Quantitative trait loci mapping for stomatal traits in interspecific hybrids of Eucalyptus

Sumathi, M.; Bachpai, V. K. W.; Deeparaj, B.; Mayavel, A.; Dasgupta, Modhumita Ghosh; Nagarajan, B.; Rajasugunasekar, D.; Sivakumar, V.; Yasodha, R.

doi:10.1007/s12041-018-0896-x

Cited by 8 publications

(11 citation statements)

References 22 publications

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“…These results demonstrate the superiority of current GBS technology in the genetic map construction for Eucalyptus . The marker density of our E. urophylla map and consensus map were slightly lower than that recently reported in E. urophylla (1773 SNPs with 885.9 cM) ( Bartholomé et al., 2015 ) and the consensus map of E. grandis × E. urophylla (2290 markers with 1107.6 cM) ( Kullan et al., 2012 ), however, the E. tereticornis map constructed here is the densest to date (such as, 585 markers with 1241.4 cM in Li et al., 2015 ; 204 markers with 1023.56 cM in Sumathi et al., 2018 ). Generally, the density of maps constructed here also compare favourably with recent GBS- or restriction site-associated sequencing (RAD-seq) maps for other forest species.…”

Section: Discussioncontrasting

confidence: 86%

“…The publication of the E. grandis reference genome ( Myburg et al., 2014 ) has also significantly aided QTL mapping efforts, with high synteny between E. grandis and other common commercial eucalypts. In Eucalyptus , most of the previous QTL studies found few substantive associations between target traits and candidate genes due to the low resolution of genetic map, large QTL interval, or lack of reference genomic information ( Freeman et al., 2009 ; Thumma et al., 2010 ; Freeman et al., 2013 ; Sumathi et al., 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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High-density genetic linkage mapping reveals low stability of QTLs across environments for economic traits in Eucalyptus

et al. 2023

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IntroductionEucalyptus urophylla, E. tereticornis and their hybrids are the most important commercial forest tree species in South China where they are grown for pulpwood and solid wood production. Construction of a fine-scale genetic linkage map and detecting quantitative trait loci (QTL) for economically important traits linked to these end-uses will facilitate identification of the main candidate genes and elucidate the regulatory mechanisms.MethodA high-density consensus map (a total of 2754 SNPs with 1359.18 cM) was constructed using genotyping by sequencing (GBS) on clonal progenies of E. urophylla × tereticornis hybrids. QTL mapping of growth and wood property traits were conducted in three common garden experiments, resulting in a total of 108 QTLs. A total of 1052 candidate genes were screened by the efficient combination of QTL mapping and transcriptome analysis.ResultsOnly ten QTLs were found to be stable across two environments, and only one (qSG10Stable mapped on chromosome 10, and associated with lignin syringyl-to-guaiacyl ratio) was stable across all three environments. Compared to other QTLs, qSG10Stable explained a very high level of phenotypic variation (18.4–23.6%), perhaps suggesting that QTLs with strong effects may be more stably inherited across multiple environments. Screened candidate genes were associated with some transcription factor families, such as TALE, which play an important role in the secondary growth of plant cell walls and the regulation of wood formation.DiscussionWhile QTLs such as qSG10Stable, found to be stable across three sites, appear to be comparatively uncommon, their identification is likely to be a key to practical QTL-based breeding. Further research involving clonally-replicated populations, deployed across multiple target planting sites, will be required to further elucidate QTL-by-environment interactions.

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Section: Discussioncontrasting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

High-density genetic linkage mapping reveals low stability of QTLs across environments for economic traits in Eucalyptus

et al. 2023

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“…The number and strength of QTL identified for leaf gas exchange traits (1–4 QTL per trait in a single experiment) were similar to previous studies of those traits ( Hervé et al, 2001 ; Teng et al, 2004 ; Pelleschi et al, 2006 ). In contrast, a greater number of QTL were identified for many of the stomatal patterning traits (e.g., PD—7, SI—9, SCA—9, SCD—9, SCTA—7 QTL in a single experiment) than in previous studies ( Patto et al, 2003 ; Hall et al, 2005 ; Laza et al, 2010 ; Schoppach et al, 2016 ; Shahinnia et al, 2016 ; Liu et al, 2017 ; Sumathi et al, 2018 ; Delgado et al, 2019 ; Prakash et al, 2020 ). This larger number of significant QTL was linked to more small-effect QTL (PVE < 10%) being successfully identified.…”

