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2013
Voort, G. V., Sargolzaei, M., Wang, Z., Moore, S., Mandell, I., Lu, D., Kelly, M., Plastow, G., Miller, S.
Background Genetic improvement of beef quality will benefit both producers and consumers, and can be achieved by selecting animals that carry desired quantitative trait nucleotides (QTN), which result from intensive searches using genetic markers. This paper presents a genome-wide association...
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Impact of Genotype Imputation on the Performance of GBLUP and Bayesian Methods for Genomic Prediction
Download2014
Sargolzaei, M., Chen, L., Li, C., Schenkel, F.
The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels,...
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2012
Plastow, G., Lu, D., Miller, S., Moore, S., Vander Voort, G., Wang Z., Sargolzaei, M., Li, C., Kelly, M.
Linkage disequilibrium (LD) and the persistence of its phase across populations are important for genomic selection as well as fine scale mapping of quantitative trait loci (QTL). However, knowledge of LD in beef cattle, as well as the persistence of LD phase between crossbreds (C) and purebreds,...
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2014
Sargolzaei, M., Li, C., Schenkel, F., Chen, L.
Background Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across populations. The goal of this study was to...