QTLs for flag leaf size and their influence on yield-related traits in wheat (Triticum aestivum L.)

Fan, Xiangtao; Cui, Fa; Zhao, Chunhua; Zhang, Wei; Yang, Lijuan; Zhao, Xueqiang; Han, Jie; Su, Qiannan; Ji, Jun; Zhao, Zhihui; Tong, Yiping; Li, Junming

doi:10.1007/s11032-015-0205-9

Cited by 85 publications

(111 citation statements)

References 38 publications

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“…The plant materials included a set of 188 recombinant inbred lines (RILs) derived from Kenong 9204 (KN9204) × Jing 411 (J411) (denoted by KJ-RIL) (Cui et al 2014a;Fan et al 2015). The 188 kJ-RILs and their parents were grown from 2011 to 2012 in Shijiazhuang (L1); 2012-2013 in Shijiazhuang (L2), Beijing (L3) and Xinxiang (L4); and 2013-2014 in Shijiazhuang (L5).…”

Section: Plant Materials Experimental Design and Phenotypic Evaluationmentioning

confidence: 99%

“…An LN treatment and an HN treatment were applied in each trial for a total of ten environments (year × location × treatment), designated as E1, E2, E3, E4, E5, E6, E7, E8, E9, and E10. The N treatments, field arrangements and experimental designs of L1 to L4 are detailed in our previous reports (Cui et al 2014a;Fan et al 2015). T5 followed the above trial design and was carried out from 2013 to 2014 in Shijiazhuang.…”

Section: Plant Materials Experimental Design and Phenotypic Evaluationmentioning

confidence: 99%

“…To estimate the broad-sense heritability (h B 2 ) of the corresponding traits, ANOVA was performed for each environment separately using QGAStation 2.0 according to Fan et al (2015). The h B 2 values were calculated using the following formula h B 2 = V G /V P ; where V G and V P are the genetic variance and phenotypic variance, respectively.…”

Section: Statistical and Qtl Analysesmentioning

confidence: 99%

“…The threshold for the detection of QTLs was calculated using 1,000 permutations, with a type 1 error of 0.05. All of the QTLs were designated according to Fan et al (2015). A QTL with an average LOD value >3.0 and an average phenotypic variance contribution >10 % was defined as a major QTL, and one showing significance in at least five/three environments/data sets was considered a stable QTL for the 11 KRTs/KRTDVs.…”

Section: Statistical and Qtl Analysesmentioning

confidence: 99%

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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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Section: Plant Materials Experimental Design and Phenotypic Evaluationmentioning

confidence: 99%

Section: Plant Materials Experimental Design and Phenotypic Evaluationmentioning

confidence: 99%

Section: Statistical and Qtl Analysesmentioning

confidence: 99%

Section: Statistical and Qtl Analysesmentioning

confidence: 99%

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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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“…Duwayri [5] considered that grain yield and grain number per spike decreased when flag leaves were removed. Many studies have shown that flag leaf size of wheat was positively correlated with thousand-grain weight, grain number per spike, yield per plant and other yield-related traits in cereals [6][7][8][9][10]. The longer the flag leaves maintain high chlorophyll content and photosynthesis, the stronger the assimilation ability of canopy, which provide more assimilation substances for grain filling, delay leaf senescence and ultimately increase grain yield [11][12][13].…”

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confidence: 99%

QTL mapping for flag leaf-related traits and genetic effect of QFLW-6A on flag leaf width using two related introgression line populations in wheat

et al. 2020

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The flag leaf is the main organ of photosynthesis during grain-filling period of wheat, and flag leaf-related traits affect plant morphology and yield potential. In this study, two BC 3 F 6 introgression line (IL) populations derived from the common recipient parent Lumai 14 with Jing 411 and Shaanhan 8675, respectively, were used to map quantitative trait loci (QTL) for flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA) and chlorophyll content (CC) at flowering stage and 15 and 20 days after anthesis (DAA) in 2016-2017 (E1) and 2017-2018 (E2) two environments. A total of 14 and 15 QTLs for flag leaf-related traits were detected in Lumai 14 / Jing 411 and Lumai 14 / Shaanhan 8675 populations, respectively. Among them, Both QFLW-6A and QFLA-6A were detected in Lumai 14 / Jing 411 population under E2 and in Lumai 14 / Shaanhan 8675 population under E1 and E2 environments, respectively. QCC S2 -3A from Lumai 14 / Jing 411 population and QCC S3 -1A, QFLL-4A and QFLL-6A from Lumai 14 / Shaanhan 8675 population were repeatedly identified under two tested environments. Moreover, eight QTL clusters controlling flag leaf-related traits were identified, which provided a genetic basis for significant correlations in phenotype among these traits. On the other hand, positive alleles of QFLW-6A for FLW detected in two populations were derived from their donors. Eighteen lines and 44 lines carried this QTL were found in Lumai 14 / Jing 411 and Lumai 14 / Shaanhan 8675 populations, respectively. The means of FLW in these lines were wider than that of the recipient parent, Lumai 14, in two environments, suggesting that QFLW-6A played an important role for increasing FLW. The IL 124 in Lumai 14 / Jing 411 population and the IL 59 and IL 127 in Lumai 14 / Shaanhan 8675 population had five, five and four donor chromosomal segments which carried no other QTL controlling FLW than QFLW-6A, respectively. And the FLWs of these lines were significantly greater than that of Lumai 14 under two environments. So these lines and their donor parent can be regarded as potential near-isogenic lines. Further, a synteny analysis found QFLW-6A was near the 574,283,851-574,283,613 bp fragment on chromosome 6A and 10 genes were in the range of 500 kb upstream and downstream of the fragment. , et al. (2020) QTL mapping for flag leafrelated traits and genetic effect of QFLW-6A on flag leaf width using two related introgression line populations in wheat. PLoS ONE 15(3): e0229912.These results provide the basis for identification of candidate gene and map-based cloning and functional verification of the QTL. PLOS ONEQTL mapping for flag leaf-related traits PLOS ONE | https://doi.org/10.The linkage maps of Lumai 14 / Jing 411 and Lumai 14 / Shaanhan 8675 BC 3 F 6 populations were constructed, based on 156 and 185 polymorphic simple sequence repeat (SSR) markers, respectively [36,41]. QTL analysis for flag leaf-related traits was performed by IciMapping 4.0 PLOS ONEQTL mapping for flag leaf-related traits PLOS ONE | https://doi.org/10.

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Breeding Approaches for Aluminum Toxicity Tolerance in Rice and Wheat

Bui,

Nguyen

2023

Heavy Metal Toxicity and Tolerance in Plants

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QTLs for flag leaf size and their influence on yield-related traits in wheat (Triticum aestivum L.)

Cited by 85 publications

References 38 publications

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

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

QTL mapping for flag leaf-related traits and genetic effect of QFLW-6A on flag leaf width using two related introgression line populations in wheat

Breeding Approaches for Aluminum Toxicity Tolerance in Rice and Wheat

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