Scrapie is a transmissible spongiform encephalopathy affecting sheep and goats. The prion protein-encoding gene (PRNP) plays a crucial role in determining susceptibility and resistance to scrapie. At the European level, surveillance of scrapie is essential to prevent the spread of the disease to livestock. According to the Regulation EU 2020/772 polymorphisms K222, D/S146 could function as resistance alleles in the genetic management of disease prevention. In Italy, a breeding plan for scrapie eradication has not been implemented for goats. However, surveillance plans based on the PRNP genotype have been developed as a preventive measure for scrapie. This research aimed to describe the polymorphisms at 7 positions within the PRNP gene in 956 goats of the Alpine, Saanen and mixed populations farmed in the Lombardy Region in Italy. PRNP polymorphisms were detected using single nucleotide polymorphism markers included in the Neogen GGP Goat 70 k chip. The K222 allele occurred in all populations, with frequencies ranging from 2.1 to 12.7%. No animals carried the S/D146 resistance allele. However, it has been demonstrated that polymorphisms in the other positions analysed could influence resistance or susceptibility to scrapie outbreaks in different ways. Ten potentially distinct haplotypes were found, and the most prevalent of the three populations was H2, which differed from the wild type (H1) in terms of mutation (S vs P) at codon 240. This study provided additional information on the genetic variability of the PRNP gene in these populations in the Lombardy region of Italy, contributing to the development of genetic control measures for disease prevention.

Copy Number Variants (CNV) are modifications affecting the genome sequence of DNA, for instance, they can be duplications or deletions of a considerable number of base pairs (i.e., greater than 1000 bp and up to millions of bp). Their impact on the variation of the phenotypic traits has been widely demonstrated. In addition, CNVs are a class of markers useful to identify the genetic biodiversity among populations related to adaptation to the environment. The aim of this study was to detect CNVs in more than four thousand Holstein cows, using information derived by a genotyping done with the GGP (GeneSeek Genomic Profiler) bovine 100K SNP chip. To detect CNV the SVS 8.9 software was used, then CNV regions (CNVRs) were detected. A total of 123,814 CNVs (4,150 non redundant) were called and aggregated into 1,397 CNVRs. The PCA results obtained using the CNVs information, showed that there is some variability among animals. For many genes annotated within the CNVRs, the role in immune response is well known, as well as their association with important and economic traits object of selection in Holstein, such as milk production and quality, udder conformation and body morphology. Comparison with reference revealed unique CNVRs of the Holstein breed, and others in common with Jersey and Brown. The information regarding CNVs represents a valuable resource to understand how this class of markers may improve the accuracy in prediction of genomic value, nowadays solely based on SNPs markers.

Advancements in precision livestock farming and machine learning have expanded the use of data-driven approaches for milk yield forecasting. In this study, a previously developed feedforward neural network (FFNN) model using genomic breeding values, parity, days in milk, month of calving, and age at calving as predictors was validated across one generation of Holstein cows. Specifically, the model was evaluated in first-parity daughters of the animals included in the original training population. Predictive performance was assessed on 228 lactation curves comprising 67,010 daily observations using a train–cross-validation–held-out test framework. On the test set, the model achieved a daily root mean squared error (RMSE) of 5.98 kg/day, with a Pearson correlation of 0.64. Sensitivity analyses were conducted by systematically shifting calving month and age (±1 to ±4 months) while holding other predictors constant. Simulated scenarios suggested increased predicted milk yield with later calving ages; however, these results reflect the structure of the training data rather than prescriptive management recommendations. While the FFNN provides robust milk yield predictions, its practical application for calving strategy decisions should be integrated with economic and reproductive considerations. Overall, the findings support the generational robustness of FFNN-based milk yield forecasting within the studied herd.