scholarly journals 210 Genotype by environment interaction for pre-weaning survival in commercial crossbred pigs

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 40-41
Author(s):  
Francesco Tiezzi ◽  
Justin Fix ◽  
Clint Schwab
Keyword(s):  
Fixed Effects ◽  
Genotype By Environment Interaction ◽  
Single Step ◽  
Random Regression ◽  
Residual Variance ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Genomic Breeding ◽  
Genotype By Environment ◽  
The Impact

Abstract Pre-weaning survival (PWS) is a trait of major importance in swine productions systems. Selection is made difficult by the low heritability of the trait(s) and genotype by environment interaction (GxE) could be present. In addition to that, given the binary nature of the trait, phenotypic variance is virtually null in contemporary group where PWS is large. The objective of this study was to assess the impact of heterogeneous phenotypic variance and GxE on PWS. We used survival to day 5 as a trait of interest, available for 574,828 crossbred piglets raised in a commercial environment. Piglets were progeny of 559 sires (450 genotyped with 60k SNP chip) and raised into 242 contemporary groups (CG). In estimating GxE, the E component was represented by fourth-order Legendre polynomials built on the CG solutions. A Single-Step random-regression sire model with heterogeneous residuals (10 classes) was used, once the CG solutions were obtained by a similar model that neglected GxE. Other (fixed) effects in the models were sow parity, litter size, litter transfer of the piglet, gender of the piglet, dam genetic line and litter (random). Results show an increase in phenotypic and residual variance as PWS decreased, which is expected given the nature of the binary trait. Genetic variance increased following the same trend, which made heritability to be constant (~2%). Genomic breeding values for most represented sires were plotted as a function of CG survival. While no variation among the sires can be found in CG with full survival, larger variance is shown as PWS decreases. Re-ranking among the sires is present as CG change. Results suggest that modeling PWS should account for the heterogeneous variance among CG. A moderate GxE in PWS at day 5 is also suggested.

scholarly journals Phenotypic and transcriptomic responses to stress differ according to population geography in an invasive species

2020 ◽  
Author(s):  
Pierre Marin ◽  
Angelo Jaquet ◽  
Justine Picarle ◽  
Marie Fablet ◽  
Vincent Merel ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Invasive Species ◽  
Genotype By Environment Interaction ◽  
Differentially Expressed ◽  
Short Time Scale ◽  
Environment Interaction ◽  
Rapid Adaptation ◽  
Non Invasive ◽  
Genotype By Environment ◽  
The Impact

AbstractBackgroundAdaptation to rapid environmental changes must occur within a short time scale. In this context, studies of invasive species may provide insights into the underlying mechanisms of rapid adaptation as these species have repeatedly encountered and successfully adapted to novel environmental conditions. Here we investigated how invasive and non-invasive populations of D. suzukii deal with an oxidative stress at both the phenotypic and molecular level. We also investigated the impact of transposable element insertions on the differential gene expression between genotypes in response to oxidative stress.ResultsInvasive populations lived longer in the untreated condition than non-invasive Japanese populations. As expected, lifespan was greatly reduced following exposure to paraquat, but this reduction varied among genotypes (a genotype by environment interaction, GEI) with invasive genotypes appearing more affected by exposure than non-invasive genotypes. We also performed transcriptomic sequencing of selected genotypes upon and without paraquat and detected a large number of genes differentially expressed, distinguishing the genotypes in the untreated environment. While a small core set of genes were differentially expressed by all genotypes following paraquat exposure, much of the response of each population was unique. Interestingly, we identified a set of genes presenting genotype by environment interaction (GEI). Many of these differences may reflect signatures of history of past adaptation. Transposable elements (TEs) were not activated after oxidative stress and differentially expressed (DE) genes were significantly depleted of TEs.ConclusionIn the decade since the invasion from the south of Asia, invasive populations of D. suzukii have diverged from populations in the native area regarding their genetic response to oxidative stress. This suggests that such transcriptomic changes could be involved in the rapid adaptation to local environments.

Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model

2019 ◽  
Vol 59 (8) ◽  
pp. 1438
Author(s):  
Y. Fazel ◽  
A. Esmailizadeh ◽  
M. Momen ◽  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameter ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Holstein Cows ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Random Regression Model

Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model. In total, 68945 milk test-day records of first, second and third lactations of 8515 animals that originated from 100 sires and 7743 dams in 34 herds, collected by the Iranian animal breeding centre during 2007–2009, were used. The different sires were considered as different genotypes, while factors such as herd size, herd milk average (HMA), herd protein average and herd fat average were used as criteria to define the different environments. The inclusion of the environmental descriptor improved not only the log-likelihood of the model, but also the Bayesian information criterion. The results showed that defining the environment on the basis of HMA affected genetic parameter estimations more than did the other environmental descriptors. The heritability of milk yield during lactating days reduced when sire × HMA was fitted to the model as an additional random effect, while the genetic and phenotypic correlations between lactating months increased. Therefore, ignoring this interaction term can lead to the biased genetic-parameter estimates, reduced selection accuracy and, thus, different ranking of the bulls in different environments.

scholarly journals Reaction norms in weights at 365 days old in nellore bulls in northern Brazil

