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

Teng, Weili; Han, Yingpeng; Du, Ye; Sun, Desheng; Zhang, Zhao; Qiu, Lijuan; Sun, Genlou; Li, W

doi:10.1038/hdy.2008.108

Cited by 85 publications

(81 citation statements)

References 42 publications

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“…Seeds were dried for 30 min in an oven at 105°C and continuously dried until the seed weight was stable at 50-70°C. All dried seeds were weighed (Teng et al 2009). The seed filling rate was calculated as follows: (W t -W t-1 )/10, and represented by the tD|(t -1)D stage ).…”

Section: Plant Materialsmentioning

confidence: 99%

Identification of QTL underlying the filling rate of protein at different developmental stages of soybean seed

et al. 2010

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Seed protein content at the harvest stage is the sum of protein accumulation during seed filling. The aim of our investigation was to identify loci underlying the filling rate of seed protein at different developmental stages. To this end, we used 143 recombinant inbred lines (RILs) derived from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' and composite interval mapping with a mixed genetic model. The genotype 9 environment interactions of the quantitative trait loci (QTL) were also evaluated. Thirty-nine unconditional QTL underlying the filling rate of seed protein at five developmental stages were mapped onto 14 linkage groups. The proportion of phenotypic variation explained by these QTL ranged from 4.88 to 26.05%. Thirty-eight conditional QTL underlying the filling rate of seed protein were mapped onto 16 linkage groups. The proportion of phenotypic variation explained by these QTL ranged from 1.87 to 31.34%. The numbers and types of QTL and their genetic effects on the filling rate of seed protein were different at each developmental stage. A G 9 E interaction effect was observed for some QTL.

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Section: Plant Materialsmentioning

confidence: 99%

Identification of QTL underlying the filling rate of protein at different developmental stages of soybean seed

et al. 2010

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“…It is necessary, therefore, to understand the dynamics of gene expression for a trait at different developmental stages as a basis for quantitative trait manipulation (Xu 1997). Recently, time-related QTL mapping has been used to reveal the genetic basis of developmental characters such as grain filling (Takai et al 2005), growth rate (Li et al 2006), blast resistance (Li et al 2008) and tiller number (Yan et al 1998a;Wu et al 1999) in rice, plant height in maize (Yan et al 2003), and seed weight in soybean (Teng et al 2009). So far, no report has documented the dynamic analysis of QTLs for plant height in wheat.…”

Section: Introductionmentioning

confidence: 98%

QTL mapping for developmental behavior of plant height in wheat (Triticum aestivum L.)

Wang

Ren

et al. 2010

Euphytica

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A recombinant inbred line (RIL) population with 305 lines derived from a cross of Hanxuan 10 9 Lumai 14 was used to identify the dynamic quantitative trait loci (QTL) for plant height (PH) in wheat (Triticum aestivum L.). Plant heights of RILs were measured at five stages in three environments. Total of seven genomic regions covering PH QTL clusters on different chromosomes identified from a DH population derived from the same cross as the RIL were used as the candidate QTLs and extensively analyzed. Five additive QTLs and eight pairs of epistatic QTLs significantly affecting plant height development were detected by unconditional QTL mapping method. Six additive QTLs and four pairs of epistatic QTLs were identified using conditional mapping approach. Among them, three additive QTLs (QPh.cgb-1B.3, QPh.cgb-4D.1, QPh.cgb-5B.2) and three pairs of epistatic QTLs (QPh.cgb-1B.1-QPh.cgb-1B.3, QPh.cgb-2A.1-QPh.cgb-2D.1, QPh.cgb-2D.1 -QPh.cgb-5B.2) were common QTLs detected by both methods. Three QTLs (QPh.cgb-4D.1, QPh.cgb-5B.3, QPh.cgb-5B.4) were expressed under both drought and well-water conditions. The present data are useful for wheat genetic manipulations through molecular marker-assisted selection (MAS), and provides new insights into understanding the genetic mechanism and regulation network underlying the development of plant height in crops. Our result in this study indicated that combining unconditional and conditional mapping methods could make it possible to reveal not only the stable/conserved QTLs for the developmental traits such as plant height but also the dynamic expression feature of the QTLs.

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“…The conditional mapping method has been used to map QTL for plant height in maize (Yan et al 2003), brown plant hopper resistance in rice (Ren et al 2004), pod number and the main stem and plant height of soybean (Sun et al 2006), seed weight of soybean (Teng et al 2009), plant height of wheat (Cui et al 2011;Wang et al 2010) and linolenic acid content of soybean seed (Han et al 2011). But, so far, there is no report on the use of conditional QTL mapping to reveal the dynamics of plant disease resistance.…”

Section: Introductionmentioning

confidence: 99%

Conditional QTL underlying resistance to late blight in a diploid potato population

Lindqvist‐Kreuze²,

Tian

et al. 2012

Theor Appl Genet

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A large number of quantitative trait loci (QTL) for resistance to late blight of potato have been reported with a "conventional" method in which each phenotypic trait reflects the cumulative genetic effects for the duration of the disease process. However, as genes controlling response to disease may have unique contributions with specific temporal features, it is important to consider the phenotype as dynamic. Here, using the net genetic effects evidenced at consecutive time points during disease development, we report the first conditional mapping of QTL underlying late blight resistance in potato under five environments in Peru. Six conditional QTL were mapped, one each on chromosome 2, 7 and 12 and three on chromosome 9. These QTL represent distinct contributions to the phenotypic variation at different stages of disease development. By comparison, when conventional mapping was conducted, only one QTL was detected on chromosome 9. This QTL was the same as one of the conditional QTL. The results imply that conditional QTL reflect genes that function at particular stages during the host-pathogen interaction. The dynamics revealed by conditional QTL mapping could contribute to the understanding of the molecular mechanism of late blight resistance and these QTL could be used to target genes for marker development or manipulation to improve resistance.

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

Cited by 85 publications

References 42 publications

Identification of QTL underlying the filling rate of protein at different developmental stages of soybean seed

Identification of QTL underlying the filling rate of protein at different developmental stages of soybean seed

QTL mapping for developmental behavior of plant height in wheat (Triticum aestivum L.)

Conditional QTL underlying resistance to late blight in a diploid potato population

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