Identification of QTLs associated with seedling root traits and their correlation with plant height in wheat

Bai, Chen; Liang, Yinli; Hawkesford, Malcolm J.

doi:10.1093/jxb/ert041

Cited by 185 publications

(204 citation statements)

References 28 publications

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“…Identification of such types of coincident QTLs has been reported in several other studies (Babu et al, 2003;Lanceras et al, 2004;Quarrie et al, 2006;Pushpendra et al, 2007;Bai et al, 2013), which could be due to (i) tight linkages between genes or QTLs that regulate the expression of separate traits or (ii) pleiotropic effect-the same gene or QTL may have an effect on two or more traits simultaneously (Tuberosa et al, 2002). Identification of such types of coincident QTLs has been reported in several other studies (Babu et al, 2003;Lanceras et al, 2004;Quarrie et al, 2006;Pushpendra et al, 2007;Bai et al, 2013), which could be due to (i) tight linkages between genes or QTLs that regulate the expression of separate traits or (ii) pleiotropic effect-the same gene or QTL may have an effect on two or more traits simultaneously (Tuberosa et al, 2002).…”

Section: Discussionsupporting

confidence: 52%

Mapping QTLs Controlling Agronomic Traits in the ‘Attila’ × ‘CDC Go’ Spring Wheat Population under Organic Management using 90K SNP Array

et al. 2017

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Our group previously reported five quantitative trait loci (QTL) associated with plant height, test weight, thousand‐kernel weight, and grain protein content in a recombinant inbred line population derived from spring wheat (Triticum aestivum L.) cultivars ‘Attila’ and ‘CDC Go’, evaluated across three environments (2008–2010) under organic management and genotyped with 579 diversity arrays technology and Rht‐B1 markers. No QTL was identified for flowering time, maturity, grain yield, and number of tillers across all three environments. In the present study, we reanalyzed the same phenotypic data with a subset of 1200 informative single‐nucleotide polymorphic (SNP) markers out of the 90K SNP array and three gene‐specific markers (Ppd‐D1, Vrn‐A1, and Rht‐B1) to investigate if high marker density improves QTL detection. Here, five moderate‐ and eleven minor‐effect QTLs were detected across all three organic environments using the new genotypic data, including 13 QTLs that were not previously detected. Up to five QTLs were detected for each trait, except grain protein content, which individually accounted for 5.5 to 18.8% of phenotypic variance. For each trait, the total phenotypic and genetic variance explained by all detected QTLs varied from 9.3 to 39.4 and from 24.6 to 96.8%, respectively, which was much greater than in our previous study. One of the moderate‐effect QTLs on 5A was coincidental for flowering time and maturity and mapped close to the Vrn‐A1 gene, while the second moderate‐effect coincidental QTL on 4B was associated with both plant height and maturity but was 27 cM from the Rht‐B1 gene. Results from this study provide additional information for wheat researchers and organic wheat breeders.

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Section: Discussionsupporting

confidence: 52%

Mapping QTLs Controlling Agronomic Traits in the ‘Attila’ × ‘CDC Go’ Spring Wheat Population under Organic Management using 90K SNP Array

et al. 2017

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“…QKh-2D.1 and QTw-2D might have been identified due to the presence of other linked genes. The Rht-B1 gene is associated with physiological processes that affect not only plant height but also yield, grain quality, seedling vigor and adaptability to adverse environments (Fischer and Stockman 1986;Gooding et al 1999Gooding et al , 2012McCartney et al 2006;Zheng et al 2010;Bai et al 2013;Zhang et al 2013;Asif et al 2015). This study indicated that Rht-B1b had negative effects on TKW, KW, KDR, TW, DT, and ZEL.…”

