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Use of different statistical models to predict direct genomic values for productive and functional traits in Italian Holsteins

Pintus, Maria Annunziata and Nicolazzi, Ezequiel Luis and Van Kaam, Jan B.C.H.M. and Biffani, Stefano and Stella, Alessandra and Gaspa, Giustino and Dimauro, Corrado and Macciotta, Nicolò Pietro Paolo (2013) Use of different statistical models to predict direct genomic values for productive and functional traits in Italian Holsteins. Journal of Animal Breeding and Genetics, Vol. 130 (1), p. 32-40. ISSN 0931-2668. eISSN 1439-0388. Article.

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DOI: 10.1111/j.1439-0388.2012.01019.x


One of the main issues in genomic selection was the huge unbalance between number of markers and phenotypes available. In this work, principal component analysis is used to reduce the number of predictors for calculating direct genomic breeding values (DGV) for production and functional traits. 2093 Italian Holstein bulls were genotyped with the 54 K Illumina beadchip, and 39 555 SNP markers were retained after data editing. Principal Components (PC) were extracted from SNP matrix, and 15 207 PC explaining 99% of the original variance were retained and used as predictors. Bulls born before 2001 were included in the reference population, younger animals in the test population. A BLUP model was used to estimate the effect of principal component on deregressed proof (DRPF) for 35 traits and results were compared to those obtained by using SNP genotypes as predictors either with BLUP or with Bayes_A models. Correlations between DGV and DRPF did not substantially differ among the three methods except for milk fat content. The lowest prediction bias was obtained for the method based on the use of principal component. Regression coefficients of DRPF on DGV were lower than one for the approach based on the use of PC and higher than one for the other two methods. The use of PC as predictors resulted in a large reduction of number of predictors (approximately 38%) and of computational time that was approximately 2% of the time needed to estimate SNP effects with the other two methods. Accuracies of genomic predictions were in most of cases only slightly higher than those of the traditional pedigree index, probably due to the limited size of the considered population.

Item Type:Article
ID Code:8118
Uncontrolled Keywords:Cattle breeding, genomic selection, principal component analysis, SNP
Subjects:Area 07 - Scienze agrarie e veterinarie > AGR/17 Zootecnica generale e miglioramento genetico
Divisions:001 Università di Sassari > 01-a Nuovi Dipartimenti dal 2012 > Agraria
Publisher:Blackwell / Wiley
Copyright Holders:© 2012 Blackwell Verlag GmbH
Deposited On:08 Nov 2012 10:54

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