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Open AccessJournal ArticleDOI

Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

Theo H E Meuwissen, +2 more
- 01 Apr 2001 - 
- Vol. 157, Iss: 4, pp 1819-1829
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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.

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Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

Genomic selection in crops, trees and forages: a review

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

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).
References
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Book

Introduction to quantitative genetics

TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
BookDOI

Markov Chain Monte Carlo in Practice

TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
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Genetics and Analysis of Quantitative Traits

Michael Lynch, +1 more
TL;DR: This book discusses the genetic Basis of Quantitative Variation, Properties of Distributions, Covariance, Regression, and Correlation, and Properties of Single Loci, and Sources of Genetic Variation for Multilocus Traits.
Journal ArticleDOI

An Introduction to Population Genetics Theory

James F. Crow, +1 more
- 01 Sep 1971 - 
TL;DR: An introduction to population genetics theory, An introduction to Population Genetics Theory, Population Genetics theory, Population genetics theory as discussed by the authors, Population genetics, population genetics, and population genetics theories, Population Genetic Theory
Book

An introduction to population genetics theory

TL;DR: An introduction to population genetics theory, An introduction to Population Genetics theory, and more.
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