Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population

2015 ◽  
Vol 291 (1) ◽  
pp. 437-454 ◽  
Author(s):  
Zhanhui Zhang ◽  
Xiangyuan Wu ◽  
Chaonan Shi ◽  
Rongna Wang ◽  
Shengfei Li ◽  
...  
2018 ◽  
Vol 36 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoxiao Sun ◽  
Tongtong Liu ◽  
Tangyuan Ning ◽  
Kai Liu ◽  
Xixian Duan ◽  
...  

2016 ◽  
Vol 155 (4) ◽  
pp. 544-555 ◽  
Author(s):  
Z. Y. DENG ◽  
W. J. LI ◽  
F. CHEN ◽  
W. Q. FANG ◽  
G. F. CHEN ◽  
...  

SUMMARYFlour whiteness (FW) is an important factor in assessing flour quality and determining the end product quality. It is an integrated sensory indicator reflecting flour colour and is negatively correlated with protein content. In order to dissect the genetic relationship between FW and its five related traits at the quantitative trait locus (QTL)/gene level, a recombinant inbred line population was evaluated under three environments. Quantitative trait loci for FW were analysed by unconditional and conditional QTL mapping. Four unconditional additive QTLs and 16 conditional additive QTLs were detected across the three environments. Of these QTLs, only one major additive QTL (Qfw1D1-1) was consistently identified using both unconditional and conditional QTL analysis. This QTL was independent of flour colour a* (a function of red-green with a positive a* for redness and negative for greenness) and b* (a green-blue value with positive value for yellowness and negative for blueness) and was only slightly affected by flour protein content. A minor additive QTL (Qfw4A-4) was also detected using these two QTL mapping methods, being independent of flour colour a* and b*. Five unconditional and ten conditional epistatic minor QTLs were detected, from which only one pair (Qfw3A-10/Qfw6B-6) was identified by both unconditional and conditional QTL mapping, also independent of flour colour a* and b*. The major QTL (Qfw1D1-1) identified in the current study for the first time can be used for improving wheat FW in marker-assisted breeding.


Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 1885-1895
Author(s):  
J P Hua ◽  
Y Z Xing ◽  
C G Xu ◽  
X L Sun ◽  
S B Yu ◽  
...  

Abstract We introduced an experimental design that produced an “immortalized F2” population allowing for complete dissection of genetic components underlying quantitative traits. Data for yield and three component traits of the immortalized F2 were collected from replicated field trials over 2 years. Using 231 marker loci, we resolved the genetic effects into individual components and assessed relative performance of all the genotypes at both single- and two-locus levels. Single-locus analysis detected 40 QTL for the four traits. Dominance effects for about one-half of the QTL were negative, resulting in little “net” positive dominance effect. Correlation between genotype heterozygosity and trait performance was low. Large numbers of digenic interactions, including AA, AD, and DD, were detected for all the traits, with AA as the most prevalent interaction. Complementary two-locus homozygotes frequently performed the best among the nine genotypes of many two-locus combinations. While cumulative small advantages over two-locus combinations may partly explain the genetic basis of heterosis of the hybrid as double heterozygotes frequently demonstrated marginal advantages, double heterozygotes were never the best genotypes in any of the two-locus combinations. It was concluded that heterozygotes were not necessarily advantageous for trait performance even among genotypes derived from such a highly heterotic hybrid.


Author(s):  
Jichun Tian ◽  
Jiansheng Chen ◽  
Guangfeng Chen ◽  
Peng Wu ◽  
Han Zhang ◽  
...  

Author(s):  
Jichun Tian ◽  
Jiansheng Chen ◽  
Guangfeng Chen ◽  
Peng Wu ◽  
Han Zhang ◽  
...  

2016 ◽  
Vol 4 (3) ◽  
pp. 220-228 ◽  
Author(s):  
Han Zhang ◽  
Jiansheng Chen ◽  
Ruyu Li ◽  
Zhiying Deng ◽  
Kunpu Zhang ◽  
...  

2009 ◽  
Vol 120 (2) ◽  
pp. 333-340 ◽  
Author(s):  
Jihua Tang ◽  
Jianbing Yan ◽  
Xiqing Ma ◽  
Wentao Teng ◽  
Weiren Wu ◽  
...  

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