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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

Applied animal genomics: results from the field.

TL;DR: The findings of empirical field studies applying GS to the breeding sectors of these main animal protein industries are reviewed and several translational considerations must be addressed before implementing GS in genetic improvement programs.
Journal ArticleDOI

Metabolomic prediction of yield in hybrid rice

TL;DR: It is hypothesized that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction.
Journal ArticleDOI

Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar).

TL;DR: These results indicate that genomic predictions can accelerate genetic progress for SRS resistance in Atlantic salmon and implementation of this approach will contribute to the control of SRS in Chile.
Journal ArticleDOI

Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods.

TL;DR: RF and especially GBM are efficient methods in identifying a subset of SNPs with direct links to candidate genes affecting the growth trait, and consistently outperformed the XgBoost in genomic prediction accuracy.
Journal ArticleDOI

Drought Tolerance in Maize: Indirect Selection through Secondary Traits versus Genomewide Selection

Cathrine Ziyomo, +1 more
- 01 Jul 2013 - 
TL;DR: The results suggest that genomewide selection could increase genetic gains per unit time for grain yield under drought, and that genotyping is cheaper than phenotyping for drought tolerance.
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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