scholarly journals MULTIPLICATIVE GENOTYPE-ENVIRONMENT INTERACTION AS A CAUSE OF REVERSED RESPONSE TO DIRECTIONAL SELECTION

Genetics ◽  
1986 ◽  
Vol 114 (1) ◽  
pp. 333-343
Author(s):  
A Gimelfarb
Keyword(s):  
Sudden Change ◽  
Phenotypic Selection ◽  
Directional Selection ◽  
Quantitative Character ◽  
Environment Interaction ◽  
Response To Selection ◽  
Random Drift ◽  
Genotype Environment Interaction ◽  
Possible Cause ◽  
Selection Of

ABSTRACT In experiments with directional selection on a quantitative character a "reversed response" to selection is occasionally observed, when selection of individuals for a higher (lower) value of the character results in a lower (higher) value of the character among their offspring. A sudden change in environments or random drift is often assumed to be responsible for this. It is demonstrated in this paper that these two causes cannot account for the reversed response at least in some of the experiments. Multiplicative genotype-environment interaction is discussed as a possible cause of a reversed response to directional selection. Such interaction entails either disruptive or stabilizing genotypic selection, even when the phenotypic selection is directional.

AN INVESTIGATION OF GENOTYPE × ENVIRONMENT INTERACTION IN RELATION TO A SELECTION EXPERIMENT IN TRIBOLIUM CASTANEUM

1971 ◽  
Vol 13 (1) ◽  
pp. 144-154 ◽  
Author(s):  
G. W. Friars ◽  
B. N. Nayak ◽  
P. Y. Jui ◽  
B. L. Raktoe
Keyword(s):  
Tribolium Castaneum ◽  
Environment Interaction ◽  
Selection Experiment ◽  
Response To Selection ◽  
Slight Tendency ◽  
Offspring Number ◽  
Genotype Environment Interaction ◽  
Reverse Trend ◽  
Selection For ◽  
Selection Of

An experiment involving the selection of certain lines of Tribolium castaneum for larva weight and others for offspring number in both wet and dry environments was carried out over nine generations. No apparent interactions of environment-selected-in × environment-tested-in was found. That is, the environment-selected-in did not condition response to selection for larva weight, although some slight tendency toward this type of effect was noted for offspring number. However, lines selected for high larva weight showed greater response over controls when tested in the wet as opposed to the dry environment.Crosses of the respective lines indicated that the hatchability of lines selected for larva weight was elevated when they were shifted from the dry to the wet environment. However, the reverse trend was apparent for the control lines. A cross of lines selected for offspring number in wet and dry environments respectively showed repeated superiority in hatchability over other lines and crosses to which it was compared.

scholarly journals Quantitative genetic variability maintained by mutation-stabilizing selection balance: sampling variation and response to subsequent directional selection

1989 ◽  
Vol 54 (1) ◽  
pp. 45-58 ◽  
Author(s):  
Peter D. Keightley ◽  
William G. Hill
Keyword(s):  
Genetic Variance ◽  
Directional Selection ◽  
Quantitative Character ◽  
Stabilizing Selection ◽  
Effective Population ◽  
Selection Responses ◽  
Quantitative Genetic ◽  
Before And After ◽  
Cage Population ◽  
Selection Of

SummaryA model of genetic variation of a quantitative character subject to the simultaneous effects of mutation, selection and drift is investigated. Predictions are obtained for the variance of the genetic variance among independent lines at equilibrium with stabilizing selection. These indicate that the coefficient of variation of the genetic variance among lines is relatively insensitive to the strength of stabilizing selection on the character. The effects on the genetic variance of a change of mode of selection from stabilizing to directional selection are investigated. This is intended to model directional selection of a character in a sample of individuals from a natural or long-established cage population. The pattern of change of variance from directional selection is strongly influenced by the strengths of selection at individual loci in relation to effective population size before and after the change of regime. Patterns of change of variance and selection responses from Monte Carlo simulation are compared to selection responses observed in experiments. These indicate that changes in variance with directional selection are not very different from those due to drift alone in the experiments, and do not necessarily give information on the presence of stabilizing selection or its strength.

scholarly journals Genotype-environment interaction and the number of test sites for the genetic improvement of rubber trees (Hevea) in São Paulo State, Brazil

2000 ◽  
Vol 23 (1) ◽  
pp. 179-187 ◽  
Author(s):  
Reginaldo Brito da Costa ◽  
Marcos Deon Vilela de Resende ◽  
Antonio José de Araujo ◽  
Paulo de Souza Gonçalves ◽  
Antonio Lúcio Mello Martins
Keyword(s):  
Genetic Correlation ◽  
Sao Paulo ◽  
Environment Interaction ◽  
São Paulo ◽  
Paulo State ◽  
Genotype Environment Interaction ◽  
Ideal Number ◽  
São Paulo State ◽  
Efficiency Of Selection ◽  
Selection Of

