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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Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations: Implication of genomic heritability for genomic evaluation
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Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture.
Hossein Mehrban,Deukhwan Lee,Mohammad Moradi,Chung IlCho,Masoumeh Naserkheil,Noelia Ibáñez-Escriche +5 more
TL;DR: The findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed.
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Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.
Yutaka Masuda,Ignacy Misztal,Shogo Tsuruta,Andres Legarra,Ignacio Aguilar,Daniela Lourenco,Breno O. Fragomeni,T.J. Lawlor +7 more
TL;DR: Develop and evaluate an efficient implementation in the computation of the inverse of genomic relationship matrix with the recursion algorithm, called the algorithm for proven and young (APY), in single-step genomic BLUP and validated genomic predictions for young bulls with more than 500,000 genotyped animals in final score for US Holsteins.
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An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments.
TL;DR: The derived equation can accurately predict the accuracy of estimating genomic values for different scenarios of multipopulation genomic prediction and can be used to investigate the potential accuracy of different multipopulations genomic prediction scenarios and to decide on the most optimal design of training populations.
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Genomic selection for grain yield and quality traits in durum wheat
Jemanesh K. Haile,Amidou N’Diaye,Fran R. Clarke,John M. Clarke,Ron Knox,Jessica Rutkoski,Filippo M. Bassi,Curtis J. Pozniak +7 more
TL;DR: All the six ST-GS models appear to be applicable for GS of yield and gluten strength traits in durum wheat, but the simple computational models RR-BLUP or G-BLup for predicating single trait and MT-SI for predicting yield and protein simultaneously are recommended.
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.