scholarly journals Quantitative trait loci analysis for seed yield and component traits in sunflower

2014 ◽  
Vol 13 (6) ◽  
pp. 754-761 ◽  
Author(s):  
Vanitha J. ◽  
Manivannan N. ◽  
Chandirakala R.
Keyword(s):  
Quantitative Trait Loci ◽  
Seed Yield ◽  
Quantitative Trait ◽  
Trait Loci ◽  
Component Traits

scholarly journals In silico integration of quantitative trait loci for seed yield and yield-related traits in Brassica napus

2013 ◽  
Vol 33 (4) ◽  
pp. 881-894 ◽  
Author(s):  
Qing-Hong Zhou ◽  
Dong-Hui Fu ◽  
Annaliese S. Mason ◽  
Yong-Jun Zeng ◽  
Chao-Xian Zhao ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Quantitative Trait Loci ◽  
Seed Yield ◽  
Quantitative Trait ◽  
In Silico ◽  
Trait Loci

Identification of quantitative trait loci underlying seed shape in soybean across multiple environments

2017 ◽  
Vol 156 (1) ◽  
pp. 3-12 ◽  
Author(s):  
W. L. Teng ◽  
M. N. Sui ◽  
W. Li ◽  
D. P. Wu ◽  
X. Zhao ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Average Distance ◽  
Recombinant Inbred Lines ◽  
Soybean Seeds ◽  
Seed Shape ◽  
Phenotypic Data ◽  
Trait Loci ◽  
Component Traits ◽  
Seed Width

AbstractSeed shape (SS) affects the yield and appearance of soybean seeds significantly. However, little detailed information has been reported about the quantitative trait loci (QTL) affecting SS, especially SS components such as seed length (SL), seed width (SW) and seed thickness (ST), and their mutual ratios of length-to-weight (SLW), length-to-thickness (SLT) and weight-to-thickness (SWT). The aim of the present study was to identify QTL underlying SS components using 129 recombinant inbred lines derived from a cross between Dongnong46 and L-100. Phenotypic data were collected from this population after it was grown across nine environments. A total of 213 simple sequence repeat markers were used to construct the genetic linkage map, which covered approximately 3623·39 cM, with an average distance of 17·01 cM between markers. Five QTL were identified as being associated with SL, five with SW, three with ST, four with SLW, two with SLT and three with SWT. These QTL could explain 1·46–22·16% of the phenotypic variation in SS component traits. Three QTL were identified in more than six tested environments three for SL, two for SW, one for ST, two for SLW and one for SLT. These QTL have great potential value for marker-assistant selection of SS in soybean seeds.

scholarly journals Overdominance and Epistasis Are Important for the Genetic Basis of Heterosis in Brassica rapa

HortScience ◽  
2007 ◽  
Vol 42 (5) ◽  
pp. 1207-1211 ◽  
Author(s):  
De-Kun Dong ◽  
Jia-Shu Cao ◽  
Kai Shi ◽  
Le-Cheng Liu
Keyword(s):  
Quantitative Trait Loci ◽  
Brassica Rapa ◽  
Quantitative Trait ◽  
Genetic Basis ◽  
Phenotypic Variance ◽  
Epistasis Analysis ◽  
Main Effect ◽  
Trait Loci ◽  
Component Traits ◽  
The Cross

To investigate the genetic basis of heterosis in Brassica rapa, an F2 population was produced from the cross of B. rapa L. subsp. chinensis (L.) Hanelt and B. rapa L. subsp. rapifera Metzg. Trait performances of the F1 hybrid showed evident mid parent heterosis, which varied from 18.55% to 101.62% for the 11 traits investigated. A total of 23 main effect quantitative trait loci (QTLs) were detected for biomass and its component traits, which could explain 4.38% to 47.80% of the phenotypic variance, respectively. Sixty-five percent of these QTLs showed obvious overdominance. Epistasis analysis detected 444 two-locus interactions for the 11 traits at the threshold of P < 0.005. Some of them remained significant when more stringent threshold were set. These results suggested that overdominance and epistasis might play an important role as the genetic basis of heterosis in B. rapa.

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