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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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Journal ArticleDOI

Warped linear mixed models for the genetic analysis of transformed phenotypes

TL;DR: In simulations and applications to real data from human, mouse and yeast, it is shown that using transformations inferred by the model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction.
Journal ArticleDOI

Genomic predictions in Angus cattle: comparisons of sample size, response variables, and clustering methods for cross-validation.

TL;DR: A larger training population size resulted in higher accuracies in validation animals and explained on average 18% (69% improvement) additional genetic variance across all traits.
Journal ArticleDOI

Prediction of plant height in Arabidopsis thaliana using DNA methylation data.

TL;DR: This work used methylation information for predicting plant height (PH) in Arabidopsis thaliana nonparametrically, using reproducing kernel Hilbert spaces (RKHS) regression and created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP).
Journal ArticleDOI

Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression

TL;DR: This study demonstrates how genome-wide selection can facilitate breeding for adaptation and proposes a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the FR-gBLUP.
Journal ArticleDOI

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.

TL;DR: ANN with Bayesian regularization performed as well as linear Bayesian regression models in predicting additive genetic values, supporting the idea that ANN are useful as universal approximators of functions of interest in breeding contexts.
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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