Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies

Zhang, Kaixin; Liu, Shulin; Li, Wenbin; Liu, Shiping; Li, Xiyu; Fang, Yanlong; Zhang, Jun; Wang, Yue; Xu, Shichao; Zhang, Jianan; Song, Jie; Qi, Zhengtang; Tian, Xiaocui; Tian, Zhixi; Li, Wenxia; Ning, Hailong

doi:10.3389/fpls.2018.01690

Cited by 30 publications

(21 citation statements)

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“…However, we only found three SNPs which were consistently repeated in both years. We note that Zhang et al [73] detected only one common QTN in three environments in a total of 129 QTNs identified for seed protein content in soybean. Su et al [74] detected a total of 70 significant SNPs for fiber-related traits in upland cotton, of which only three SNPs were found across two environments.…”

Section: Comparison Of Present Gwas Results With Previous Studiesmentioning

confidence: 66%

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Ali

et al. 2020

IJMS

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Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.

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Section: Comparison Of Present Gwas Results With Previous Studiesmentioning

confidence: 66%

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Ali

et al. 2020

IJMS

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“…is one of the most important crops worldwide accounting for the high content of edible oil (18-20%) and high protein (35-40%) in its seeds. At present, over one-third of edible oils and two-thirds of protein meal are derived from soybean in the world [1,2]. In 2017, more than 340 million metric tons of soybean were produced globally and provided for ever-increasing human consumption and animal feed necessitates [3].…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, more than 340 million metric tons of soybean were produced globally and provided for ever-increasing human consumption and animal feed necessitates [3]. In the past decades, intensive studies on genetics, multiple omics, and genome-wide association (GWAS) in soybean have been conducted [2,[4][5][6][7][8][9]. However, a simple and high efficient transformation method is the prerequisite for functional annotations of a large number of candidate genes.…”

Section: Introductionmentioning

confidence: 99%

One-step generation of composite soybean plants with transgenic roots by Agrobacterium rhizogenes-mediated transformation

et al. 2020

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Background: Agrobacterium rhizogenes-mediated (ARM) transformation is a highly efficient technique for generating composite plants composed of transgenic roots and wild-type shoot, providing a powerful tool for studying root biology. The ARM transformation has been established in many plant species, including soybean. However, traditional transformation of soybean, transformation efficiency is low. Additionally, the hairy roots were induced in a medium, and then the generated composite plants were transplanted into another medium for growth. This two-step operation is not only time-consuming, but aggravates contamination risk in the study of plant-microbe interactions. Results: Here, we report a one-step ARM transformation method with higher transformation efficiency for generating composite soybean plants. Both the induction of hairy roots and continuous growth of the composite plants were conducted in a single growth medium. The primary root of a 7-day-old seedling was decapitated with a slanted cut, the residual hypocotyl (maintained 0.7-1 cm apical portion) was inoculated with A. rhizogenes harboring the gene construct of interest. Subsequently, the infected seedling was planted into a pot with wet sterile vermiculite. Almost 100% of the infected seedlings could produce transgenic positive roots 16 days post-inoculation in 7 tested genotypes. Importantly, the transgenic hairy roots in each composite plant are about three times more than those of the traditional ARM transformation, indicating that the one-step method is simpler in operation and higher efficiency in transformation. The reliability of the one-step method was verified by CRISPR/Cas9 system to knockout the soybean Rfg1, which restricts nodulation in Williams 82 (Nod-) by Sinorhizobium fredii USDA193. Furthermore, we applied this method to analyze the function of Arabidopsis YAO promoter in soybean. The activity of YAO promoter was detected in whole roots and stronger in the root tips. We also extended the protocol to tomato. Conclusions: We established a one-step ARM transformation method, which is more convenient in operation and higher efficiency (almost 100%) in transformation for generating composite soybean plants. This method has been validated in promoter functional analysis and rhizobia-legume interactions. We anticipate a broad application of this method to analyze root-related events in tomato and other plant species besides soybean.

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“…Cook et al (2012) constructed a NAM population of 25 RILs and identified beneficial alleles for improving maize kernel starch, protein, and oil. Zhang et al (2018a) used a four-way RIL to perform GWAS on the genetic mechanism of soybean protein. Henning et al (2016) conducted GWAS using a hop population constructed from the parents "Teamaker" and USDA 21422M, revealing the genetic basis of fiber quality traits and yield components in 231 lines.…”

Section: Introductionmentioning

confidence: 99%

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

et al. 2020

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Plant height is an important target for soybean breeding. It is a typical quantitative trait controlled by multiple genes and is susceptible to environmental influences. Here, we carried out phenotypic analysis of 156 recombinant inbred lines derived from "Dongnong L13" and "Henong 60" in nine environments at four locations over 6 years using interval mapping and inclusive composite interval mapping methods. We performed quantitative trait locus (QTL) analysis by applying pre-built simple-sequence repeat maps. We detected 48 QTLs, including nine significant QTLs detected by multiple methods and in multiple environments. Meanwhile, genotyping of all lines using the SoySNP660k BeadChip produced 54,836 non-redundant single-nucleotide polymorphism (SNP) genotypes. We used five multi-locus genome-wide association analysis methods to locate 10 quantitative trait nucleotides (QTNs), four of which overlap with previously located QTLs. Five candidate genes related to plant height are predicted to lie within 200 kb of these four QTNs. We identified 19 homologous genes in Arabidopsis, two of which may be associated with plant height. These findings further our understanding of the multigene regulatory network and genetic determinants of soybean plant height, which will be important for breeding high-yielding soybean.

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Identification of QTNs Controlling Seed Protein Content in Soybean Using Multi-Locus Genome-Wide Association Studies

Cited by 30 publications

References 50 publications

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

One-step generation of composite soybean plants with transgenic roots by Agrobacterium rhizogenes-mediated transformation

Linkage Analysis and Multi-Locus Genome-Wide Association Studies Identify QTNs Controlling Soybean Plant Height

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