Mining of Candidate Maize Genes for Nitrogen Use Efficiency by Integrating Gene Expression and QTL Data

Liu, Ruixiang; Zhang, Hao; Zhao, Peng; Zhang, Zuxin; Liang, Wenke; Tian, Zhigang; Zheng, Yanyan

doi:10.1007/s11105-011-0346-x

Cited by 52 publications

(30 citation statements)

References 40 publications

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“…Moreover, cQTL1_1Y, cQTL2_1Y, cQTL2_2Y, cQTL4_1Y, cQTL4_2Y, cQTL9_1Y, cQTL9_2Y, cQTL9_3Y, cQTL4_3P, and cQTL9_1P (on bins 1.03, 2.02, 2. 04, 4.06, 4.08, 9.02, 9.03, 9.03, and 9.03, respectively) were found within similar regions, as previously reported (Liu et al 2012). These cQTL reportedly control yield and yield component traits along with cQTL in this study, suggesting again that low-N tolerance, yield and yield component, and plant architecture cQTLs may share genetic mechanisms.…”

Section: Low-n-induced Yield and Yield Component Traits Cqtlsupporting

confidence: 90%

“…Thus, cQTL1_2Y (on bin 1.03) was reportedly located in the region from 216.55 to 225.33 cM, and we further narrowed this region (Coque et al 2008;Liu et al 2012). Moreover, cQTL1_1Y, cQTL2_1Y, cQTL2_2Y, cQTL4_1Y, cQTL4_2Y, cQTL9_1Y, cQTL9_2Y, cQTL9_3Y, cQTL4_3P, and cQTL9_1P (on bins 1.03, 2.02, 2.…”

Section: Low-n-induced Yield and Yield Component Traits Cqtlmentioning

confidence: 75%

“…Further, 46 unique N uptake and assimilation probes were identified using Affymetrix 82 K maize arrays (Liseron-Monfils et al 2013), and low-Nresponsive miRNAs and corresponding target genes were predicted in genome-wide analyses Xu et al 2011). Several global studies of Arabidopsis and rice gene and miRNA expressions have identified abundant differentially expressed genes under conditions of N starvation Krapp et al2011;Peng et al 2007;Bi et al 2007;Cai et al 2012a), and numerous expressed sequence tags (ESTs) have been associated with low-N stress with corresponding anchored candidate genes, allowing forward and reverse integration of QTL information (Liu et al 2012). Moreover, numerous genes are reportedly involved in N use efficiency (NUE) in mode plants, for example, recently, a major rice NUE quantitative trait locus was found to be synonymous with the previously identified gene DEP1 (DENSE AND ERECT PANICLES 1) using fine mapping (Sun et al 2014), and these have been sufficiently cloned and characterized for a functional segregation of N uptake efficiency genes of the nitrate transporter family (NRT), N assimilation efficiency genes of the glutamine synthetase family (GS), N translocation and remobilization efficiency genes such as asparagine synthetase (AS), and coordinate C metabolism genes (Xu et al 2012a).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies about stress-tolerant cQTL have identified QTL under dichotomous conditions but have failed to exclude QTL of the same traits detected under other conditions (Hao et al 2010;Li et al 2010c;Liu et al 2012;Semagn et al 2013;Zhang et al 2014). Because plant stress-response mechanisms involve multiple intricate physiological and biochemical pathways, the ensuing phenotypes lack direct associations with the corresponding tolerant QTL.…”

Section: Introductionmentioning

confidence: 99%

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Mining for low-nitrogen tolerance genes by integrating meta-analysis and large-scale gene expression data from maize

et al. 2015

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Nitrogen (N) is the most important macronutrient for plant growth and development. Hence, understanding genetic architectures and functional genes involved in the response to N deficiency can greatly facilitate the development of low-Ntolerant cultivars. In this study, we collected 212 quantitative trait loci (QTL) of agronomically important traits under low-N stress conditions in maize. We then identified 21 consensus QTL (cQTL) strongly induced for low-N tolerance after excluding overlapping cQTL containing QTL simultaneously identified in meta-analyses of studies performed under other environmental conditions. Among the 21 cQTL, 30 candidate maize genes were identified from maize large-scale differential expression data derived from analyses of low-N stress, and the 12 most important maize orthologs were identified using homologous BLAST analyses of genes with known functions in N use efficiency in model plants. Furthermore, maize orthologs associated with low-N tolerance and metabolism were also predicted using large-scale expression data from other model plants. The present genetic loci and candidate genes indicate the molecular mechanisms of low-N tolerance in maize and may provide information for QTL fine mapping and molecular marker-assisted selection.

