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Commercial single nucleotide polymorphism (SNP) arrays have been recently developed for several species and can be used to identify informative markers to differentiate breeds or populations for several downstream applications. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this work, we compared several methods of SNPs preselection (Delta, F st and principal component analyses (PCA)) in addition to Random Forest classifications to analyse SNP data from six dairy cattle breeds, including cosmopolitan (Holstein, Brown and Simmental) and autochthonous Italian breeds raised in two different regions and subjected to limited or no breeding programmes (Cinisara, Modicana, raised only in Sicily and Reggiana, raised only in Emilia Romagna). From these classifications, two panels of 96 and 48 SNPs that contain the most discriminant SNPs were created for each preselection method. These panels were evaluated in terms of the ability to discriminate as a whole and breed-by-breed, as well as linkage disequilibrium within each panel. The obtained results showed that for the 48-SNP panel, the error rate increased mainly for autochthonous breeds, probably as a consequence of their admixed origin lower selection pressure and by ascertaining bias in the construction of the SNP chip. The 96-SNP panels were generally more able to discriminate all breeds. The panel derived by PCA-chrom (obtained by a preselection chromosome by chromosome) could identify informative SNPs that were particularly useful for the assignment of minor breeds that reached the lowest value of Out Of Bag error even in the Cinisara, whose value was quite high in all other panels. Moreover, this panel contained also the lowest number of SNPs in linkage disequilibrium. Several selected SNPs are located nearby genes affecting breed-specific phenotypic traits (coat colour and stature) or associated with production traits. In general, our results demonstrated the usefulness of Random Forest in combination to other reduction techniques to identify population informative SNPs.

A selective DNA pooling approach was applied to identify QTL for conjugated linoleic acid (CLA), vaccenic acid (VA) and Δ(9) -desaturase (D9D) milk content in Italian Brown Swiss dairy cattle. Milk samples from 60 animals with higher values (after correction for environmental factors) and 60 animals with lower values for each of these traits from each of five half-sib families were pooled separately. The pools were genotyped using the Illumina BovineSNP50 BeadChip. Sire allele frequencies were compared between high and low tails at the sire and marker level for SNPs for which the sires were heterozygous. An r procedure was implemented to perform data analysis in a selective DNA pooling design. A correction for multiple tests was applied using the proportion of false positives among all test results. BTA 19 showed the largest number of markers in association with CLA. Associations between SNPs and the VA and Δ(9) -desaturase traits were found on several chromosomes. A bioinformatics survey identified genes with an important role in pathways for milk fat and fatty acids metabolism within 1 Mb of SNP markers associated with fatty acids contents., (© 2014 Stichting International Foundation for Animal Genetics.)

This work aimed to confirm previously reported quantitative trait loci (QTL) affecting the somatic cell score (SCS) in dairy cattle on Bos taurus autosomes (BTA) 4 and 26. A granddaughter design with selective genotyping was implemented that included half-sib families from 12 male lines of Italian Holstein cattle. The animals were genotyped for 5 microsatellite markers each on regions of BTA 4 (average marker spacing 9.42 cM) and BTA 26 (average marker spacing 5.26 cM), previously reported by other authors as carrying QTL for somatic cell count. Quantitative trait loci analyses were performed using interval mapping by regressing sire breeding values for SCS onto genotype probabilities at 1-cM intervals along the 2 chromosome regions. Breeding values for SCS were estimated for the whole population using a test-day repeatability animal model. Results were not significant on a chromosome basis, but a possible QTL was found at BM4505 on BTA 26, confirming this region for further studies of QTL affecting SCS in the Italian Holstein population.