Single nucleotide polymorphisms (SNPs) able to describe population differences can be used for important applications in livestock, including breed assignment of individual animals, authentication of mono-breed products and parentage verification among several other applications. To identify the most discriminating SNPs among thousands of markers in the available commercial SNP chip tools, several methods have been used. Random forest (RF) is a machine learning technique that has been proposed for this purpose. In this study, we used RF to analyse PorcineSNP60 BeadChip array genotyping data obtained from a total of 2737 pigs of 7 Italian pig breeds (3 cosmopolitan-derived breeds: Italian Large White, Italian Duroc and Italian Landrace, and 4 autochthonous breeds: Apulo-Calabrese, Casertana, Cinta Senese and Nero Siciliano) to identify breed informative and reduced SNP panels using the mean decrease in the Gini Index and the Mean Decrease in Accuracy parameters with stability evaluation. Other reduced informative SNP panels were obtained using Delta, Fixation index and principal component analysis statistics, and their performances were compared with those obtained using the RF-defined panels using the RF classification method and its derived Out Of Bag rates and correct prediction proportions. Therefore, the performances of a total of six reduced panels were evaluated. The correct assignment of the animals to its breed was close to 100% for all tested approaches. Porcine chromosome 8 harboured the largest number of selected SNPs across all panels. Many SNPs were included in genomic regions in which previous studies identified signatures of selection or genes (e.g. ESR1, KITL and LCORL) that could contribute to explain, at least in part, phenotypically or economically relevant traits that might differentiate cosmopolitan and autochthonous pig breeds. Random forest used as preselection statistics highlighted informative SNPs that were not the same as those identified by other methods. This might be due to specific features of this machine learning methodology. It will be interesting to explore if the adaptation of RF methods for the identification of selection signature regions could be able to describe population-specific features that are not captured by other approaches.

Single nucleotide polymorphism (SNP) genotyping tools, which can analyse thousands of SNPs covering the whole genome, have opened new opportunities to estimate the inbreeding level of animals directly using genome information. One of the most commonly used genomic inbreeding measures considers the proportion of the autosomal genome covered by runs of homozygosity (ROH), which are defined as continuous and uninterrupted chromosome portions showing homozygosity at all loci. In this study, we analysed the distribution of ROH in three commercial pig breeds (Italian Large White, n = 1968; Italian Duroc, n = 573; and Italian Landrace, n = 46) and four autochthonous breeds (Apulo-Calabrese, n = 90; Casertana, n = 90; Cinta Senese, n = 38; and Nero Siciliano, n = 48) raised in Italy, using SNP data generated from Illumina PorcineSNP60 BeadChip. We calculated ROH-based inbreeding coefficients (FROH) using ROH of different minimum length (1, 2, 4, 8, 16 Mbp) and compared them with several other genomic inbreeding coefficients (including the difference between observed and expected number of homozygous genotypes (FHOM)) and correlated all these genomic-based measures with the pedigree inbreeding coefficient (FPED) calculated for the pigs of some of these breeds. Autochthonous breeds had larger mean size of ROH than all three commercial breeds. FHOM was highly correlated (0.671 to 0.985) with FROH measures in all breeds. Apulo-Calabrese and Casertana had the highest FROH values considering all ROH minimum lengths (ranging from 0.273 to 0.189 and from 0.226 to 0.152, moving from ROH of minimum size of 1 Mbp (FROH1) to 16 Mbp (FROH16)), whereas the lowest FROH values were for Nero Siciliano (from 0.072 to 0.051) and Italian Large White (from 0.117 to 0.042). FROH decreased as the minimum length of ROH increased for all breeds. Italian Duroc had the highest correlations between all FROH measures and FPED (from 0.514 to 0.523) and between FHOM and FPED (0.485). Among all analysed breeds, Cinta Senese had the lowest correlation between FROH and FPED. This might be due to the imperfect measure of FPED, which, mainly in local breeds raised in extensive production systems, cannot consider a higher level of pedigree errors and a potential higher relatedness of the founder population. It appeared that ROH better captured inbreeding information in the analysed breeds and could complement pedigree-based inbreeding coefficients for the management of these genetic resources.