Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits

Ma, Jian; Tu, Yang; Zhu, Jing; Luo, Wei; Liu, Huan; Liu, Cong; Li, Shuiqin; Liu, Jiajun; Ding, Ping; Habib, Ahsan; Mu, Yang; Tang, Huaping; Liu, Yaxi; Jiang, Qiantao; Chen, Guoyue; Wang, Jirui; Li, Wei; Pu, Zhien; Zheng, Yu; Wei, Yuming; Kang, Houyang; Chen, Guangdeng; Lan, Xiujin

doi:10.1007/s00122-019-03458-2

Cited by 67 publications

(66 citation statements)

References 63 publications

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“…For example, QTLs with pleiotropic effects to SN, SL, and KPS were identified on chromosomes 1B, 4B, and 5A (Deng et al, 2011). Similarly, Ma et al (2020) detected two pleiotropic QTLs associated with FLL and FLR on chromosomes 5B, two pleiotropic QTLs for FLOA and FLBA on chromosomes 2D, and three pleiotropic QTLs for FLL, FLW and FLA on chromosomes 2D, and they shared the same or overlapped physical intervals on 'Chinese Spring' (CS) genomes. Additionally, these pleiotropic QTLs exhibited significant associations in Pearson correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Agronomic traits are usually controlled by multiple genes and numerous quantitative trait loci (QTL) for them have been reported on A, B, and D genomes in wheat. For instance, in hexaploid wheat, major QTLs for FLL, FLW, flag leaf area (FLA), the ratio of length/width of flag leaf (FLR), flag leaf angle (FLANG), fag leaf opening angle (FLOA) and fag leaf bend angle (FLBA) were mapped to chromosomes 2D, 5B, 4B ( Ma et al, 2020 ). Liu et al (2018c) detected QTLs for FLL, FLW, FLA and FLANG on chromosomes 1B, 2B, 3A, 3D, 4B, 5A, 6B, 7B, and 7D using a recombinant inbred line (RIL) population derived from ‘ND3331’ and ‘Zang1817.’ Hu et al (2020) identified 161 QTLs for yield-related traits including grain yield per plant (GYP), spike number per plant (SN), kernel number per spike (KPS), SL, SNS, FLL, FLW, FLA, PH, AD and heading date (HD) on 21 chromosomes except 2D, 3D, and 6D.…”

Section: Introductionmentioning

confidence: 99%

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Major QTL for Seven Yield-Related Traits in Common Wheat (Triticum aestivum L.)

Jin

Liu

et al. 2020

Front. Genet.

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Flag leaves, plant height (PH), and spike-related traits are key determinants contributing to yield potential in wheat. In this study, we developed a recombinant inbred line (RIL) population with 94 lines derived from the cross between 'AS985472' and 'Sumai 3.' A genetic map spanned 3553.69 cM in length were constructed using 1978 DArT markers. Severn traits including flag leaf width (FLW), flag leaf length (FLL), PH, anthesis date (AD), spike length (SL), spikelet number spike (SNS), and spike density (SD) were evaluated against this RIL population under three different environments. Combined phenotypic data and genetic map, we identified quantitative trait loci (QTL) for each trait. A total of four major and stably expressed QTLs for FLW, PH, and SD were detected on chromosomes 2D and 4B. Of them, the major QTLs individually explained 10.10-30.68% of the phenotypic variation. QTLs with pleiotropic effects were identified on chromosomes 4A and 6D as well. Furthermore, the genetic relationships between seven yield-related traits were detected and discussed. A few genes related to leaf growth and development at the interval of a major locus for FLW on chromosome 2D were predicated. Overall, the present study provided useful information for understanding the genetic basis of yield-related traits and will be useful for marker-assisted selection in wheat breeding.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Major QTL for Seven Yield-Related Traits in Common Wheat (Triticum aestivum L.)

Jin

Liu

et al. 2020

Front. Genet.

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“…The wheat 55K SNP array has been widely used for genotyping and QTL identification mostly in hexaploid common wheat (Ren et al 2018;Liu et al 2020b;Ma et al 2020). In the current study, the wheat 55K SNP array was employed for genotyping a tetraploid wheat RIL population and a high-quality genetic linkage map was constructed.…”

Section: Discussionmentioning

confidence: 99%

The 55K SNP-Based Exploration of QTL for Spikelet Number Per Spike in a Tetraploid Wheat (Triticum turgidumL.) Population: Chinese Landrace ‘Ailanmai’ × Wild Emmer

Zhu

Wei

et al. 2020

Preprint

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Spikelet number per spike (SNS) is a key factor in determining kernel number per spike which has a great effect on wheat grain yield. Modern wheat breeding narrows genetic diversity among cultivars leading to a detrimental effect on future wheat improvement. It is thus of great significance to explore new genetic resources for SNS to increase wheat yield. Here, a tetraploid landrace Ailanmai mult wild emmer wheat recombinant inbred line (RIL) population was used to construct a high-density genetic map using the wheat 55K single nucleotide polymorphism (SNP) array. Our results showed that the genetic and physical locations of 94.83% (6204) of the mapped markers were consistent. Subsequently, fourteen quantitative trait loci (QTL) for SNS explaining 4.23-27.26% of the phenotypic variation were identified. QSns.sau-AM-2B.3 and QSns.sau-AM-3B.2 were considered as major and novel QTL and their combination had the largest effect and increased SNS by 17.47%. In the physical intervals of QSns.sau-AM-2B.3 and QSns.sau-AM-3B.2, some development-related genes were predicted to participate in the spikelet growth and affect SNS. Additionally, significant correlations between SNS and other agronomic traits like significant and positive correlation between SNS and thousand kernel weight were detected and analyzed. Our findings demonstrated the feasibility of wheat 55K SNP array in genetic mapping of tetraploid wheat and provided an example of exploring outstanding genetic resources from wheat related species for further utilization in common wheat improvement.

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“…These systems predate the freeware outlined above and have been applied in many studies since their release. Like GrainScan and SmartGrain, WinSEEDLE has been used in explorations of genetic and phenotypic underpinnings of yield components in wheat [111][112][113][114][115][116]. SeedCount has seen broad use in academic research.…”

Section: Challenges and Software Applicable To Seed Phenotyping In Wheatmentioning

confidence: 99%

Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

2020

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Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.

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Flag leaf size and posture of bread wheat: genetic dissection, QTL validation and their relationships with yield-related traits

Cited by 67 publications

References 63 publications

Major QTL for Seven Yield-Related Traits in Common Wheat (Triticum aestivum L.)

Major QTL for Seven Yield-Related Traits in Common Wheat (Triticum aestivum L.)

The 55K SNP-Based Exploration of QTL for Spikelet Number Per Spike in a Tetraploid Wheat (Triticum turgidumL.) Population: Chinese Landrace ‘Ailanmai’ × Wild Emmer

Image-Based, Organ-Level Plant Phenotyping for Wheat Improvement

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