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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Development and Evolution of an Intermediate Wheatgrass Domestication Program
TL;DR: In this paper, Barkworth et al. proposed a domestication program to improve the grain yield of the intermediate wheatgrass (Thinopyrum intermedium [Host] Barkworth & D.R. Dewey).
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Computational strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction.
Andres Legarra,Vincent Ducrocq +1 more
TL;DR: This work proposes several strategies to implement the single step of the SSGBLUP in a simpler manner and shows a new derivation of the single-step method, which includes, as an extra effect, deviations from strictly polygenic breeding values.
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Genomics to systems biology in animal and veterinary sciences: Progress, lessons and opportunities☆
TL;DR: The Systems genomic BLUP (sgBLUP) prediction method is introduced in this paper as a next generation WGP or GS tool that can account for and differentiate SNPs with known biological roles in the phenotypic or disease outcomes and potentially increase the accuracy of prediction.
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Genomic evaluation by including dominance effects and inbreeding depression for purebred and crossbred performance with an application in pigs.
TL;DR: It is confirmed that genetic correlations of breeding values between purebred and crossbred performances within breed are positive and moderate and closed-form estimators of inbreeding depression, variance components and correlations that can be easily interpreted in a quantitative genetics context are developed.
Impact of imputing markers from a low density chip on the reliability of genomic breeding values in Holstein populations
Romain Dassonneville,R.F. Brøndum,Tom Druet,Sébastien Fritz,François Guillaume,Bernt Guldbrandtsen,Mogens Sandø Lund,Vincent Ducrocq,Guosheng Su +8 more
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.
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.