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We used genome-wide SNP data from 18 local cattle breeds from six countries of the Alpine region to characterize population structure and identify genomic regions underlying positive selection. The geographically close breeds Evolèner, Eringer, Valdostana Pezzata Nera, and Valdostana Castana were found to differ from all other Alpine breeds. In addition, three breeds, Simmental, and Original Braunvieh from Switzerland and Pinzgauer from Austria built three separate clusters. Of the 18 breeds studied, the intra-alpine Swiss breed Evolèner had the highest average inbreeding based on runs of homozygosity (FROH) and the highest average genomic relationship within the breed. In contrast, Slovenian Cika cattle had the lowest average genomic inbreeding and the lowest average genomic relationship within the breed. We found selection signatures on chromosome 6 near known genes such as KIT and LCORL explaining variation in coat color and body size in cattle. The most prominent selection signatures were similar regardless of marker density and the breeds in the data set. In addition, using available high-density SNP data from 14 of the breeds we identified 47 genome regions as ROH islands. The proportion of homozygous animals was higher in all studied animals of local breeds than in Holstein and Brown Swiss cattle, the two most important commercial breeds in the Alpine region. We report ROH islands near genes related to thermoregulation, coat color, production, and stature. The results of this study serve as a basis for the search for causal variants underlying adaptation to the alpine environment and other specific characteristics selected during the evolution of local Alpine cattle breeds.

Stress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types of stress that negatively affects cattle. In addition, thanks to the arising of the Internet of Things in Animal Health, some researchers have proposed systems and models for the detection of this type of stress in an automated way, collecting and using data from meteorological variables (e.g., temperature, humidity), heart rate and others. However, the proposed models are mainly focused on heat stress detection that uses threshold-based estimation to determine the presence of stress; but, the level of stress experienced by cows can vary depending on their breed, or their ability to adapt to the environment where they are located. Therefore, in this project we propose an IoT platform for automatic detection of stress in cattle based on physiological signals; which is divided into three parts: i) implement a sensing device to collect physiological data, ii) a new method for automatic detection of stress based on physiological signals, and iii) an intuitive visualizer for monitoring cattle in individually way. The future research project, named PhyDac, is going to be carried out for two years with the participation of farmers from Peruvian regions (Arequipa, Cusco).

Additional file 5: Supplementary Figure S1 PCA and FST based on SNP genotypes.

Copy Number Variations (CNVs) have become promising markers, representing a major source of genomic variation. CNV involvement in phenotypic expression and in different diseases onset have been widely demonstrated in humans as well as in many domestic animals. However, this genomic investigation is still missing in Felis catus. This work is the first CNV mapping from a large data set of Whole Genome Sequencing (WGS) data in the domestic cat. A total of 42 cats of 14 different breeds were sequenced on the Illumina XTen (Washington University-St. Louis) which generated approximately 30-fold genome coverage from 150 paired-end reads (99 Lives Initiative). Maverix Biomics mapped the reads on the v6.2 reference assembly. CNV detection was performed using cn.mops and CNVnator, two Read Depth method software. One cat was excluded as outlier while, on the 41 remaining individuals, 1640 CNVs were detected by both the software and used to obtain 2891 CNVRs with BedTools. CNVRs covered the 0.4% of the total cat genome, with 136 loss, 127 gain and 26 complex detected (Fig. 1). A total of 164 singletons were identified and 9 CNVRs mapped in at least the 50% of the individuals. The number of CNVs in each cat ranged from 12 to 83. The clustering analysis of the detected CNVs was performed with R package “pvclust” and shows that same breed individuals cluster together. This study has led to the genetic characterization of 14 main cat breeds. Further analyses including other breeds and considering the genes located within these regions, could led to better evaluate the relationship between the presence of a specific CNV and a specific breed trait. This study can be considered a starting point for genomic CNV identification in the domestic cat, which could be further developed using the new released Felis catus vs9.0 reference aassembly., International Journal of Health, Animal Science and Food Safety, Vol 5, No 1s (2018)

Additional file 2: Table S1. Name of the breeds, geographic coordinates (longitude and latitude) of the center of origin, sample size before (n-PreQC) and after (n-PostQC) genotyping quality control, and origin of genotyping data.