2015 ◽  
Vol 36 (6Supl2) ◽  
pp. 4613 ◽  
Author(s):  
Jorge Luís Ferreira ◽  
Alliny Souza de Assis ◽  
Fernando Brito Lopes ◽  
Thomas Wayne Murphy ◽  
Marcelo Corrêa da Silva ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Reaction Norm ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Residual Effects ◽  
Breeding Value ◽  
Environment Interaction ◽  
Nellore Cattle ◽  
Regression Techniques ◽  
Northern Brazil

<p>Genotype by environment interaction (GxE) studies are of particular interest in Brazil because of the regional diversity of environmental effects and the wide variety of management systems. The present study evaluates GxE effects on 365 d weight (365W) of Nellore cattle raised on pasture in northern Brazil. The analysis utilized random regression techniques to model the reaction norm. Fixed effects consisted of sex, contemporary group, and the covariate of age of cow at calving. The environmental gradient, defined by the concatenation of a bull and the state in which the calf was born, was modeled by second order Legendre polynomials. Direct additive genetic and residual effects were fit as random. Results showed differences in the magnitude of expression of genotype in proportion to decreasing favorability of the environment. As the environment became more unfavorable, the correlation of breeding value to different environments decreased. The correlations between the intercept and the level slope for 365W feature were of moderate magnitude, predominantly indicating the reclassification of sires in different environments. Reaction standard model was coherent from a technical and biological view point and enabled the perception of GxE in the genetic evaluation of Nellore cattle in the states of Maranhão, Pará and Tocantins.</p><p> </p>

scholarly journals Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model

Heredity ◽  
2019 ◽  
Vol 123 (2) ◽  
pp. 202-214 ◽  
Author(s):  
Zhe Zhang ◽  
Morten Kargo ◽  
Aoxing Liu ◽  
Jørn Rind Thomasen ◽  
Yuchun Pan ◽  
...  
Keyword(s):  
Reaction Norm ◽  
Genotype By Environment Interaction ◽  
Single Step ◽  
Holstein Cattle ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Norm Model ◽  
Fertility Traits

Simultaneously accounting for population structure, genotype by environment interaction, and spatial variation in marker–trait associations in sugarcaneThis article is one of a selection of papers from the conference “Exploiting Genome-wide Association in Oilseed Brassicas: a model for genetic improvement of major OECD crops for sustainable farming”.

Genome ◽  
2010 ◽  
Vol 53 (11) ◽  
pp. 973-981 ◽  
Author(s):  
Xianming Wei ◽  
Phillip A. Jackson ◽  
Scott Hermann ◽  
Andrzej Kilian ◽  
Katarzyna Heller-Uszynska ◽  
...  
Keyword(s):  
Population Structure ◽  
Association Mapping ◽  
Spatial Variation ◽  
Sugar Content ◽  
Genotype By Environment Interaction ◽  
Cane Yield ◽  
Type I ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
The Impact

Few association mapping studies have simultaneously accounted for population structure, genotype by environment interaction (GEI), and spatial variation. In this sugarcane association mapping study we tested models accounting for these factors and identified the impact that each model component had on the list of markers declared as being significantly associated with traits. About 480 genotypes were evaluated for cane yield and sugar content at three sites and scored with DArT markers. A mixed model was applied in analysis of the data to simultaneously account for the impacts of population structure, GEI, and spatial variation within a trial. Two forms of the DArT marker data were used in the analysis: the standard discrete data (0, 1) and a continuous DArT score, which is related to the marker dosage. A large number of markers were significantly associated with cane yield and sugar content. However, failure to account for population structure, GEI, and (or) spatial variation produced both type I and type II errors, which on the one hand substantially inflated the number of significant markers identified (especially true for failing to account for GEI) and on the other hand resulted in failure to detect markers that could be associated with cane yield or sugar content (especially when failing to account for population structure). We concluded that association mapping based on trials from one site or analysis that failed to account for GEI would produce many trial-specific associated markers that would have low value in breeding programs.

scholarly journals Among-environment heteroscedasticity and genetic autocorrelation: implications for the study of phenotypic plasticity.

Genetics ◽  
1995 ◽  
Vol 139 (4) ◽  
pp. 1815-1829
Author(s):  
P Dutilleul ◽  
C Potvin
Keyword(s):  
Phenotypic Plasticity ◽  
Variance Component ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Data Set ◽  
Genotype By Environment ◽  
Sample Covariance ◽  
The Impact

Abstract The impact of among-environment heteroscedasticity and genetic autocorrelation on the analysis of phenotypic plasticity is examined. Among-environment heteroscedasticity occurs when genotypic variances differ among environments. Genetic autocorrelation arises whenever the responses of a genotype to different environments are more or less similar than expected for observations randomly associated. In a multivariate analysis-of-variance model, three transformations of genotypic profiles (reaction norms), which apply to the residuals of the model while preserving the mean responses within environments, are derived. The transformations remove either among-environment heteroscedasticity, genetic autocorrelation or both. When both nuisances are not removed, statistical tests are corrected in a modified univariate approach using the sample covariance matrix of the genotypic profiles. Methods are illustrated on a Chlamydomonas reinhardtii data set. When heteroscedasticity was removed, the variance component associated with the genotype-by-environment interaction increased proportionally to the genotype variance component. As a result, the genetic correlation rg was altered. Genetic autocorrelation was responsible for statistical significance of the genotype-by-environment interaction and genotype main effects on raw data. When autocorrelation was removed, the ranking of genotypes according to their stability index dramatically changed. Evolutionary implications of our methods and results are discussed.

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