Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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TLDR
It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.Abstract:
Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.read more
Citations
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Evaluation of genome-wide selection efficiency in maize nested association mapping populations
TL;DR: The RR-BLUP method was the preferred method for estimating marker effects in GWS with prediction accuracies comparable to or greater than BayesA and BayesB, and the accuracy of prediction was relatively insensitive to marker density.
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On Marker-Assisted Prediction of Genetic Value: Beyond the Ridge
TL;DR: Here, phenotype-marker associations are modeled hierarchically via multilevel models including chromosomal effects, a spatial covariance of marked effects within chromosomes, background genetic variability, and family heterogeneity, and Bayesian methods are presented.
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Genomic Prediction of Northern Corn Leaf Blight Resistance in Maize with Combined or Separated Training Sets for Heterotic Groups
TL;DR: The results encourage the application of genomic prediction in NCLB-resistance breeding programs and the use of combined training sets, which led to significantly greater prediction accuracies for both heterotic groups.
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Genomic selection in crops, trees and forages: a review
Zibei Lin,Zibei Lin,Zibei Lin,Ben J. Hayes,Ben J. Hayes,Ben J. Hayes,Hans D. Daetwyler,Hans D. Daetwyler,Hans D. Daetwyler +8 more
TL;DR: The capacity of genomic selection to reduce generation intervals by accurately evaluating traits at an early age makes it an effective tool to deliver more genetic gain from plant breeding in many cases.
Journal ArticleDOI
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models
Jaime Cuevas,José Crossa,Osval A. Montesinos-López,Juan Burgueño,Paulino Pérez-Rodríguez,Gustavo de los Campos +5 more
TL;DR: Two multi-environment Bayesian genomic models are proposed: one considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK).
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