scholarly journals Association between lactoferrin single nucleotide polymorphisms and milk production traits in Polish Holstein cattle

2014 ◽  
Vol 57 (1) ◽  
pp. 1-12
Author(s):  
Adrianna Pawlik ◽  
Grazyna Sender ◽  
Magdalena Sobczynska ◽  
Agnieszka Korwin-Kossakowska ◽  
Jolanta Oprzadek ◽  
...  
Keyword(s):  
Genotype By Environment Interaction ◽  
Bovine Lactoferrin ◽  
Environment Interaction ◽  
Production Traits ◽  
Breeding Values ◽  
Genotype By Environment ◽  
Lactoferrin Gene ◽  
Milk Performance ◽  
Estimated Breeding Values ◽  
The Impact

Abstract. Bovine lactoferrin exhibits strong potential for further applications as a mastitis resistance marker. Since selection for mastitis resistance should not interfere with dairy performance, we investigated the association between bovine lactoferrin gene polymorphism and production traits in Polish Holsteins. The associations between four SNPs, localized in the 5’-flanking region and in exons 4 and 9 of the lactoferrin gene, and dairy performance were examined. SNPs were associated with almost all test-day milk performance traits. Significant associations were found between lactoferrin genotypes and the estimated breeding values for those traits. To find out whether the discrepancies between the lactoferrin gene SNP’s influence on phenotype (test-day milk performance) and on estimated breeding values originate from the impact of other factors, we explored the genotype by environment interaction. Substantial impacts of SCC, lactation stage and parity were found. This paper suggests that the genotype by environment interaction may significantly change associations between genes and traits. It is important to include similar analyses to the studies on disease markers before using them in the selection.

scholarly journals Accuracies of univariate and multivariate genomic prediction models in African Cassava

2017 ◽  
Author(s):  
Uche Godfrey Okeke ◽  
Deniz Akdemir ◽  
Ismail Rabbi ◽  
Peter Kulakow ◽  
Jean-Luc Jannink
Keyword(s):  
Genomic Selection ◽  
Mixed Model ◽  
Genetic Evaluation ◽  
Environment Interaction ◽  
Genotype By Environment Interactions ◽  
Breeding Values ◽  
Genotype By Environment ◽  
Step Model ◽  
One Step ◽  
Estimated Breeding Values

List of abbreviationsGSGenomic SelectionBLUPBest Linear Unbiased PredictionEBVsEstimated Breeding ValuesEGVsEstimated genetic ValuesGEBVsGenomic Estimated Breeding ValuesSNPsSingle Nucleotide polymorphismsGxEGenotype-by-environment interactionsGxEGenotype-by-environment interactionsGxGGene-by-gene interactionsGxGxEGene-by-gene-by-environment interactionsuTUnivariate single environment one-step modeluEUnivariate multi environment one-step modelMTMulti-trait single environment one-step modelMEMultivariate single trait multi environment modelAbstractBackgroundGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.MethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.ResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.ConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

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.

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 Genotype by Environment Interaction for Production Traits While Accounting for Heteroscedasticity

2007 ◽  
Vol 90 (8) ◽  
pp. 3889-3899 ◽  
Author(s):  
A.G. Fahey ◽  
M.M. Schutz ◽  
D.L. Lofgren ◽  
A.P. Schinckel ◽  
T.S. Stewart
Keyword(s):  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Production Traits ◽  
Genotype By Environment

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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