QTL mapping for stay-green in maize (<i>Zea mays</i>)

WangAi-yu,; Liyan,; ZhangChun-qing,

doi:10.4141/cjps2011-108

Cited by 46 publications

(36 citation statements)

References 37 publications

(36 reference statements)

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“…at P # 0.05 and 0.01, respectively. quantitative trait loci underlying the stay-green trait (Zheng et al, 2009;Wang et al, 2012;Belícuas et al, 2014). Our study expands this knowledge by evaluating the role of a larger number of alleles captured in a diversity panel encompassing a substantial portion of the genetic variation in U.S. midwestern dent maize.…”

Section: Discussionmentioning

confidence: 92%

“…Valuable insights into the genetic architecture of senescence have been provided by the forward genetic screens in multiple plant species; however, these mostly represent the major effect of genes producing visible mutant phenotypes. Biparental mapping populations have also been used to identify quantitative trait loci in Arabidopsis (Wingler et al, 2010;Chardon et al, 2014), maize (Zheng et al, 2009;Wang et al, 2012;Belícuas et al, 2014;Trachsel et al, 2016), rice (Oryza sativa; Jiang et al, 2004;Abdelkhalik et al, 2005;Yoo et al, 2007), sorghum (Tuinstra et al, 1997;Xu et al, 2000;Harris et al, 2007), and wheat (Vijayalakshmi et al, 2010;Bogard et al, 2011;Pinto et al, 2016). These studies have been useful in understanding the genetic architecture of senescence but often lack the resolution to identify the underlying genes.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize

et al. 2019

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Premature senescence in annual crops reduces yield, while delayed senescence, termed stay-green, imposes positive and negative impacts on yield and nutrition quality. Despite its importance, scant information is available on the genetic architecture of senescence in maize (Zea mays) and other cereals. We combined a systematic characterization of natural diversity for senescence in maize and coexpression networks derived from transcriptome analysis of normally senescing and stay-green lines. Sixty-four candidate genes were identified by genome-wide association study (GWAS), and 14 of these genes are supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport and signaling, and sink activity. Eight of the GWAS candidates, independently supported by a coexpression network underlying stay-green, include a trehalose-6-phosphate synthase, a NAC transcription factor, and two xylan biosynthetic enzymes. Source-sink communication and the activity of cell walls as a secondary sink emerge as key determinants of staygreen. Mutant analysis supports the role of a candidate encoding Cys protease in stay-green in Arabidopsis (Arabidopsis thaliana), and analysis of natural alleles suggests a similar role in maize. This study provides a foundation for enhanced understanding and manipulation of senescence for increasing carbon yield, nutritional quality, and stress tolerance of maize and other cereals.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize

et al. 2019

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“…Wang et al. () used 189 F 2 individuals derived from a single cross of inbred lines A150‐3‐2 (a stay‐green inbred line) and Mo17 and detected 15 QTL on chromosomes 1, 4, 5, 6 and 9 for green leaf area per plant (GLA), total leaf number per plant (TLN) and left green leaf number per plant (LLN). Among the 15 QTL, qGLA1‐1‐1 , qGLA2‐1‐1 and qGLA3‐1‐1 were on the same region with qFv/Fm01‐01 (21.76–39.12 cM), qCHL01‐02 (163.14–182.88 cM) and qSGA01‐01 (198.27–223.69 cM) detected in our study, respectively; qTLN‐2‐1 was on the same region of qFv/Fm02‐01 (64.93–84.12 cM); qTLN‐3‐1 was on the same region of qFv/Fm02‐01 (118.08–136.93 cM); qGLA2‐5‐1 , qGLA3‐5‐1 and qLLN‐5‐1 were on the same region of qFv/Fm05‐01 and qSGA05‐01 (90.38–110.22 cM).…”

Section: Discussionmentioning

confidence: 99%

Mapping and validation of the quantitative trait loci for leaf stay‐green‐associated parameters in maize

