Genetic analysis of stay‐green, yield, and agronomic traits in spring wheat

Cook, Jason P.; Acharya, Ramesh; Martin, John M.; Blake, N. K.; Khan, Imran; Heo, Hwa‐Young; Kephart, K. D.; Eckhoff, J. L.; Talbert, L. E.; Sherman, Jamie

doi:10.1002/csc2.20302

Cited by 10 publications

(9 citation statements)

References 50 publications

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“…Compared with the individual maps, three other TKW QTLs were stably identified using the consensus map (Table 7), i.e., qTKW-1B-2, qTKW-2D-2, and qTKW-6B-3, which were in similar positions as those reported by Gerard et al (2019), Zhang et al (2020c), andCook et al (2021), respectively.…”

Section: Comparison Of the Detected Qtls With Studies On Other Mapping Populationsmentioning

confidence: 70%

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Wang

Wen

et al. 2021

Front. Plant Sci.

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Wheat is one of the most important cereal crops worldwide. A consensus map combines genetic information from multiple populations, providing an effective alternative to improve the genome coverage and marker density. In this study, we constructed a consensus map from three populations of recombinant inbred lines (RILs) of wheat using a 90K single nucleotide polymorphism (SNP) array. Phenotypic data on plant height (PH), spike length (SL), and thousand-kernel weight (TKW) was collected in six, four, and four environments in the three populations, and then used for quantitative trait locus (QTL) mapping. The mapping results obtained using the constructed consensus map were compared with previous results obtained using individual maps and previous studies on other populations. A simulation experiment was also conducted to assess the performance of QTL mapping with the consensus map. The constructed consensus map from the three populations spanned 4558.55 cM in length, with 25,667 SNPs, having high collinearity with physical map and individual maps. Based on the consensus map, 21, 27, and 19 stable QTLs were identified for PH, SL, and TKW, much more than those detected with individual maps. Four PH QTLs and six SL QTLs were likely to be novel. A putative gene called TraesCS4D02G076400 encoding gibberellin-regulated protein was identified to be the candidate gene for one major PH QTL located on 4DS, which may enrich genetic resources in wheat semi-dwarfing breeding. The simulation results indicated that the length of the confidence interval and standard errors of the QTLs detected using the consensus map were much smaller than those detected using individual maps. The consensus map constructed in this study provides the underlying genetic information for systematic mapping, comparison, and clustering of QTL, and gene discovery in wheat genetic study. The QTLs detected in this study had stable effects across environments and can be used to improve the wide adaptation of wheat cultivars through marker-assisted breeding.

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Section: Comparison Of the Detected Qtls With Studies On Other Mapping Populationsmentioning

confidence: 70%

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Wang

Wen

et al. 2021

Front. Plant Sci.

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“…Previous studies have demonstrated that unlike the cosmetic stay-green phenotype, the functional stay-green phenotype usually involves multiple metabolic pathways and is controlled by many loci (Cook et al, 2021;Ren et al, 2022). The difference in leaf senescence between CN17 and CN19 is very conspicuous, especially in the later grain filling stage; therefore, despite their genetic background being different, as demonstrated by a pedigree analysis, these cultivars were used to detect DEGs (Li et al, 2017).…”

Section: Identification Of Key Genes Responsible For the Functional S...mentioning

confidence: 99%

Comparative transcriptome analysis revealed differential gene expression involved in wheat leaf senescence between stay-green and non-stay-green cultivars

Yang

Guo

et al. 2022

Front. Plant Sci.

