scholarly journals Evaluation of Stability of Field Pea genotypes in response to Mycosphaerella Pinodes Infection

2014 ◽  
Vol 65 (1) ◽  
pp. 79-85
Author(s):  
Lech Boros
Keyword(s):  
Quantitative Traits ◽  
Mixed Model ◽  
Field Experiments ◽  
Interaction Analysis ◽  
Regression Function ◽  
Mycosphaerella Pinodes ◽  
Environment Interaction ◽  
Linear Regression Function ◽  
Genotype Environment Interaction ◽  
Main Effects

Abstract Interaction of genotypes with environment for quantitative traits among them certain disease resistance makes difficult choice of proper genotypes for breeding proposes and may affects further cultivation effects. The aim of this study was assessment of stability of reaction to Mycosphaerella pinodes infection for the set of pea genotypes in four years field experiments with vary epidemic pressure. The Sheffé-Caliński mixed model and the Caliński-Kaczmarek joint regression model for genotype-environment interaction analysis was applied. Tested pea genotypes were grouped into two categories; responding stable to M. pinodes (reacting proportionally to changed environment) and unstable ones (showing significant interaction with environment). The unstable genotypes reacted irregularly to environments (not able to describe the reaction to M. pinodes by any linear regression function). Pea genotypes PI 142441, PI 142442, PI 404221, PI 413691, cv. Radley and Bohun were characterized by high negative main effects (most resistant) for disease severity and showed stable response to M. pinodes infection. Stability of mycospharealla blight reactions was not associated with the level of resistance in the cultivars tested.

scholarly journals Key locations for soybean genotype assessment in South Brazil region

2020 ◽  
Vol 41 (3) ◽  
pp. 767
Author(s):  
Samuel Cristian Dalló ◽  
Andrei Daniel Zdziarski ◽  
Leomar Guilherme Woyann ◽  
Anderson Simionato Milioli ◽  
Rodrigo Zanella ◽  
...  
Keyword(s):  
Interaction Analysis ◽  
Block Design ◽  
Environment Interaction ◽  
Santa Barbara ◽  
Breeding Programs ◽  
Soybean Genotype ◽  
Discriminating Power ◽  
Genotype Environment Interaction ◽  
Program Costs ◽  
Main Effects

Mitigating the high costs of soybean breeding programs requires constant improvement of all the involved processes. Identifying representative and discriminating test locations, as well as excluding redundant and/or non-representative locations, makes it possible to select genotypes with more accuracy while reducing the costs of the multi-environment trials (MET). Therefore, this study had three objectives: to evaluate the representativeness and discriminating power of test locations; to identify similar test locations for each Edaphoclimatic Region (ECR) and locations that did not contribute to genotype evaluation; and to recommend the best locations for evaluating MET in order to reduce breeding program costs in the soybean macro regions 1 (M1) and 2 (M2). Grain yield (GY) data from ‘Value-for-Cultivation-and-Use’ (VCU) trials obtained during the 2012-2016 crop seasons were used, totaling 132 environments (location x year) and 43 genotypes. The experiments were arranged in a randomized complete block design with three replications. Representative and discriminant locations were identified by GGL (genotype main effects plus genotype × location interaction) + GGE (genotype main effects plus genotype × environment interaction) analysis, using GGEbiplot software. Representative and discriminant locations were identified for each ECR and can be used as core locations for breeding programs. Similarly, locations that were not representative and discriminant, or that present redundancy in the results, should be excluded from or replaced in MET. The most recommended locations for conducting VCU trials in M1 are: Cachoeira do Sul (ECR 101); Ronda Alta, Passo Fundo, Santa Bárbara do Sul, and Ciríaco (ECR 102); and Castro (ECR 103). For M2, the most suitable locations are Rolândia, Marechal Cândido Rondon, Campo Mourão, Santa Terezinha de Itaipu, Palotina, Floresta, and Londrina (ECR 201); Naviraí (ECR 202); and Ponta Porã and Maracajú (ECR 204).

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

scholarly journals Adaptive Abilities of Broad Bean (Vicia faba L.) Accession in Terms of Main Quantitative Traits

2019 ◽  
Vol 65 (4) ◽  
pp. 136-146
Author(s):  
Natalia Georgieva ◽  
Valentin Kosev
Keyword(s):  
Plant Height ◽  
Seed Weight ◽  
Quantitative Traits ◽  
Broad Bean ◽  
Environment Interaction ◽  
High Plasticity ◽  
Forage Crops ◽  
Genotype Environment Interaction ◽  
Adaptive Ability ◽  
Adaptive Abilities

Abstract The experimental activity was conducted at the Institute of Forage Crops (Pleven) during the period 2016 – 2018. The adaptive ability of 10 broad bean accessions was determined with respect to main quantitative traits based on parametric and nonparametric analysis. The environment influences to the highest degree the traits of 1st pod height, pods number and seed weight per plant. The plant height and seeds number were strongly influenced by the genotype, and the mass of 100 seeds was determined by the genotype × environment interaction. The broad bean accessions can be distributed as follows: Fb 1929 has a high value of the 1st pod height (34 cm) and is characterized by high plasticity and stability; BGE 029055 and Fb 1896 are stable and form a large number of pods per plant (11 – 15); Fb 1896 and Fb 2486 are distinguished with good adaptability and stability, increased seed weight (28.01 and 30.28 g, respectively) and 100 seeds mass (105.48 g and 91.31 g). Accessions BGE 032012 and Fb 2481 represent a selection value in terms of plant height (61.36 and 65.83 cm); Fb 1929 – in 1st pod height (32.46 cm); and BGE 029055, Fb 1896 and Fb 2486 – in pods number (10.59, 9.67 and 11.89). Fb 1896, Fb 2486 and BGE 041470 can be used to develop a new genetic diversity in breeding aimed at increasing the mass of 100 seeds and seed productivity.