Section: Novel Stable Qtls and Qtl Co-segregation With Known Genesmentioning

confidence: 80%

QTL detection for wheat kernel size and quality and the responses of these traits to low nitrogen stress

et al. 2015

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QTLs for tolerance to N stress, 27 and 14 of which were stable across the tested environments, respectively. These QTLs were distributed across all wheat chromosomes except for chromosomes 3A, 4D, 6D, and 7B. Eleven QTL clusters that simultaneously affected kernel size-and quality-related traits were identified. At nine locations, 25 of the 49 QTLs for N deficiency tolerance coincided with the QTLs for kernel characteristics, indicating their genetic independence. The feasibility of indirect selection of a superior genotype for kernel size and quality under high-N conditions in breeding programs designed for a lower input management system are discussed. In addition, we specified the functions of Glu-A1, Glu-B1, Glu-A3, Glu-B3, TaCwi-A1, TaSus2, TaGS2-D1, PPO-D1, Rht-B1, and Ha with regard to kernel characteristics and the sensitivities of these characteristics to N stress. This study provides useful information for the genetic improvement of wheat kernel size, quality, and resistance to N stress. Abbreviations GPCGrain protein content WGC Wet gluten content AbstractKey message QTLs for kernel characteristics and tolerance to N stress were identified, and the functions of ten known genes with regard to these traits were specified. Abstract Kernel size and quality characteristics in wheat (Triticum aestivum L.) ultimately determine the end use of the grain and affect its commodity price, both of which are influenced by the application of nitrogen (N) fertilizer. This study characterized quantitative trait loci (QTLs) for kernel size and quality and examined the responses of these traits to low-N stress using a recombinant inbred line population derived from Kenong 9204 × Jing 411. Phenotypic analyses were conducted in five trials that each included lowand high-N treatments. We identified 109 putative additive QTLs for 11 kernel size and quality characteristics and 49Communicated by I. Mackay.F. Cui, X. Fan, and M. Chen contributed equally to this work. Electronic supplementary materialThe online version of this article

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“…QTL for root traits have been mapped in rice (Oryzasativa L.) (Champoux et al 1995;Zheng et al 2000;Courtois et al 2013;Sandhu et al 2013;Liang et al 2013;Dang et al 2014), maize (Zea mays L.) (Lebreton et al 1995;Rahman et al 2011;Osman et al 2013), sorghum (Mace et al 2012), soybean (Zhang et al 2014), Brassica napus , common bean (Phaseolus vulgaris L.) (Ochoa et al 2006;López-Marín et al 2009), Arabidopsis (Fitz Gerald et al 2006), and bread wheat (Spielmeyer et al 2007;Maccaferri et al 2008;Zhang et al 2008;Li et al 2010;Sharma et al 2011;Guo et al 2012;Hamada et al 2012;Ibrahim et al 2012;Ren et al 2012;Bai et al 2013;Christopher et al 2013;Liu et al 2013;Yu and Chen 2013;Zhang et al 2013). In some cases, these studies have suggested possible roles for these QTL in determining plant yield, due to the overlap of QTL for root features with those for traits related to productivity (Johnson et al 2000;Tuberosa et al 2002a, b;Steele et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of root morphological traits in wheat

et al. 2014

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Traits related to root architecture are of great importance for yield performance of crop species, although they remain poorly understood. The present study is aimed at identifying the genomic regions involved in the control of root morphological traits in durum wheat (Triticum durum Desf.). A set of 123 recombinant inbred lines derived from the durum wheat cross of cvs. 'Creso' × 'Pedroso' were grown hydroponically to two growth stages, and were phenotypically evaluated for a number of root traits. In addition, meta-(M)QTL analysis was performed that considered the results of other root traits studies in wheat, to compare with the 'Creso' × 'Pedroso' cross and to increase the QTL detection power. Eight quantitative trait loci (QTL) for traits related to root morphology were identified on chromosomes 1A, 1B, 2A, 3A, 6A and 6B in the 'Creso' × 'Pedroso' segregating population. Twenty-two MQTL that comprised from two to six individual QTL that had widely varying confidence intervals were found on 14 chromosomes. The data from the present study provide a detailed analysis of the genetic basis of morphological root traits in wheat. This study of the 'Creso' × 'Pedroso' durum-wheat population has revealed some QTL that had not been previously identified.

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Identification of QTLs associated with seedling root traits and their correlation with plant height in wheat

Cited by 185 publications

References 28 publications

Mapping QTLs Controlling Agronomic Traits in the ‘Attila’ × ‘CDC Go’ Spring Wheat Population under Organic Management using 90K SNP Array

Mapping QTLs Controlling Agronomic Traits in the ‘Attila’ × ‘CDC Go’ Spring Wheat Population under Organic Management using 90K SNP Array

QTL detection for wheat kernel size and quality and the responses of these traits to low nitrogen stress

Genetic analysis of root morphological traits in wheat

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