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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Metabolomic selection for enhanced fruit flavor
TL;DR: In this paper , the authors leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor.
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Breeding the dairy cow of the future: what do we need?
TL;DR: Current knowledge clearly demonstrates that once unfavourable trends have been identified and the appropriate breeding strategy implemented, the reversal of genetic trends is achievable, even for low-heritability traits such as reproductive performance.
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Leveraging Breeding Values Obtained from Random Regression Models for Genetic Inference of Longitudinal Traits
TL;DR: This work identifies quantitative trait loci with transient and persistent effects on shoot growth in rice and applies the first application of random regression models for GWAS of longitudinal traits in crops.
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Use of multi-trait and random regression models to identify genetic variation in tolerance to porcine reproductive and respiratory syndrome virus
Graham Lough,H. Rashidi,Ilias Kyriazakis,Jack C. M. Dekkers,Andrew S. Hess,Melanie K. Hess,N. Deeb,Antti Kause,Joan K. Lunney,Raymond R. R. Rowland,Herman A. Mulder,Andrea Doeschl-Wilson +11 more
TL;DR: Of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance, and evidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only.
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Whole-genome sequence data uncover loss of genetic diversity due to selection
TL;DR: The use of WGS for genetic diversity quantification revealed that selection results in considerable losses of genetic diversity for rare variants, which could be mitigated by a few percent depending on which method is chosen to estimate relationships from WGS data.
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.