Quantitative trait loci analysis for the developmental behavior of Soybean (Glycine max L. Merr.)

Sun, Desheng; Li, Wenbin; Zhang, Zhongchen; Chen, Qingshan; Ning, Hailong; Qiu, Lijuan; Sun, Genlou

doi:10.1007/s00122-005-0169-y

Cited by 84 publications

(96 citation statements)

References 29 publications

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“…The association of developmental behavior of quantitative traits with molecular markers had been reported in rice and cotton (Wu et al, 1999;Ye et al, 2003;Yan et al, 1998a, b), and morphological traits and seed quality traits in soybean (Sun et al, 2006;Li et al, 2007;Xin et al, 2008). The main objective of the present study was to identify both conditional and unconditional QTL underlying the development of gain in seed weight and the QTL underlying final mean seed weight in soybean.…”

Section: Introductionmentioning

confidence: 69%

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QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

Teng

Han

et al. 2008

Heredity

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At harvest traits such as seed weight are the sum of development and responses to stresses over the growing season and particularly during the reproductive phase of growth. The aim here was to measure quantitative trait loci (QTL) underlying the seed weight from early development to drying post harvest. One hundred forty-three F 5 derived recombinant inbred lines (RILs) developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were used for the analysis of QTL underlying mean 100-seed weight at six different developmental stages. QTL Â Environment interactions (QE) were analyzed by a mixed genetic mode based on 3 years' data. At an experiment-wise threshold of a ¼ 0.05 and by single-point analysis 94 QTL unaffected by QE underlay the mean seed weight at different developmental stages. Sixty-eight QTL affected by QE that also underlay mean seed weight were identified. From the 162 QTL 42 could be located on 12 linkage groups by composite interval mapping (LOD42.0). The numbers, locations and types of the QTL and the genetic effects were different at each developmental stage. On linkage group C2 the distantly linked QTL swC2-1, swC2-2 and swC2-3 each affected mean seed weight throughout the different developmental stages. The DNA markers linked to the QTL possessed potential for use in marker-assisted selection for soybean seed size. The identification of QTL with genetic main effects and QE interaction effects suggested that such interactions might significantly alter seed weight during seed development.

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Section: Introductionmentioning

confidence: 69%

“…Developmental programs are very important factors controlling the development of quantitative traits such as seed composition, yield and plant height (Yan et al, 1998b;Sun et al, 2006;Li et al, 2007). The development of some quantitative traits occurs through the actions and interactions of many genes.…”

Section: Introductionmentioning

confidence: 99%

QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

Teng

Han

et al. 2008

Heredity

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“…In this study, most QTL were detected at only one stage, and a few QTL were expressed in two continuous or separate stages. Sun et al (2006) and Zheng et al (2011) believe that this is the result of differential gene expression at different times. These results indicate that QTL/gene expression is timedependent during the entire developmental process.…”

Section: Discussionmentioning

confidence: 99%

Dynamic QTL analysis of protein content and glutamine synthetase activity in recombinant inbred wheat lines

Liang

et al. 2015

Genet. Mol. Res.

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ABSTRACT. Protein content (PC) is a crucial factor that determines the end-use and nutritional quality of wheat (Triticum aestivum). Glutamine synthetase (GS), which is a major participant in nitrogen metabolism, can convert inorganic nitrogen into organic nitrogen. Although many studies have been conducted on PC and GS, a dynamic analysis of all of the filling stages has not been conducted. Therefore, 115 F 9-10 recombinant inbred wheat lines of 'R131/ R142' were used to analyze PC and GS activity during different developmental stages, using the conditional quantitative trait loci (QTL) mapping method. Twenty-two and six conditional QTL were detected for PC and GS activily, respectively. More QTL in leaf PC were detected during the early filling stages than in the later filling stages. Grain PC QTL displayed different dynamic variations to leaf PC QTL during the entire grain-filling stages. All of the QTL were expressed differently over time, and nine conditional QTL were detected across two filling stages. QTL with similar functions may have tended to group in specific locales. This study provides dynamic 8707 Dynamic QTL analysis in wheat RIL ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (3): 8706-8715 (2015) genetic information on protein accumulation during grain-filling stages.

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“…Recently, conditional QTL mapping, or a combination of unconditional and conditional QTL mapping has been used to study the dynamics of developmental traits. Sun et al [63] used conditional and unconditional mapping to study the developmental behavior of soybean. Yang et al [45] used conditional mapping to dissect the developmental behavior of tiller number and plant height, and found that conditional mapping was superior to conventional mapping for studying developmental traits.…”

Section: Dynamic Mapping For Developmental Traitsmentioning

confidence: 99%

Statistical approaches in QTL mapping and molecular breeding for complex traits

Zhu

2012

Chin. Sci. Bull.

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Most of the important agronomic traits in crops, such as yield and quality, are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction. Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs. Conventionally, the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects. With recent advances in molecular genotyping and high-throughput technology, the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible. The improvement of complex traits has also been achieved by pyramiding individual QTL. In this review, we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction, and discuss their applications in crop breeding for complex traits. The most important economic traits in crops are quantitative in nature. The genetic variation of quantitative traits is usually controlled by minor-effect genes and the environments as well as by epistatic effects between different genes and by interactions between genes and environments. Because of their complicated genetic architecture, quantitative traits are usually referred to as complex trait. The genetic architecture includes knowledge about the numbers and positions of the genes in the chromosomes, the magnitude of their effects, and the contributions of additive, dominance and epistatic effects [1].Most plant breeders and quantitative geneticists have long been interested in uncovering the genetic architecture of yield and other complex traits [2]. Understanding genetic architecture can provide insights into how the architecture is translated into genetic variation and selection response [3]. Based on diallele cross experiments, the genetic architecture of a trait has been analyzed by decomposing the trait variation into additive, dominance, and epistasis variances and studying their interaction variances with the environments [4][5][6][7]. The total genetic covariance between two traits can also be dissected into genetic covariate components. With the increasing availability of molecular markers, the quantitative genetics theory of complex traits and marker-assisted selection has undergone major advances. It is now possible to connect marker variations with phenotypic variations, and to dissect genetic architecture of traits into individual genetic variants. The selection of traits can also be achieved using individual quantitative trait loci (QTLs). In this review, we discuss some of the statistical methods used for QTL mapping of experimental populations and examine their applications in marker-assisted selection (MAS) for improving complex traits.

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Quantitative trait loci analysis for the developmental behavior of Soybean (Glycine max L. Merr.)

Cited by 84 publications

References 29 publications

QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

Dynamic QTL analysis of protein content and glutamine synthetase activity in recombinant inbred wheat lines

Statistical approaches in QTL mapping and molecular breeding for complex traits

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