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

2021 ◽  
pp. 1-13
Author(s):  
Aliya Momotaz ◽  
Per H. McCord ◽  
R. Wayne Davidson ◽  
Duli Zhao ◽  
Miguel Baltazar ◽  
...  
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Area 5 ◽  
Randomized Complete Block Design ◽  
Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

GENOTYPE-ENVIRONMENT INTERACTION OF YIELD IN CEREAL CROPS IN NORTHWESTERN CANADA

1981 ◽  
Vol 61 (2) ◽  
pp. 255-263 ◽  
Author(s):  
R. M. De PAUW ◽  
D. G. FARIS ◽  
C. J. WILLIAMS
Keyword(s):  
Triticum Aestivum L ◽  
Regression Coefficients ◽  
Cereal Crops ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Hordeum Vulgare L ◽  
Genotype Environment Interaction ◽  
Early Maturing ◽  
Frost Free Period ◽  
Main Effects

Three cultivars of each crop, wheat (Triticum aestivum L.), oats (Avena sativa L.), and barley (Hordeum vulgare L.), were grown for 4 yr at five locations north of the 55th parallel in northwestern Canada. There were highly significant differences among all main effects and interactions. Galt barley produced the highest seed yield followed by Centennial barley, Random oats and Harmon oats. Victory oats, Olli barley, Neepawa wheat and Pitic 62 wheat yielded similarly to each other while Thatcher wheat was significantly lower yielding. Mean environment yields ranged from 2080 to 5610 kg/ha. The genotype-environment (GE) interaction of species and cultivars was sufficiently complicated that it could not be characterized by one or two statistics (e.g., stability variances or regression coefficients). However, variability in frost-free period among years and locations contributed to the GE interaction because, for example, some cultivars yielded well (e.g., Pitic 62) only in those year-location environments with a relatively long frost-free period while other early maturing cultivars (e.g., Olli) performed well even in a short frost-free period environment.

Genotype × Environment Interaction in Psychopathology: Fact or Artifact?

2006 ◽  
Vol 9 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Lindon J. Eaves
Keyword(s):  
Candidate Genes ◽  
Biological Significance ◽  
Simulated Data ◽  
Threshold Value ◽  
Environment Interaction ◽  
Additive Effects ◽  
Mixture Of Distributions ◽  
Genotype Environment Interaction ◽  
Genes And Environment ◽  
Main Effects

AbstractRecent studies have claimed to detect interaction between candidate genes and specific environmental factors (Genotype × Environment interaction, G × E) in susceptibility to psychiatric disorder. The objective of the present study was to examine possible artifacts that could explain widely publicized findings. The additive effects of candidate genes and measured environment on liability to disorder were simulated under a model that allowed for mixture of distributions in liability conditional on genotype and environment. Simulated liabilities were dichotomized at a threshold value to reflect diagnosis of disorder. Multiple blocks of simulated data were analyzed by standard statistical methods to test for the main effects and interactions of genes and environment on outcome. The main outcome of this study was simulated liabilities and diagnoses of major depression and antisocial behavior. Analysis of the dichotomized data by logistic regression frequently detected significant G × E interaction even though none was present for liability. There is therefore reason to question the biological significance of published findings.

scholarly journals Temporal uniformity of the spontaneous mutational variance of quantitative traits in Drosophila melanogaster

2000 ◽  
Vol 75 (1) ◽  
pp. 47-51 ◽  
Author(s):  
AURORA GARCÍA-DORADO ◽  
JESUS FERNÁNDEZ ◽  
CARLOS LÓPEZ-FANJUL
Keyword(s):  
Drosophila Melanogaster ◽  
Morphological Traits ◽  
Quantitative Traits ◽  
Wing Length ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Bristle Number ◽  
Sib Mating ◽  
Mutational Variance ◽  
Interaction Variance

Spontaneous mutations were allowed to accumulate over 209 generations in more than 100 lines, all of them independently derived from a completely homozygous population of Drosophila melanogaster and subsequently maintained under strong inbreeding (equivalent to full-sib mating). Traits scored were: abdominal (AB) and sternopleural (ST) bristle number, wing length (WL) and egg-to-adult viability (V). On two occasions – early (generations 93–122) and late (generations 169–209) – ANOVA estimates of the mutational variance and the mutational line × generation interaction variance were obtained. Mutational heritabilities of morphological traits ranged from 2 × 10−4 to 2 × 10−3 and the mutational coefficient of variation of viability was 0·01. For AB, WL and V, temporal uniformity of the mutational variance was observed. However, a fluctuation of the mutational heritability of ST was detected and could be ascribed to random genotype × environment interaction.

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