Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information
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TLDR
The proposed methodology may allow the upgrading of an existing evaluation to incorporate the genomic information when the information attributable to genomics can be expressed as modifications to the numerator relationship matrix.About:
This article is published in Journal of Dairy Science.The article was published on 2009-09-01 and is currently open access. It has received 475 citations till now. The article focuses on the topics: Conjugate gradient method & Matrix (mathematics).read more
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Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.
TL;DR: A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure.
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A relationship matrix including full pedigree and genomic information
TL;DR: This work proposes a joint distribution of genotyped and ungenotyped genetic values, with a pedigree-genomic relationship matrix H, which is suitable for iteration on data algorithms that multiply a vector times a matrix, such as preconditioned conjugated gradients.
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Genomic prediction when some animals are not genotyped
TL;DR: The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction, and has the potential to become the standard tool for genomic predictions of breeding values in future practical evaluations in pig and cattle breeding.
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Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels
Malena Erbe,Ben J. Hayes,Ben J. Hayes,Lakshmi K. Matukumalli,S. Goswami,Phil J. Bowman,Coralie M. Reich,B.A. Mason,Michael E. Goddard +8 more
TL;DR: This work assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers.
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Genome-wide association mapping including phenotypes from relatives without genotypes.
TL;DR: The single-step GBLUP (ssGBLUP) method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.
References
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Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
TL;DR: 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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BI-CGSTAB: a fast and smoothly converging variant of BI-CG for the solution of nonsymmetric linear systems
TL;DR: Numerical experiments indicate that the new variant of Bi-CG, named Bi- CGSTAB, is often much more efficient than CG-S, so that in some cases rounding errors can even result in severe cancellation effects in the solution.
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Efficient Methods to Compute Genomic Predictions
TL;DR: Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously, and a blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects.
Book
Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods
TL;DR: In this book, which focuses on the use of iterative methods for solving large sparse systems of linear equations, templates are introduced to meet the needs of both the traditional user and the high-performance specialist.