scholarly journals Mathematical Ability and Socio-Economic Background: IRT Modeling to Estimate Genotype by Environment Interaction

2017 ◽  
Vol 20 (6) ◽  
pp. 511-520 ◽  
Author(s):  
Inga Schwabe ◽  
Dorret I. Boomsma ◽  
Stéphanie M. van den Berg
Keyword(s):  
Individual Differences ◽  
Genetic Model ◽  
Environmental Influences ◽  
Genotype By Environment Interaction ◽  
Mathematical Ability ◽  
Environment Interaction ◽  
Environmental Variance ◽  
Behavioral Traits ◽  
Genotype By Environment ◽  
Sum Scores

Genotype by environment interaction in behavioral traits may be assessed by estimating the proportion of variance that is explained by genetic and environmental influences conditional on a measured moderating variable, such as a known environmental exposure. Behavioral traits of interest are often measured by questionnaires and analyzed as sum scores on the items. However, statistical results on genotype by environment interaction based on sum scores can be biased due to the properties of a scale. This article presents a method that makes it possible to analyze the actually observed (phenotypic) item data rather than a sum score by simultaneously estimating the genetic model and an item response theory (IRT) model. In the proposed model, the estimation of genotype by environment interaction is based on an alternative parametrization that is uniquely identified and therefore to be preferred over standard parametrizations. A simulation study shows good performance of our method compared to analyzing sum scores in terms of bias. Next, we analyzed data of 2,110 12-year-old Dutch twin pairs on mathematical ability. Genetic models were evaluated and genetic and environmental variance components estimated as a function of a family's socio-economic status (SES). Results suggested that common environmental influences are less important in creating individual differences in mathematical ability in families with a high SES than in creating individual differences in mathematical ability in twin pairs with a low or average SES.

Population Genetics of Complex Traits

2020 ◽  
pp. 225-262
Author(s):  
Daniel L. Hartl
Keyword(s):  
Complex Traits ◽  
Response Selection ◽  
Genotype By Environment Interaction ◽  
Prediction Equation ◽  
Correlated Response ◽  
Environment Interaction ◽  
Environmental Variance ◽  
Narrow Sense Heritability ◽  
Genotype By Environment ◽  
Basic Concepts

Chapter 8’s focus is on the genetic architecture of complex traits determined jointly by multiple genes and environmental factors. Sometimes called quantitative genetics, the basic concepts include components of genetic and environmental variance, genotype-by-environment interaction, genotype-by-environment association, correlation between relatives, and broad-sense and narrow-sense heritability. It distinguishes between physiological and statistical epistasis, and it shows why the former can be large while the latter may be negligible. Various types of artificial selection are considered, and individual truncation selection is examined in detail, culminating in the famous prediction equation R = h 2 S. Special topics include genomic selection, correlated response, selection limits, and the heritability of liability of threshold traits.

Genotype by environment interactions in lamb weight and carcass composition traits

2002 ◽  
Vol 75 (1) ◽  
pp. 3-14 ◽  
Author(s):  
N. Maniatis ◽  
G. E. Pollott
Keyword(s):  
Variance Components ◽  
Mixed Model ◽  
Genotype By Environment Interaction ◽  
Carcass Composition ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Additive Effects ◽  
Environmental Variance ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment

AbstractThe systematic use of the same genotype in several different environments provides information that can be used to estimate genotype by environment interaction (G ✕ E) variances and parameters. Data from the UK Suffolk Sire Referencing Scheme Ltd were used to investigate a range of sire and dam by environment interactions in lamb weight (at 8 weeks and scanning) and body composition traits (muscle and fat depth). These interactions were calculated in a DFREML mixed model containing direct additive, maternal additive, maternal environmental random variance components and the covariance between direct and maternal additive effects. Sire interactions with year, flock and flock-year and dam effects within and between litters were investigated. The addition of all G ✕ E (co)variance components resulted in an improved fit of the model for all traits. Sire interactions accounted for between 2 and 3% of the phenotypic variance in all traits, usually at the expense of both additive effects. Maternal litter environmental variance components ranged from 10% (fat depth and muscle depth) to 20% (8-week weight) of phenotypic variance. Most of this variation was found in the residual component of variance when the term was omitted from the model. When fitting sire G✕ E components in a model the covariance between direct and maternal additive genetic effects, as a proportion of phenotypic variance, was reduced to a low level (from –0·36 to –0·08 for 8-week weight). Genotype by environment interactions form a significant source of variation in lamb growth and composition traits and reduce the high negative correlation between additive effects found previously in these traits.

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).

