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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Across breed multi-trait random regression genomic predictions in the Nordic Red dairy cattle
M. L. Makgahlela,Esa Mäntysaari,Ismo Strandén,Minna Koivula,Mikko J. Sillanpää,U. S. Nielsen,Jarmo Juga +6 more
TL;DR: This article evaluated reliabilities of direct genomic breeding values (DGV) in selection candidates using multi-trait random regression model which account for interactions between marker effects and breed of origin in the admixed Nordic Red dairy cattle.
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The use of runs of homozygosity for estimation of recent inbreeding in Holstein cattle
Artur Gurgul,Tomasz Szmatoła,P. Topolski,Igor Jasielczuk,Kacper Żukowski,Monika Bugno-Poniewierska +5 more
TL;DR: The correlations tended to increase as pedigree depth increased, and were the highest for animals with seven complete generations of pedigree data, which suggests that ROH-based inbreeding coefficients better reflect recent relatedness among animals.
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Increasing the number of single nucleotide polymorphisms used in genomic evaluation of dairy cattle.
G.R. Wiggans,T.A. Cooper,Paul M. VanRaden,C.P. Van Tassell,Derek M. Bickhart,Tad S. Sonstegard +5 more
TL;DR: GeneSeek designed a new version of the GeneSeek Genomic Profiler HD BeadChip for Dairy Cattle, which originally had >77,000 single nucleotide polymorphisms (SNP), but this set was reduced to 77,321 SNP to remove SNP that were not included during manufacture, reduce computing time, and improve imputation performance.
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KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters
TL;DR: A machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency.
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Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population.
TL;DR: This study compared genomic predictions based on imputed high-density markers in the Nordic Holstein population using a genomic BLUP (GBLUP) model, 4 Bayesian exponential power models with different shape parameters, and a Bayesian mixture model (a mixture of 4 normal distributions).
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.