The optimal number of partial least squares components in genomic selection for pork pH

• Main Author: Silveira,Fernanda Gomes da
• Publication Date: 2017
• Other Authors: Duarte,Darlene Ana Souza, Chaves,Lucas Monteiro, Silva,Fabyano Fonseca e, Filho,Ivan Carvalho, Duarte,Marcio de Souza, Lopes,Paulo Sávio, Guimarães,Simone Eliza Facioni
• Format: Article
• Language: eng
• Source: Ciência Rural
• Download full: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000100652
• topic: SNP; genomic prediction; meat quality

ABSTRACT: The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.