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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Quantitative Trait Loci Mapping and Molecular Breeding for Developing Stress Resilient Maize for Sub-Saharan Africa
Kassa Semagn,Yoseph Beyene,Raman Babu,Sudha K. Nair,Manje Gowda,Biswajit Das,Amsal Tarekegne,Stephen Mugo,George Mahuku,Mosisa Worku,Marilyn L. Warburton,Michael Olsen,Boddupalli M. Prasanna +12 more
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Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses
Daniela Lourenco,Ignacy Misztal,Shogo Tsuruta,Ignacio Aguilar,E. Ezra,M. Ron,Andrey Shirak,Joel Ira Weller +7 more
TL;DR: Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations.
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Genomic relationships reveal significant dominance effects for growth in hybrid Eucalyptus.
TL;DR: This work evaluated models accounting for additive, dominance, and first-order epistatic interactions and showed that the models can capture a large proportion of the genetic variance from dominance and epistasis for growth traits as those components are typically not independent.
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Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix
TL;DR: Relaxing the GBLUP assumption of equal marker contribution by increasing the weight that is given to a specific marker in the construction of the trait-specific G resulted in increased predictive performance and bias in prediction estimates was reduced.
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Effect of predictor traits on accuracy of genomic breeding values for feed intake based on a limited cow reference population
TL;DR: Using predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records, which was reduced when more predictor traits were used.
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.