The present study quantifies the possible genotype-environment interactions and determines the ideal number of test sites for rubber trees [Hevea brasiliensis (Willd ex Adr. de Juss.) Muell Arg] in the plateau region of São Paulo State. The study was based on the genetic correlation among progenies at three different sites and on estimates of genetic gains with indirect selection of rubber trees. Twenty-two half-sib progenies were planted at the Jaú, Pindorama and Votuporanga experimental stations using random blocks with five replications and 10 plants per plot. At three years of age, the plants were evaluated for their total number of latex ring vessels (NR), rubber production (RP), bark thickness (BT) and girth (SG). There was significant genetic variability in the characters RP, SG and BT, mainly among progenies from Pindorama and Votuporanga. The effects of genotype-site interactions were significant for RP and SG. The finding of significant interactions was not a complicating factor because of the large genetic correlation detected. These results indicate that the use of two sites is more profitable when the gains in efficiency of selection are greater than 10%. Thus, Pindorama and Votuporanga will satisfactorily attend the studied region.

Estimation of sire with feeding regime interaction in pigs

1987 ◽  
Vol 1987 ◽  
pp. 20-20
Author(s):  
N.D. Cameron ◽  
M.K. Curran
Keyword(s):  
Genetic Improvement ◽  
Environment Interaction ◽  
Restricted Feeding ◽  
Production Traits ◽  
Commercial Unit ◽  
Feeding Regime ◽  
Genotype Environment Interaction ◽  
Commercially Important ◽  
Ad Libitum ◽  
Selection Of

Genetic improvement in commercially important traits of pigs produced under commercial conditions is generally achieved through selection of parents which are performance tested under special conditions. If pigs are performance tested in one environment and progeny tested in another environment, then the same animals may not be selected on the basis of the two tests, which would result in a genotype-environment interaction. The testing station and commercial unit often have feeding regime (ad-libitum, restricted or both) confounded with environment. This study estimated the genetic correlation between ad-libitum and restricted feeding in one environment for production traits to traits to determine the contribution 6T the genotype-feeding regime interaction to the genotype-environment interaction.

Selection of environments using simultaneous clustering based on genotype × environment interaction

1994 ◽  
Vol 74 (2) ◽  
pp. 311-317 ◽  
Author(s):  
C. P. Baril ◽  
J-B. Denis ◽  
P. Brabant
Keyword(s):  
Cluster Analysis ◽  
Wheat Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Yield Data ◽  
Cluster Procedure ◽  
Genotype Environment Interaction ◽  
Series Of Experiments ◽  
The Common ◽  
Selection Of

Cluster analysis is used to classify genotypes and environments to decompose and interpret genotype × environment (GE) interaction. A simultaneous clustering method is applied to wheat-yield data collected over 8 yr in seven locations, with two agronomic treatments per location. This approach evidenced redundancies among the used environments constituting the Institut National de la Recherche Agronomique series of experiments in northern France. The aim is to reduce the number of environments without losing GE interaction. A graphical method based on the decreasing mean square of GE interaction is proposed to provide a cutting criterion of the cluster procedure. The comparison of groupings made independently for successive years suggested the removal of some environments, hence providing rational savings in the breeding program. Lastly, the simultaneous two-way clustering procedure is compared with the common one-way clustering procedure. Key words: Cluster analysis, genotype × environment interaction, pattern analysis, series of experiments, wheat

scholarly journals EFFICIENCY OF GRAIN FORMATION PRODUCTIVITY USE OF PISUM SATIVUM L. SELECTION

2018 ◽  
Vol 13 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Александра Фадеева ◽  
Aleksandra Fadeeva ◽  
Ксения Шурхаева ◽  
Kseniya Shurhaeva
Keyword(s):  
Seed Production ◽  
Environmental Conditions ◽  
Environment Interaction ◽  
Seed Productivity ◽  
Seed Formation ◽  
Genotype Environment Interaction ◽  
Grain Formation ◽  
Hereditary Character ◽  
Genotypic Characteristics ◽  
Selection Of

The results of investigations on revealing the features of the structure formation of seed productivity in new genotypes of peas with non-permeable beans are presented. Selection of Kaban, Fregat, Veles varieties and sample KT-6489 with a new trait was aimed at increasing the number of beans and seeds per plant. The increase in seed productivity was accompanied by a decrease in the mass of 1000 seeds and an increase in the performance of beans. Differences in the variability of the examined signs of seed productivity testify to the genotypic characteristics of the reaction to the effect of environmental conditions. The selection value of the “productivity of seed formation” and its hereditary character is grounded. The use of sources with a high seed-forming ability in breeding has contributed to an increase in the proportion of seeds from planted ovules on the plant. In the varieties with soft beans, the values of this feature, depending on environmental conditions, varied within the range 54.4-70.9%. In new breeding developments, these figures were significantly increased to 81.8-91.0%. Statistical analysis proved the reliability and significance of genotypic differentiation of the trait in changing environmental conditions. A high significant effect of the genotype with a share of 83.7% on the variability of seed production was established. The influence of the environment and the interaction of the factors “genotype x environment” on the trait was assessed by a weak impact (3.61 and 3.94%). The presence of stable genotypes in the study group results from the revealed advantage of variance of genotypes over the variance of the “genotype-environment” interaction. A sample of KT-6489 with a weak variability in seed production (1.6%) with variations by years 84.0-86.7% was isolated.