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Breeders agree that leaf senescence is a favorable process for wheat seed yield improvement due to the remobilization of leaf nutrients. However, several studies have suggested that staying green may be an important strategy for further increasing wheat yields. In this study, we performed a comparative transcriptome analysis between wheat cultivars CN17 and CN19 after heading and also measured photosynthetic parameters, chlorophyll (Chl) contents, and antioxidant enzyme activities at various time points after heading. The physiological and biochemical indexes revealed that CN17 exhibited a functionally stay-green phenotype while CN19 did not. We identified a total of 24,585 and 34,410 differential expression genes between genotypes at two time-points and between time-points in two genotypes, respectively, and we also found that 3 (37.5%) genes for leaf senescence, 46 (100%) for photosynthesis – antenna protein, 33 (70.21%) for Chl metabolism and 34 (68%) for antioxidative enzyme activity were upregulated in CN17 compared with CN19 during leaf senescence, which could be regulated by the differential expression of SAG39 (senescence-associated gene 39), while 22 (100%) genes for photosynthesis – antenna proteins, 6 (46.15%) for Chl metabolism and 12 (80%) for antioxidative enzyme activity were upregulated in CN17 compared with CN19 before the onset of leaf senescence. Here, we further clarified the expression profiles of genes associated with a functional stay-green phenotype. This information provides new insight into the mechanism underlying delayed leaf senescence and a new strategy for breeders to improve wheat yields.

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“…Statistical analysis. Since all wheat genotypes, except for the checks, had not been previously tested at the Siirt location, the genotypic effect was considered random in the analysis of variance (ANOVA) (Cook et al 2021). The ANOVA, t-test, and descriptive statistics for each variable (EGC, VS, and GY) measured in this experiment, which was conducted based on the ARCBD, and the correlation analysis between the variables were performed using SAS software (Wolfinger et al 1997;Kling & Merk 2021).…”

Section: Methodsmentioning

confidence: 99%

“…As the genotypes tested in the first season were different from those tested in the second season, the significance levels of the differences between the variables measured over seasons were determined by the t-test. The broad sense heritability (H 2 ) values were predicted by the best linear unbiased predictor (BLUP) statistical method (Cullis et al 2006;Cook et al 2021;Kling & Merk 2021).…”

Section: Methodsmentioning

confidence: 99%

Phenotyping winter wheat for early ground cover

Kaya¹

2022

CZECH J GENET PLANT

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The relationship between the early ground cover and the grain yield in winter wheat is not yet fully understood. In a winter wheat breeding programme, selection for early ground cover is traditionally made using visual scoring. Although visual scoring is preferred as a phenotypic screening tool by wheat breeders, its output may not be reliable, as it requires experience. A smartphone camera-based digital image technique can be recommended as a feasible, reliable, repeatable, affordable, and fast selection tool for early ground cover in wheat as an alternative to visual scoring. For this purpose, two wheat trials were conducted in the 2017–2018 and 2019–2020 seasons. In both seasons, 215 wheat genotypes in total, together with three checks from spring wheat, were tested under rain-fed conditions in the spring wheat zone in Turkey. All the tested wheat genotypes were grouped into spring, facultative, and winter growth habit using visual scoring. Simultaneously, photos were taken from each plot with a smartphone camera, and the early ground cover (%) was estimated using the smartphone camera-based digital image technique. The relationships between grain yield, visual scoring, and early ground cover could so be estimated. In both seasons, significant negative correlation between grain yield and visual scoring (r = −0.679** and r = −0.704**, respectively) and significant positive correlation between the grain yield and the early ground cover (r = 0.745** and r = 0.747**, respectively) were observed. The correlation between visual scoring and early ground cover were negative (r = −0.862** and r = −0.926**, respectively). The broad sense heritability estimates in both seasons were 0.51 and 0.85, respectively, for early ground cover, 0.91 and 0.94 for visual scoring, and 0.86 and 0.69 for grain yield. In this study, we revealed that testing winter wheat genotypes in the spring wheat zone rather than in the winter wheat zone could be a more effective way to unveil the positive relationship between the early ground cover and the grain yield. We have shown that the smartphone-based digital image technique is a useful selection tool for early ground cover in winter wheat.

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Genetic analysis of stay‐green, yield, and agronomic traits in spring wheat

Cited by 10 publications

References 50 publications

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Construction of Consensus Genetic Map With Applications in Gene Mapping of Wheat (Triticum aestivum L.) Using 90K SNP Array

Comparative transcriptome analysis revealed differential gene expression involved in wheat leaf senescence between stay-green and non-stay-green cultivars

Phenotyping winter wheat for early ground cover

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