Exploring genotype‐by‐environment interaction in sweet sorghum under tropical environments

2021 ◽  
Author(s):  
Vander Fillipe Souza ◽  
Pedro César de Oliveira Ribeiro ◽  
Indalécio Cunha Vieira Júnior ◽  
Isadora Cristina Martins Oliveira ◽  
Cynthia Maria Borges Damasceno ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Tropical Environments

Genotype by environment interaction on phenology and synchronous maturity of mungbean

2021 ◽  
Author(s):  
Siti Marwiyah ◽  
Willy Bayuardi Suwarno ◽  
Desta Wirnas ◽  
Trikoesoemaningtyas xxx ◽  
Surjono Hadi Sutjahjo
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment

scholarly journals Genotype and environment interaction of sweetpotato varieties

2019 ◽  
Vol 44 (3) ◽  
pp. 501-512
Author(s):  
S Sultana ◽  
HC Mohanta ◽  
Z Alam ◽  
S Naznin ◽  
S Begum
Keyword(s):  
Tuber Yield ◽  
Agricultural Research ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
Environment Interaction ◽  
Dry Matter Content ◽  
Genotype By Environment ◽  
Discriminating Power ◽  
Study Results ◽  
Main Effect

The article presents results of additive main effect and multiplicative interaction (AMMI) and genotype (G) main effect and genotype by environment (GE) interaction (G × GE) biplot analysis of a multi environmental trial (MET) data of 15 sweetpotato varieties released from Bangladesh Agricultural Research Institute conducted during 2015–2018. The objective of this study was to determine the effects of genotype, environment and their interaction on tuber yield and to identify stable sweetpotato genotypes over the years. The experimental layout was a randomized complete block design with three replications at Gazipur location. Combined analysis of variance (ANOVA) indicated that the main effects due to genotypes, environments and genotype by environment interaction were highly significant. The contribution of genotypes, environments and genotype by environment interaction to the total variation in tuber yield was about 60.16, 10.72 and 12.82%, respectively. The first two principal components obtained by singular value decomposition of the centred data of yield accounted for 100% of the total variability caused by G × GE. Out of these variations, PC1 and PC2 accounted for 71.5% and 28.5% of variability, respectively. The study results identified BARI Mistialu- 5, BARI Mistialu- 14 and BARI Mistialu- 15 as the closest to the “ideal” genotype in terms of yield potential and stability. Varieties ‘BARI Mistialu- 8, BARI Mistialu- 11 and BARI Mistialu- 12’ were also selected as superior genotypes. BARI Mistialu- 3 and BARI Mistialu- 13 was comparatively low yielder but was stable over the environment. Among them BARI Mistialu-12, BARI Mistialu-14 and BARI Mistialu-15 are rich in nutrient content while BARI Mistialu-8 and BARI Mistialu-11 are the best with dry matter content and organoleptic taste. Environments representing in 1st and 3rd year with comparatively short vectors had a low discriminating power and environment in 2nd year was characterized by a high discriminating power. Bangladesh J. Agril. Res. 44(3): 501-512, September 2019

EFFECTS OF GROWTH INHIBITION BY HYDROCORTISONE ACETATE ON VARIANCE IN CHICKENS

1970 ◽  
Vol 12 (3) ◽  
pp. 627-634
Author(s):  
J. S. Gavora ◽  
G. C. Hodgson
Keyword(s):  
Body Weight ◽  
External Environment ◽  
Hormonal Treatment ◽  
Genotype By Environment Interaction ◽  
Future Research ◽  
Environment Interaction ◽  
Internal Environment ◽  
Hydrocortisone Acetate ◽  
Genotype By Environment ◽  
Treatment Interaction

Traditionally genotype by environment interaction studies have dealt with changes in external environment. In this experiment an attempt was made to alter internal environment and keep external environment constant. Cockerels from each of six different commercial stocks were injected with 0,1,2 and 4 mgs hydrocortisone acetate per 100 gms body weight at 14 days of age. This type of hormonal treatment was shown to release additional variability in growth without producing any stock-treatment interaction at the level of means. The results indicate a possible new avenue for future research.

Genotype by environment interaction for yearling weight in Nellore cattle applying reaction norms models

2018 ◽  
Vol 58 (11) ◽  
pp. 1996
Author(s):  
S. Ribeiro ◽  
J. P. Eler ◽  
V. B. Pedrosa ◽  
G. J. M. Rosa ◽  
J. B. S. Ferraz ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Environmental Gradient ◽  
Genotype By Environment Interaction ◽  
Reaction Norms ◽  
Breeding Value ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Nellore Cattle ◽  
Gradient Variation

In the present study, a possible existence of genotype × environment interaction was verified for yearling weight in Nellore cattle, utilising a reaction norms model. Therefore, possible changes in the breeding value were evaluated for 46 032 animals, from three distinct herds, according to the environmental gradient variation of the different contemporary groups. Under a Bayesian approach, analyses were carried out utilising INTERGEN software resulting in solutions of contemporary groups dispersed in the environmental gradient from –90 to +100 kg. The estimates of heritability coefficients ranged from 0.19 to 0.63 through the environmental gradient and the genetic correlation between intercept and slope of the reaction norms was 0.76. The genetic correlation considering all animals of the herds in the environmental gradient ranged from 0.83 to 1.0, and the correlation between breeding values of bulls in different environments ranged from 0.79 to 1.0. The results showed no effect of genotype × environment interaction on yearling weight in the herds of this study. However, it is important to verify a possible influence of the genotype × environment in the genetic evaluation of beef cattle, as different environments might cause interference in gene expression and consequently difference in phenotypic response.

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