scholarly journals AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

2018 ◽  
Vol 55 (2) ◽  
pp. 97-121 ◽  
Author(s):  
Anderson Cristiano Neisse ◽  
Jhessica Letícia Kirch ◽  
Kuang Hongyu
Keyword(s):  
Cluster Analysis ◽  
Wheat Yield ◽  
Hierarchical Cluster ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Spring Bread Wheat ◽  
Genotype Environment Interaction ◽  
Complex Process ◽  
Yield Trial ◽  
Selection Of

SummaryThe presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn’t scale well beyond two-dimensional plots, which happens whenever the first two components don’t capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data’s evaluation, permitting increased reliability in the selection of superior cultivars and test environments.

scholarly journals AMMI and Stability Analyses of Bunch Mass in Multilocational Testing of Musa Germplasm in Sub-Saharan Africa

1998 ◽  
Vol 123 (4) ◽  
pp. 623-627 ◽  
Author(s):  
Rodomiro Ortiz
Keyword(s):  
Environmental Changes ◽  
Sub Saharan Africa ◽  
Environment Interaction ◽  
Stability Analyses ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Sub Saharan ◽  
Main Effects ◽  
New Cultivars ◽  
Selection Of

There is a genuine need within a plantain and banana (Musa spp.) breeding program to assess thoroughly the experimental materials through a sequence of trials. This will result in the selection of promising clones as potential new cultivars in the targeted agroecozone. Stability analyses and the additive main effects and multiplicative interaction (AMMI) model provide together a means for the identification of clones with 1) homeostatic responses to environmental changes, 2) a genotypic response to environmental changes, and 3) adaptation to specific niches. Fourteen polyploid clones (10 tetraploid hybrids and 4 triploid cultivars) were evaluated in a broad range of environments in sub-Saharan Africa to determine the value of stability and AMMI analyses in Musa trials. The interpretation of the results, especially those concerning the genotype × environment interaction, was facilitated by the combination of stability and AMMI analyses. Tetraploid hybrids combining heavy and stable bunch mass were identified. The results also suggested that a clone should be assessed in the ratoon cycle because plantain and banana are perennial crops. Likewise, high yielding clones with specific adaptation should be selected in environments showing the respective environmental or biotic stress.

Genotype × environment interaction and selection of maize (Zea mays L.) hybrids across moisture regimes

2021 ◽  
Vol 270 ◽  
pp. 108224
Author(s):  
Ashok Singamsetti ◽  
J.P. Shahi ◽  
P.H. Zaidi ◽  
K. Seetharam ◽  
M.T. Vinayan ◽  
...  
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Moisture Regimes ◽  
Selection Of

Interpreting genotype×environment interaction in sesame (Sesamum indicum L.)

2006 ◽  
Vol 145 (3) ◽  
pp. 263-271 ◽  
Author(s):  
H. LAURENTIN ◽  
D. MONTILLA ◽  
V. GARCIA
Keyword(s):  
Yield Components ◽  
Principal Component ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Silt Content ◽  
Genotype Environment Interaction ◽  
Principal Component Axis ◽  
Superior Genotypes ◽  
Edaphic Properties ◽  
Selection Of

An understanding of genotype by environment (G×E) interaction would be useful for establishing breeding objectives, identifying the best test conditions, and finding areas of optimal cultivar adaptation. Data from field assays including eight environments and eight elite lines were analysed to identify environmental and genotypic variables related with G×E interaction for yield in sesame multi-environment trials in Venezuela. Both predictable and unpredictable environmental variables were recorded. Yield components were recorded as genotypic variables. Yield and yield components were used to perform additive main effect and multiplicative interaction (AMMI) analysis. Significant differences (P<0·01) for G×E interaction were observed for all variables examined, except for the number of branches per plant. For yield, 0·28 of the total sum of squares corresponded to G×E interaction. Using environmental and genotypic data, correlation analysis was carried out between genotypic and environmental scores of the first interaction principal component axis (IPCA 1) for all variables examined. Significant correlations (P<0·05) were observed between IPCA 1 for yield and content of sand and silt in soil. No significant correlation was found between IPCA 1 score for yield and genotypic variables. These results indicate that edaphic properties at the trial locations play an important role in yield G×E interaction in Venezuelan sesame. These results should help select test sites for sesame in Venezuela to minimize G×E interaction and make selection of superior genotypes easier. Two strategies can be recommended: multi-environment trials at sites with average, not extreme, sand and silt content, or stratification of sites according to sand and silt content.

Sign in / Sign up

Export Citation Format

Share Document