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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Prediction of plant height in Arabidopsis thaliana using DNA methylation data.
TL;DR: This work used methylation information for predicting plant height (PH) in Arabidopsis thaliana nonparametrically, using reproducing kernel Hilbert spaces (RKHS) regression and created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP).
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Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression
Delphine Ly,Sylvie Huet,Arnaud Gauffreteau,Renaud Rincent,Gaëtan Touzy,Agathe Mini,Jean-Luc Jannink,Jean-Luc Jannink,Fabien Cormier,Etienne Paux,Stéphane Lafarge,Jacques Le Gouis,Gilles Charmet +12 more
TL;DR: This study demonstrates how genome-wide selection can facilitate breeding for adaptation and proposes a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the FR-gBLUP.
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Invited review: Quantitative trait nucleotide determination in the era of genomic selection
J.I. Weller,M. Ron +1 more
TL;DR: This review will attempt to address the following questions based on the current state of bovine genomics and statistics: What are the pros and cons for QTN determination?
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Genomic Prediction Enhanced Sparse Testing for Multi-environment Trials
Diego Jarquin,Reka Howard,José Crossa,Yoseph Beyene,Manje Gowda,Johannes W. R. Martini,Giovanny Covarrubias Pazaran,Juan Burgueño,Ángela Pacheco,Martin Grondona,Valentin Wimmer,Boddupalli M. Prasanna +11 more
TL;DR: The results showed that the genome-based model including GE (M3) captured more phenotypic variation than the models that did not include this component and provided higher prediction accuracy than models M1 and M2 for the different allocation scenarios.
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Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures
TL;DR: Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved, and the ranking of estimation methods was different with than without selection.
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.