Comparison of Genomic Best Linear Unbiased Prediction and Bayesian Regularization Neural Networks for Genomic Selection
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چکیده
منابع مشابه
A Comparison of the Sensitivity of the BayesC and Genomic Best Linear Unbiased Prediction(GBLUP) Methods of Estimating Genomic Breeding Values under Different Quantitative Trait Locus(QTL) Model Assumptions
The objective of this study was to compare the accuracy of estimating and predicting breeding values using two diverse approaches, GBLUP and BayesC, using simulated data under different quantitative trait locus(QTL) effect distributions. Data were simulated with three different distributions for the QTL effect which were uniform, normal and gamma (1.66, 0.4). The number of QTL was assumed to be...
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Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Ge...
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BACKGROUND With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework...
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Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the prob...
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Genomic selection is an upgrading form of marker-assisted selection for quantitative traits, and it differs from the traditional marker-assisted selection in that markers in the entire genome are used to predict genetic values and the QTL detection step is skipped. Genomic selection holds the promise to be more efficient than the traditional marker-assisted selection for traits controlled by po...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2019
ISSN: 2169-3536
DOI: 10.1109/access.2019.2922006