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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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Citations
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Impact of imputing markers from a low density chip on the reliability of genomic breeding values in Holstein populations

TL;DR: The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50-000- marker SNP panel.
Journal ArticleDOI

Development of an alfalfa SNP array and its use to evaluate patterns of population structure and linkage disequilibrium.

TL;DR: A high level of consistency in LD was found between and within the two subpopulations of cultivated dormant and nondormant alfalfa suggesting that genome-wide association studies (GWAS) and genomic selection (GS) could be conducted using al falfa genotypes from throughout the fall dormancy spectrum, but the relatively low LD levels would require a large number of markers to fully saturate the genome.
BookDOI

Advances in plant breeding strategies: Breeding, biotechnology and molecular tools

TL;DR: Through case studies from the Pampa Biome, the valorization of plant genetic resources through new domestication, the promotion of the use of scientifi cally developed best management practices for in situ conservation, the widening of the germplasm base for breeding programs, plant breeding for stress tolerance, the development of participatory plant breeding programs and theDevelopment of high quality products are discussed.
Journal ArticleDOI

Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets

TL;DR: DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets and achieves scalable and accurate prediction performance across a range of realistic genetic architectures.
Journal ArticleDOI

Dry matter intake and feed efficiency profiles of 3 genotypes of Holstein-Friesian within pasture-based systems of milk production

TL;DR: Results showed that aggressive selection had no effect on dry matter intake across lactation when managed on intensive pasture-based systems of milk production, although the ranking of genotypes for feed efficiency differed depending on the definition of feed efficiency used.
References
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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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Michael Lynch, +1 more
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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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