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Genomic selection, that is based on the prediction of the breeding value (the genomic breeding value or GEBV) of the animals considering genomic information, is changing breeding strategies and approaches in dairy cattle and in several other livestock species. The starting points for the application of genomic selection in pigs were the development of a first commercial single-nucleotide polymorphism panel for high throughput genotyping, the sequencing of the pig genome and the application of statistical and methodological approaches first developed in dairy cattle and then adapted to the peculiarities of the pig breeding industry. In this review, we focused on the specific applications of the genomic selection in pigs, considering its limits, its advantages and its perspectives throughout an analysis of the simulation studies and field applications already available targeting many different traits (with high and low heritability). In addition, we presented an overview of the problems related to the implementation of genomic selection in crossbred and multi-breed pig populations and the potential solutions taking into account the economic aspects that should be faced in including genomic selection in pig breeding programmes.

The development of high throughput genomics (next generation sequencing and high throughput genotyping) and metabolomics platforms has opened new perspectives for the identification of the genetics factors affecting traits of biological relevance in all species, including production traits in farm animals. In pigs, benefits derived from the recent sequencing of the pig genome can be fully exploited by combining advanced genomics and metabolomics approaches. In this work we integrated several resources, experiments and data with the final aim to identify markers (DNA polymorphisms and metabolites) associated with production traits in Italian Large White pigs. High throughput genotyping was carried out using the Illumina Porcine60SNP BeadChip array and customized Golden Gate system on extreme and divergent pigs for back fat thickness (BFT) estimated breeding values (EBVs) (300-560 animals) and average daily gain (ADG) EBVs (360 pigs), chosen among a population of about 12,000 performance tested pigs. Next generation sequencing was carried out using the Ion Torrent PGM machine to identify single nucleotide polymorphisms (SNPs) from two reduced representation libraries developed from pooled genomic DNA constructed from 50 pigs with most positive and 50 pigs with most negative BFT EBVs, respectively. A total of 7,510,918 reads were produced and 447,031 SNPs were called, using stringent criteria. Genome wide association studies made it possible to identify a quite large number of significant SNPs affecting BFT, ADG and correlated traits. In addition, several genome regions containing significant SNPs for BFT were enriched of SNPs identified from the Next Generation Sequencing experiment. Metabolomics information was obtained from 800 performance tested pigs using a mass spectrometry (MS/MS) analytical pipeline to measure 180 blood plasma metabolites. Estimated heritability and correlation among all these parameters and production traits indicated that a few metabolites could be useful predictors of EBVs for production traits. All these data will be used to develop a first systems biology platform to understand the fine biological mechanisms affecting production traits in pigs.