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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Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE
TL;DR: The main focus of quantitative genetics in plant breeding is on identifying candidates with the best genotypic value for a target population of environments as mentioned in this paper, while the core concept of continuous variation being due to multiple Mendelian loci remains unchanged.
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Identification and Dissection of Four Major QTL Affecting Milk Fat Content in the German Holstein-Friesian Population
Xiaolong Wang,Christine Wurmser,Hubert Pausch,Simone Jung,F Reinhardt,Jens Tetens,Georg Thaller,Ruedi Fries +7 more
TL;DR: The results provide evidence that alteration of regulatory sites is an important aspect of genetic variation of complex traits in cattle.
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Evaluation of the accuracy of imputed sequence variant genotypes and their utility for causal variant detection in cattle
Hubert Pausch,Iona M. MacLeod,Ruedi Fries,Reiner Emmerling,Phil J. Bowman,Hans D. Daetwyler,Michael E. Goddard +6 more
TL;DR: The population-based imputation of millions of sequence variants in large cohorts is computationally feasible and provides accurate genotypes, however, the accuracy of imputation is low in regions where the genome contains large segmental duplications or the coverage with array-derived single nucleotide polymorphisms is poor.
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Unknown-parent groups in single-step genomic evaluation.
TL;DR: In single-step genomic evaluation using best linear unbiased prediction (ssGBLUP), genomic predictions are calculated with a relationship matrix that combines pedigree and genomic information and contributions to UPG elements from genomic relationships can be biased.
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Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.
Renaud Rincent,Jean-Paul Charpentier,Patricia Faivre-Rampant,Etienne Paux,Jacques Le Gouis,Catherine Bastien,Vincent Segura +6 more
TL;DR: This work proposed using near-infrared spectroscopy as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach ”phenomic selection” (PS).
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.