Character Association Analysis for Grain Iron and Zinc Concentrations and Grain Yield Components in Rice Genotypes

2017 ◽  
Vol 5 (4) ◽  
pp. 940-945
Author(s):  
Sriram Ajmera ◽  
Genetics ◽  
1997 ◽  
Vol 145 (2) ◽  
pp. 453-465 ◽  
Author(s):  
Zhikang Li ◽  
Shannon R M Pinson ◽  
William D Park ◽  
Andrew H Paterson ◽  
James W Stansel

The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F4 progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Teqing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have “main” effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.


2014 ◽  
Vol 7 (1) ◽  
pp. 19-33 ◽  
Author(s):  
D. Ruswandi ◽  
J. Supriatna ◽  
A.T. Makkulawu ◽  
B. Waluyo ◽  
H. Marta ◽  
...  

2019 ◽  
Vol 132 (9) ◽  
pp. 2707-2719 ◽  
Author(s):  
Kai P. Voss-Fels ◽  
Gabriel Keeble-Gagnère ◽  
Lee T. Hickey ◽  
Josquin Tibbits ◽  
Sergej Nagornyy ◽  
...  

2011 ◽  
Vol 48 (No. 5) ◽  
pp. 230-235
Author(s):  
M. Sabo ◽  
M. Bede ◽  
Ž.U. Hardi

Variability of grain yield components of some new winter wheat genotypes (e.g. Lara, Lenta, Kruna, Fiesta, Perla, and one line of AG-45) was examined. The analysis of grain yield components of these genotypes and the line was undertaken in a two-year research (1997/1998 and 1998/1999) at two different locations. Significant differences among genotypes, locations and research years were established. In the first experimental year (1997/1998) there was a high positive correlation between nearly all components of the grain yield. The most significant correlation was found between the grain number per spike and grain yield. In the second experimental year (1998/1999) the components did not show statistically significant correlation with the grain yield. It seems that the grain yield of examined genotypes depended significantly on the grain number per spike, grain mass per spike, and agroecological conditions during the vegetation period, whereby the potential yield was determined by the interaction among genotypes, location and production year. The biggest differences among examined genotypes of winter wheat were found in the stem height and spike length.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiwei Yang ◽  
Zonghua Liu ◽  
Qiong Chen ◽  
Yanzhi Qu ◽  
Jihua Tang ◽  
...  

2012 ◽  
Vol 4 (2) ◽  
pp. 124-127 ◽  
Author(s):  
Ghaffar KIANI ◽  
Ghorbanali NEMATZADEH

This study performed to determine the association between grain yield and yield components in fifty-four selected rice genotypes at F2 populations. Results showed that traits, the panicles per plant (r = 0.751) and filled grains per panicle (r = 0.458) correlated significantly with grain yield, while grain yield was negatively associated with non-filled grains per panicle (-0.297). Path coefficient analysis revealed that grain yield was associated with panicles per plant and filled grains per panicle with the direct effects of 0.691 and 0.568, respectively. The greatest indirect effect belonged to panicle length (0.301) through filled grains per panicle. Stepwise regression analysis showed that 72.1 percent of yield variation could be explained by three characters: the panicles per plant, filled grains per panicle and panicle length. Information obtained in this study revealed that traits, the panicles per plant and filled grains per panicle, could be used as selection criteria for grain yield improvement at segregating populations of rice.


2014 ◽  
Vol 10 (2) ◽  
pp. 83-94 ◽  
Author(s):  
N Pratap ◽  
PK Singh ◽  
R Shekhar ◽  
SK Soni ◽  
AK Mall

One hundred high yielding rice genotypes were evaluated to determine character association, variability and diversity for grain yield, yield components and quality characters. High estimates of heritability, genetic advance, genotypic and phenotypic coefficients of variation were recorded for panicle hill-1, flag leaf area and grain yield hill-1. Majority of the traits showed significant and positive associations between yield and yield components like biological yield hill-1 followed by harvest-index as most important traits which need due consideration at the time of devising selection strategy. Thus, presence of several contrasting types of inter-relationships simultaneously would bring improvement in others due to correlated responses. Path analysis might have resulted into cancellation of contrasting associations by each other which ultimately lead to lowering of the net impact. This suggested that selection would be quite efficient in improving yield and yield components in context of germplasm evaluated. The crossing between superior genotypes of above diverse cluster pairs may provide desirable transgressive segregants for developing high yielding varieties of aromatic and non-aromatic rice. Thus, hybridization of Swarna of cluster XI with promising genotypes of cluster VI (Narendra 118, Vandana, Narendra 1, Akashi and Narendra 97) is recommended. DOI: http://dx.doi.org/10.3329/sja.v10i2.18326 SAARC J. Agri., 10(2): 83-94 (2012)


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiwei Yang ◽  
Zonghua Liu ◽  
Qiong Chen ◽  
Yanzhi Qu ◽  
Jihua Tang ◽  
...  

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