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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Multi-trait genomic prediction for nitrogen response indices in tropical maize hybrids
Danilo Hottis Lyra,Leandro de Freitas Mendonça,Giovanni Galli,Filipe Couto Alves,Ítalo Stefanine Correia Granato,Roberto Fritsche-Neto +5 more
TL;DR: To compare accuracy of single- and multi-trait genomic prediction in two maize datasets, and to evaluate prediction of four selection indices that could contribute to the selection of tropical maize hybrids under contrasting nitrogen conditions, it is aimed to compare the use of linear (GBLUP) and nonlinear (RKHS/GK) kernels in STGP and MTGP analyses.
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Accuracy of Across-Environment Genome-Wide Prediction in Maize Nested Association Mapping Populations
Zhigang Guo,Dominic M. Tucker,Daolong Wang,Christopher J. Basten,Elhan S. Ersoz,William Briggs,Jianwei Lu,Min Li +7 more
TL;DR: This study evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments to conclude that genome-wide prediction provided greater prediction accuracies than traditional quantitative traits loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction.
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Solving efficiently large single-step genomic best linear unbiased prediction models.
TL;DR: The developed approaches to invert the two relationship matrices are expected to allow much higher number of genotyped animals than was used in this study.
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Models for Genome × Environment Interaction: Examples in Livestock
TL;DR: In both livestock and plant breeding, methods that use genomic information can better cope with a reduced degree of replication of individuals across environments, as it is actually the alleles that must be replicated across environments.
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Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters.
Matteo Bergamaschi,Christian Maltecca,Constantino Schillebeeckx,Nathan P. McNulty,Clint Schwab,Caleb M Shull,Justin Fix,Francesco Tiezzi +7 more
TL;DR: This study provides new evidence linking gut microbiome composition with growth and carcass traits in swine, while also identifying putative host genetic markers associated with significant differences in the abundance of several prevalent microbiome features.
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.