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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Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize.
TL;DR: Genomic selection is a useful approach for prescreening candidate lines, and the empirical evidence provided by the current study for TRMs and nonadditive effects can inform plant breeding and in turn contribute to the improvement of selection efficiency in practical GS-assisted breeding programs.
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Genetic tests for estimating dairy breed proportion and parentage assignment in East African crossbred cattle
Eva M. Strucken,Hawlader A. Al-Mamun,Cecilia Esquivelzeta-Rabell,Cedric Gondro,Okeyo Ally Mwai,John P. Gibson +5 more
TL;DR: Panels of SNPs based on the largest differences in allele frequency between European dairy breeds and a combined Nelore/N’Dama population gave the best predictions of dairy breed proportion and performance for parentage assignment was based on opposing homozygotes.
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Breeding and Genetics Symposium: really big data: processing and analysis of very large data sets.
TL;DR: Modern animal breeding data sets are large and getting larger, due in part to recent availability of high-density SNP arrays and cheap sequencing technology, and data mining algorithms applied to large data sets may help identify unexpected relationships in the data, and improved visualization tools will provide insights.
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Genome-Wide Association Mapping of Major Root Length QTLs Under PEG Induced Water Stress in Wheat
TL;DR: The B genome showed significant importance in controlling root growth both under stress and non-stress conditions, and the identified markers can potentially be validated and used for marker assisted selection.
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Genome-wide Association Mapping and Prediction of Adult Stage Septoria tritici Blotch Infection in European Winter Wheat via High-Density Marker Arrays.
Quddoos H. Muqaddasi,Yusheng Zhao,Bernd Rodemann,Jörg Plieske,Martin W. Ganal,Marion S. Röder +5 more
TL;DR: Elite European winter wheat varieties hold large genetic variance for resistance to Septoria tritici blotch (STB) infection and genomic prediction suggested the effective use of genomic selection for STB resistance.
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.