titoli, abstracts, parole chiave >>>
Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle

Gaspa, Giustino and Pintus, Maria Annunziata and Nicolazzi, Ezequiel Luis and Vicario, Daniele and Valentini, Alessio and Dimauro, Corrado and Macciotta, Nicolò Pietro Paolo (2013) Use of principal component approach to predict direct genomic breeding values for beef traits in Italian Simmental cattle. Journal of Animal Science, Vol. 91 (1), p. 29-37. eISSN 1525-3163. Article.

Full text not available from this repository.

DOI: 10.2527/jas.2011-5061


In the current study, principal component (PC) analysis was used to reduce the number of predictors in the estimation of direct genomic breeding values (DGV) for meat traits in a sample of 479 Italian Simmental bulls. Single nucleotide polymorphism marker genotypes were determined with the 54K Illumina beadchip. After edits, 457 bulls and 40,179 SNP were retained. Principal component extraction was performed separately for each chromosome and 2466 new variables able to explain 70% of total variance were obtained. Bulls were divided into reference and validation population. Three scenarios of the ratio reference:validation were tested: 70:30, 80:20, 90:10. Effect of PC scores on polygenic EBV was estimated in the reference population using different models and methods. Traits analyzed were 7 beef traits: daily BW gain, size score, muscularity score, feet and legs score, beef index (economic index), calving ease direct effect, and cow muscularity. Accuracy was calculated as correlation between DGV and polygenic EBV in the validation bulls. Muscularity, feet and legs, and the beef index showed the greatest accuracies; calving ease, the least. In general, accuracies were slightly greater when reference animals were selected at random and the best scenario was 90:10 and no substantial differences in accuracy were found among different methods. Principal component analysis is entirely based on the factorization of the SNP (co)variance matrix and produced a reduced set of variables (6% of the original variables) which may be used for different phenotypic traits. In spite of this huge reduction in the number of independent variables, DGV accuracies resulted similar to those obtained by using the whole set of SNP markers. Accuracies of direct genomic values found in the present work were always greater than those of traditional parental average (PA). Thus, results of the present study may suggest a possible advantage of use of genomic indexes in the preselection of performance test candidates for beef traits. Moreover, the relevant reduction of variable space might allow genomic selection implementation also in small populations.

Item Type:Article
ID Code:8836
Uncontrolled Keywords:Beef trait, genomic selection, principal component analysis
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:American Society of Animal Science
Deposited On:27 Mar 2013 17:35

I documenti depositati in UnissResearch sono protetti dalle leggi che regolano il diritto d'autore

Repository Staff Only: item control page