Genotype by Environment Interaction Effects on Starch Content and Digestibility in Potato (Solanum tuberosum L.)

2013 ◽  
Vol 61 (16) ◽  
pp. 3941-3948 ◽  
Author(s):  
Stephanie Bach ◽  
Rickey Y. Yada ◽  
Benoit Bizimungu ◽  
Ming Fan ◽  
J. Alan Sullivan
Keyword(s):  
Solanum Tuberosum ◽  
Solanum Tuberosum L ◽  
Starch Content ◽  
Genotype By Environment Interaction ◽  
Interaction Effects ◽  
Environment Interaction ◽  
Genotype By Environment

Genotype by environment interaction effects on yield and curcumin in turmeric (Curcuma longa L.)

2014 ◽  
Vol 53 ◽  
pp. 358-364 ◽  
Author(s):  
M. Anandaraj ◽  
D. Prasath ◽  
K. Kandiannan ◽  
T. John Zachariah ◽  
V. Srinivasan ◽  
...  
Keyword(s):  
Curcuma Longa ◽  
Genotype By Environment Interaction ◽  
Interaction Effects ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops

2009 ◽  
Vol 91 (3) ◽  
pp. 193-207 ◽  
Author(s):  
A. M. BAUER ◽  
F. HOTI ◽  
T. C. REETZ ◽  
W.-D. SCHUH ◽  
J. LÉON ◽  
...  
Keyword(s):  
Gibbs Sampling ◽  
Bayesian Model ◽  
Genotype By Environment Interaction ◽  
Interaction Effects ◽  
Relationship Matrix ◽  
Environment Interaction ◽  
Breeding Values ◽  
Genotype By Environment ◽  
Frequentist Approach ◽  
High Degree

SummaryIn self-pollinating populations, individuals are characterized by a high degree of inbreeding. Additionally, phenotypic observations are highly influenced by genotype-by-environment interaction effects. Usually, Bayesian approaches to predict breeding values (in self-pollinating crops) omit genotype-by-environment interactions in the statistical model, which may result in biased estimates. In our study, a Bayesian Gibbs sampling algorithm was developed that is adapted to the high degree of inbreeding in self-pollinated crops and accounts for interaction effects between genotype and environment. As related lines are supposed to show similar genotype-by-environment interaction effects, an extended genetic relationship matrix is included in the Bayesian model. Additionally, since the coefficient matrix C in the mixed model equations can be characterized by rank deficiencies, the pseudoinverse of C was calculated by using the nullspace, which resulted in a faster computation time. In this study, field data of spring barley lines and data of a ‘virtual’ parental population of self-pollinating crops, generated by computer simulation, were used. For comparison, additional breeding values were predicted by a frequentist approach. In general, standard Bayesian Gibbs sampling and a frequentist approach resulted in similar estimates if heritability of the regarded trait was high. For low heritable traits, the modified Bayesian model, accounting for relatedness between lines in genotype-by-environment interaction, was superior to the standard model.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

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