need for singular metrics capable of discriminating between staygreen and non-staygreen types, preferably in absence of time-course phenotyping, to increase efficiency of in-field phenotyping and selection. Here, we compile resources we referred to when scoring senescence under field conditions, providing our own insights following successful mapping of NAM-1 homoeologues, which are known senescence regulators (Chapman et al ., 2020). Here we aim to equip researchers and breeders alike with the appropriate knowledge to guide future phenotyping strategies, support trait genetic mapping and selection, and help to inform weighting of senescence traits in breeding, genomic selection or other applications.…”

Section: Introductionmentioning

confidence: 99%

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Capturing and Selecting Senescence Variation in Wheat

Chapman

Orford

Lage

et al. 2020

Preprint

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Senescence is a highly quantitative trait, but in wheat the genetics underpinning senescence regulation remain relatively unknown. To select senescence variation, and ultimately identify novel genetic regulators, accurate characterisation of senescence phenotypes is essential. When investigating senescence, phenotyping efforts often focus on, or are limited to, visual assessment of the flag leaves. However, senescence is a whole plant process, involving remobilisation and translocation of resources into the developing grain. Furthermore, the temporal progression of senescence poses challenges regarding trait quantification and description, whereupon the different models and approaches applied result in varying definitions of apparently similar metrics.To gain a holistic understanding of senescence we phenotyped flag leaf and peduncle senescence progression, alongside grain maturation. Reviewing the literature, we identified techniques commonly applied in quantification of senescence variation and developed simple methods to calculate descriptive and discriminatory metrics. To capture senescence dynamism, we developed the idea of calculating thermal time to different flag leaf senescence scores, for which between year Spearman’s rank correlations of r ≥ 0.59, P < 4.7 × 10−5(TT70), identify as an accurate phenotyping method. Following our experience of senescence trait genetic mapping, we recognised the need for singular metrics capable of discriminating senescence variation, identifying Thermal Time to Flag Leaf Senescence score of 70 (TT70) and Mean Peduncle senescence (MeanPed) scores as most informative. Moreover, grain maturity assessments confirmed a previous association between our staygreen traits and grain fill extension, illustrating trait functionality.Here we review different senescence phenotyping approaches and share our experiences of phenotyping two independent RIL populations segregating for staygreen traits. Together, we direct readers towards senescence phenotyping methods we found most effective, encouraging their use when investigating and discriminating senescence variation of differing genetic bases, and to aid trait selection and weighting in breeding and research programs alike.

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Section: Resultsmentioning

confidence: 61%

Section: Methodsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%