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Section: Low-n-induced Yield and Yield Component Traits Cqtlsupporting

confidence: 90%

Section: Low-n-induced Yield and Yield Component Traits Cqtlmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mining for low-nitrogen tolerance genes by integrating meta-analysis and large-scale gene expression data from maize

et al. 2015

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“…Metaanalysis revealed 22 QTLs under low N conditions with 4 consensus QTLs found on chromosomes 1 and 4, 2 on chromosomes 3, 5, 6, and 9, and 3 on chromosome 2. Chromosomes 7, 9, and 10 contained only 1 consensus QTL (79). However, these analyses were looking at yield under low N, which is not what some researchers would consider to be NUE.…”

Section: Mapping Nitrogen Use Efficiency Quantitative Trait Locimentioning

confidence: 99%

The Genetics of Nitrogen Use Efficiency in Crop Plants

et al. 2015

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In the past 50 years, the application of synthetic nitrogen (N) fertilizer to farmland resulted in a dramatic increase in crop yields but with considerable negative impacts on the environment. New solutions are therefore needed to simultaneously increase yields while maintaining, or preferably decreasing, applied N to maximize the nitrogen use efficiency (NUE) of crops. In this review, we outline the definition of NUE, the selection and development of NUE crops, and the factors that interact with NUE. In particular, we emphasize the challenges of developing crop plants with enhanced NUE, using more classical genetic approaches based on utilizing existing allelic variation for NUE traits. The challenges of phenotyping, mapping quantitative trait loci (QTLs), and selecting candidate genes for NUE improvement are described. In addition, we highlight the importance of different factors that lead to changes in the NUE components of nitrogen uptake efficiency (NUpE) and nitrogen utilization efficiency (NUtE).

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Association mapping for image‐based root traits in tropical maize under water stress in semi‐arid regions

de Souza Silveira,

de Pontes,

Machado

et al. 2024

Agronomy Journal

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The root system is an organ that indicates signs of stress when a plant is subjected to water deficit conditions. However, its assessment is challenging. An alternative has been to obtain variables through image processing. In this way, it allows the rapid evaluation of genetic diversity panels. It contributes to identifying genomic regions or genes associated with the expression of the root system under water deficit. Hence, a public diversity panel of 360 inbred lines of maize was evaluated under well‐watered (WW) and water stress (WS) conditions. Roots were phenotyped image‐based. Then, genome‐wide association studies (GWAS) were conducted in WW and WS for each trait, using the Fixed and Random Model Circulating Probability Unification (FarmCPU) method. We found 23 genes or genomic regions with significant associations, of which eleven are exclusive to the WW condition, seven to the WS condition, and four are simultaneously associated with both WW and WS. All genomic regions related to the root system in the WS condition are associated with physiological mechanisms and molecular responses related to tolerance to water deficit that can be explored in subsequent studies and by breeding programs to obtain varieties more tolerant and water use efficient to this condition.This article is protected by copyright. All rights reserved

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Mining of Candidate Maize Genes for Nitrogen Use Efficiency by Integrating Gene Expression and QTL Data

Cited by 52 publications

References 40 publications

Mining for low-nitrogen tolerance genes by integrating meta-analysis and large-scale gene expression data from maize

Mining for low-nitrogen tolerance genes by integrating meta-analysis and large-scale gene expression data from maize

The Genetics of Nitrogen Use Efficiency in Crop Plants

Association mapping for image‐based root traits in tropical maize under water stress in semi‐arid regions

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