Section: Discussionmentioning

confidence: 73%

“…Linkage mapping in barley ( Hordeum vulgare ), wheat ( Triticum aestivum ), and rice ( Oryza sativa ) has discovered quantitative trait loci (QTL) that are associated with stomatal patterning traits ( Patto et al, 2003 ; Laza et al, 2010 ; Liu et al, 2014 , 2017 ; Sumathi et al, 2018 ), including some that colocalize with yield QTL ( Shahinnia et al, 2016 ). But, the only reports of similar experiments in maize predate statistical techniques, such as QTL mapping ( Heichel, 1971 ).…”

Section: Introductionmentioning

confidence: 99%

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping

et al. 2021

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Stomata are adjustable pores on leaf surfaces that regulate the trade-off of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping epidermal cell traits have limited discoveries about the genetic basis of stomatal patterning. A high-throughput epidermal cell phenotyping pipeline is presented here and used for quantitative trait loci (QTL) mapping in field-grown maize (Zea mays). The locations and sizes of stomatal complexes and pavement cells on images acquired by an optical topometer from mature leaves were automatically determined. Computer estimated stomatal complex density (SCD; R2 = 0.97) and stomatal complex area (SCA; R2 = 0.71) were strongly correlated with human measurements. Leaf gas exchange traits were genetically correlated with the dimensions and proportion of stomatal complexes (rg = 0.39 to 0.71) but did not correlate with SCD. Heritability of epidermal traits was moderate to high (h2 = 0.42-0.82) across two field seasons. 36 QTLs were consistently identified for a given trait in both years. 24 hotspots of overlapping QTLs for multiple traits were identified, with univariate versus multivariate single marker analysis providing evidence consistent with pleiotropy in multiple cases. Putative orthologs of genes known to regulate stomatal patterning in Arabidopsis (Arabidopsis thaliana) were located within some, but not all, of these regions. This study demonstrates how discovery of the genetic basis for stomatal patterning can be accelerated in maize, a C4 model species where these processes are poorly understood.

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“…Some researchers, such as Takahashi [5], Peel [6], and Sumathi [7], used manual counting in their studies to determine the number of epidermal stomata. This approach is obviously tedious and labour‐intensive, and errors and omissions are prone to occur when the number of pores is large.…”

Section: Introductionmentioning

confidence: 99%

An automatic plant leaf stoma detection method based on YOLOv5

Guo

Gong

et al. 2022

IET Image Processing

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The stomata on the leaf surface are mainly responsible for the material exchange between the internal and external environments of the plant, a large number of methods have been proposed to automatically measure the distribution position and number of stomatal, but few methods could achieve both stomatal count and open/closed-state judgment. Therefore, this study proposes an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning. In order to obtain more stomatal feature information and send it to the network for learning, the proposed method adds a coordinate attention (CA) mechanism to the YOLOV5 backbone part. At the same time, in order to avoid the overfitting of the model during the training process, the authors added the training trick of label smoothing. Finally, the detection ability of the proposed method for stomata is verified on the broad bean leaves stomata dataset. The experimental results show that our method achieves a detection accuracy of 0.934 and an mAP of 0.968. By comparing with other state-of-the-art algorithms, the detection capability of our method has been significantly improved. The generalization of the model is verified on the wheat leaf stomatal dataset. The experimental results show that our method can achieve a detection accuracy of 0.894 and an mAP of 0.907.

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Quantitative trait loci mapping for stomatal traits in interspecific hybrids of Eucalyptus

Cited by 8 publications

References 22 publications

High-density genetic linkage mapping reveals low stability of QTLs across environments for economic traits in Eucalyptus

High-density genetic linkage mapping reveals low stability of QTLs across environments for economic traits in Eucalyptus

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping

An automatic plant leaf stoma detection method based on YOLOv5

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