Invited review: reliability of genomic predictions for North American Holstein bulls.
Paul M. VanRaden,C.P. Van Tassell,G.R. Wiggans,Tad S. Sonstegard,Robert D. Schnabel,Jeremy F. Taylor,Flavio S Schenkel +6 more
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TLDR
Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations and genomic prediction improves reliability by tracing the inheritance of genes even with small effects.About:
This article is published in Journal of Dairy Science.The article was published on 2009-01-01 and is currently open access. It has received 1166 citations till now.read more
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Invited review: Genomic selection in dairy cattle: progress and challenges.
TL;DR: In this article, a new technology called genomic selection is revolutionizing dairy cattle breeding, which refers to selection decisions based on genomic breeding values (GEBV) and is calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby capturing all the quantitative trait loci (QTL) that contribute to variation in a trait.
Journal Article
Invited review: Genomic selection in dairy cattle: progress and challenges (vol 92, pg 433, 2009)
TL;DR: The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams, and the increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBv only.
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Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.
TL;DR: A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure.
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Extension of the bayesian alphabet for genomic selection
David Habier,Rohan L. Fernando,Kadir Kizilkaya,Kadir Kizilkaya,Dorian J. Garrick,Dorian J. Garrick +5 more
TL;DR: Estimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture, and it is believed that Bayes Cπ has merit for routine applications.
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Genome-Wide Regression and Prediction with the BGLR Statistical Package
TL;DR: The BGLR R-package implements a large collection of Bayesian regression models, including parametric variable selection and shrinkage methods and semiparametric procedures, which allows integrating various parametric and nonparametric shrinkage and variable selection procedures in a unified and consistent manner.
References
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Efficient Methods to Compute Genomic Predictions
TL;DR: 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.
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Commercial application of marker- and gene-assisted selection in livestock: Strategies and lessons
TL;DR: Although molecular genetic information has been used in industry programs for several decades and is growing, the extent of use has not lived up to initial expectations and the current attitude toward marker-assisted selection is therefore one of cautious optimism.
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SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries
Curtis P. Van Tassell,Timothy P. L. Smith,Lakshmi K. Matukumalli,Lakshmi K. Matukumalli,Jeremy F. Taylor,Robert D. Schnabel,Cindy Lawley,Christian D. Haudenschild,Stephen S. Moore,Wesley C. Warren,Tad S. Sonstegard +10 more
TL;DR: An economical, efficient, single-step method for SNP discovery, validation and characterization that uses deep sequencing of reduced representation libraries (RRLs) from specified target populations and may be applied to any species with at least a partially sequenced genome.
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Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition
Bernard Grisart,Frédéric Farnir,Latifa Karim,Nadine Cambisano,Jong-Joo Kim,Alex Kvasz,Myriam Mni,Patricia Simon,Jean-Marie Frère,Wouter Coppieters,Michel Georges +10 more
TL;DR: A high-density single-nucleotide polymorphism map of the 3.8-centimorgan BULGE30-BULGE9 interval containing the QTL is constructed and shows that the association with milk fat percentage maximizes at the DGAT1 gene.
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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.