Efficient Methods to Compute Genomic Predictions
TLDR
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.About:
This article is published in Journal of Dairy Science.The article was published on 2008-11-01 and is currently open access. It has received 4196 citations till now. The article focuses on the topics: Best linear unbiased prediction & Allele frequency.read more
Citations
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Whole-Genome Regression and Prediction Methods Applied to Plant and Animal Breeding
TL;DR: An overview of available methods for implementing parametric WGR models is provided, selected topics that emerge in applications are discussed, and a general discussion of lessons learned from simulation and empirical data analysis in the last decade are presented.
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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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Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle
Hans D. Daetwyler,Aurélien Capitan,Hubert Pausch,Paul Stothard,Rianne van Binsbergen,R.F. Brøndum,Xiaoping Liao,Anis Djari,Sabrina Rodriguez,Cécile Grohs,Diane Esquerre,Olivier Bouchez,Marie-Noelle Rossignol,Christophe Klopp,Dominique Rocha,Sébastien Fritz,André Eggen,Phil J. Bowman,David Coote,Amanda J. Chamberlain,Charlotte Anderson,Curt P VanTassell,Ina Hulsegge,Michael E. Goddard,Bernt Guldbrandtsen,M.S. Lund,Roel F. Veerkamp,Didier Boichard,Ruedi Fries,Ben J. Hayes +29 more
TL;DR: The 1000 bull genomes project supports the goal of accelerating the rates of genetic gain in domestic cattle while at the same time considering animal health and welfare by providing the annotated sequence variants and genotypes of key ancestor bulls.
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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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The Impact of Genetic Architecture on Genome-Wide Evaluation Methods
TL;DR: The relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on Me, which is a property of the target genome, as well as the architecture of the trait (NQTL).
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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Coefficients of Inbreeding and Relationship
TL;DR: The importance of having a coefficient by means of which the degree of inbreeding may be expressed has been brought out by Pearl' in a number of papers published between 1913 and 1917.
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Strategy for applying genome-wide selection in dairy cattle.
TL;DR: Genome-wide selection may become a popular tool for genetic improvement in livestock after a strategy that utilizes these advantages was compared with a traditional progeny testing strategy under a typical Canadian-like dairy cattle situation.
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Derivation, calculation, and use of national animal model information.
Paul M. VanRaden,G.R. Wiggans +1 more
TL;DR: New terms and definitions were developed to explain national USDA genetic evaluations computed by an animal model, whereiability is the squared correlation of predicted and true transmitting ability.
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Accuracy of Genomic Selection Using Different Methods to Define Haplotypes
TL;DR: It was concluded that genomic selection is considerably more accurate than traditional selection, especially for a low-heritability trait.