Yang

Zhang

et al. 2017

Plant Breeding

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Functional stay‐green is generally regarded as a desirable trait of varieties in major crops including maize. In this study, we used an F3:4 recombinant inbred line population with 165 lines from a cross between a stay‐green inbred line (Zheng58) and a model inbred line (B73) using 211 polymorphic simple sequence repeat markers to map quantitative trait loci for three stay‐green‐associated parameters, chlorophyll content, photosystem II photochemical efficiency and stay‐green area, at maturity stage, detected a total of 23 quantitative trait loci (QTL) on nine chromosomes. Single QTL explained 3.7–13.5% of the phenotypic variance. Additionally, we validated some important stay‐green QTL using a heterogeneous inbred family approach and found that the stay‐green‐associated parameters were significantly correlated with the plant yield. This study may contribute to a better insight into the regulatory mechanism behind leaf stay‐green in maize and a novel development of elite maize varieties with delayed leaf senescence through molecular marker‐assisted selection.

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“…Stay-green is a physiological mechanism that delays or renders inoperative foliar senescence (Thomas and Howarth, 2000;Thomas and Ougham, 2014). Stay-green has been widely studied in several crops: sorghum Kebede et al, 2001;Mace et al, 2012;Crasta et al, 1999;Sanchez et al, 2002;Subudhi et al, 2000;Tao et al, 1998), wheat (Christopher et al, 2008;Joshi et al, 2007;Kumar et al, 2010;Lopes and Reynolds, 2012), maize (Wang et al, 2012a;Zheng et al, 2009), and rice (Gustafson et al, 2011;Jiang et al, 2004;Lim and Paek, 2015;Rong et al, 2013). The expression of stay-green in sorghum under normal and stress conditions is a complex physiological process.…”

Section: Researchmentioning

confidence: 99%

QTL Mapping for Grain Yield, Flowering Time, and Stay‐Green Traits in Sorghum with Genotyping‐by‐Sequencing Markers

Sukumaran

Zhu

et al. 2016

Crop Science

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Molecular breeding can complement traditional breeding approaches to achieve genetic gains in a more efficient way. In the present study, genetic mapping was conducted in a sorghum recombinant inbred line (RIL) population developed from Tx436 (a non‐stay‐green high food quality inbred) × 00MN7645 (a stay‐green high yield inbred) and evaluated in eight environments (location and year combination) in a hybrid background of Tx3042 (a non‐stay‐green A‐line). Phenotyping was conducted for agronomic traits (grain yield and flowering time), physiological traits of stay‐green (chlorophyll content [SPAD] and chlorophyll fluorescence [Fv/Fm] measured on the leaves), and green leaf area visual score (GLAVS). This population was genotyped with genotyping‐by‐sequencing (GBS) technology. Data processing resulted in 7144 high quality single nucleotide polymorphisms (SNPs) that were used in a genome‐wide single marker scan with physical distance. A selected subset of 1414 SNPs was used for composite interval mapping (CIM) with genetic distance. These complementary methods revealed fifteen QTLs for the traits studied. In addition, QTL mapping for individual environments and year‐wise combinations revealed 42 QTLs. A consistent QTL for grain yield under normal and stressed conditions was identified in chromosome 1 that explained 8 to 16% of the phenotypic variation. QTLs for flowering time were identified in chromosomes 2, 6, and 9 that explained 6 to 11% of the phenotypic variation. Stay‐green QTLs in chromosomes 3 and 4 explained 8 to 24% of the phenotypic variation. These identified QTLs with flanking SNPs of known genomic positions could be used to improve grain yield, flowering time, and stay‐green in sorghum molecular breeding programs.

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QTL mapping for stay-green in maize (Zea mays)

Cited by 46 publications

References 37 publications

Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize

Integrated Genome-Scale Analysis Identifies Novel Genes and Networks Underlying Senescence in Maize

Mapping and validation of the quantitative trait loci for leaf stay‐green‐associated parameters in maize

QTL Mapping for Grain Yield, Flowering Time, and Stay‐Green Traits in Sorghum with Genotyping‐by‐Sequencing Markers

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