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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Frequentist p-values for large-scale-single step genome-wide association, with an application to birth weight in American Angus cattle
Ignacio Aguilar,Andres Legarra,Fernando Flores Cardoso,Fernando Flores Cardoso,Yutaka Masuda,Daniela Lourenco,Ignacy Misztal +6 more
TL;DR: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL) and can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.
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The Dimensionality of Genomic Information and Its Effect on Genomic Prediction.
TL;DR: Genomic predictions with the APY inverse of the GRM are more accurate than by the regular inverse, and these assumptions were evaluated by simulation.
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Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
Philomin Juliana,Ravi P. Singh,Pawan K. Singh,José Crossa,Julio Huerta-Espino,Caixia Lan,Sridhar Bhavani,Jessica Rutkoski,Jessica Rutkoski,Jesse Poland,Gary C. Bergstrom,Mark E. Sorrells +11 more
TL;DR: Genomic prediction for seedling and adult plant resistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction and concluded that GS is a promising approach for improvement of quantitative rust resistance.
Journal ArticleDOI
Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep
TL;DR: This study shows that there is a large range of genome diversity in Australian sheep breeds, especially in Merino sheep, and the observed range of diversity will influence the design of genome-wide association studies and the results that can be obtained from them.
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Genetic Variation in the Social Environment Contributes to Health and Disease
Amelie Baud,Megan K. Mulligan,Francesco Paolo Casale,Jesse Ingels,Casey J. Bohl,Jacques Callebert,Jean-Marie Launay,Jon P. Krohn,Andres Legarra,Robert W. Williams,Oliver Stegle +10 more
TL;DR: An important contribution of the social environment to phenotypic variation is uncovered, which sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.
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.