Copy number variants (CNVs) are polymorphisms which influence phenotypic variation and are an important source of genetic variability [1]. In Mexico the backyard poultry population is a unique widespread Creole chicken (Gallus gallus domesticus) population, an undefined cross among different breeds brought to Mexico from Europe and under natural selection for almost 500 years [2-3]. The aim of this study was to investigate genomic variation in the Mexican chicken population using CNVs. A total of 256 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array were used in the analyses. The individual CNV calling, based on log-R ratio and B-allele frequency values, was performed using the Hidden Markov Model (HMM) of PennCNV software on the autosomes [4-5]. CNVs were summarized to CNV regions (CNVRs) at a population level (i.e. overlapping CNVs), using BEDTools. The HMM detected a total of 1924 CNVs in the genome of 256 samples resulting, at population level, in 1216 CNV regions, of which 959 gains, 226 losses and 31 complex CNVRs (i.e. containing both losses and gains), covering a total of 47 Mb of sequence length corresponding to 5,12 % of the chicken galGal4 assembly autosome. A comparison among this study and 7 previous reports about CNVs in chicken was performed, finding that the 1,216 CNVRs detected in this study overlap with 617 regions (51%) mapped by others studies. This study allowed a deep insight into the structural variation in the genome of unselected Mexican chicken population, which up to now has not been never genetically characterized with SNP markers. Based on a cluster analysis (pvclust – R package) on CNV markers the population, even if presenting extreme morphological variation, does not resulted divided in differentiated genetic subpopulations. Finally this study provides a CNV map based on the 600K SNP chip array jointly with a genome-wide gene copy number estimates in Mexican chicken population., International Journal of Health, Animal Science and Food Safety, V. 4, N. 1s (2017)

Background It is known that a paracrine mechanism exists between mesenchymal stem cells and target cells. This process may involve microvesicles (MVs) as an integral component of cell-to-cell communication. Methods In this context, this study aims to understand the efficacy of MVs in in-vitro endometrial stressed cells in view of potential healing in in-vivo studies. For this purpose, the presence and type of MVs secreted by amniotic mesenchymal stem cells (AMCs) were investigated and the response of endometrial cells to MVs was studied using a dose-response curve at different concentrations and times. Moreover, the ability of MVs to counteract the in vitro stress in endometrial cells induced by lipopolysaccharide was studied by measuring the rate of apoptosis and cell proliferation, the expression of some pro-inflammatory genes such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin 1β (IL-1β), and metalloproteinases (MMP) 1 and 13, and the release of some pro- or anti-inflammatory cytokines. Results MVs secreted by the AMCs ranged in size from 100 to 200 nm. The incorporation of MVs was gradual over time and peaked at 72 h. MVs reduced the apoptosis rate, increased cell proliferation values, downregulated pro-inflammatory gene expression, and decreased the secretion of pro-inflammatory cytokines. Conclusion Our data suggest that some microRNAs could contribute to counteracting in-vivo inflammation of endometrial tissue.

Genomic studies and their use in selection programs are having a strong impact in dairy cattle selection (E. Liu et al., 2010). The first aim was to create a high resolution map of CNV regions (CNVRs) in Brown Swiss cattle and the characterization of identified CNVs as markers for quantitative and population genetic studies. CNVs were called in a set of 164 sires with PennCNV and genoCN. PennCNV identified 2,377 CNVRs comprising 1,162 and 1,131 gain and loss events, respectively, and 84 regions of complex nature. GenoCN detected 41,519 CNVRs comprising 3,475 and 34,485 gain and loss events, respectively, and 3,559 regions of complex ones. Consensus calls between algorithms were summarized to CNVRs at the population level. GenoCN was also used to identify total allelic content in consensus CNVRs. Moreover, population haplotype frequencies were calculated. Linkage disequilibrium (LD) was established between CNVs and SNPs in and around CNVRs. In this study the potential contribution of CNVs as genetic markers for genome wide association studies (GWAS) has been assessed thanks to PIC and LD values. The next aim is to investigate genomic structural variation in cattle using dense SNP information in more than 1000 samples of the Italian and Swiss Brown Swiss breed genotyped on HD Bovine BeadChips. Today there is still no CNV map available across Brown Swiss populations belonging to different countries. This study therefore expands the catalogue of CNVRs in the bovine genome, delivers an international based high-resolution map of CNVRs specific to Brown Swiss dairy cattle and will lastly provide information for GEBV estimation with CNVs., International Journal of Health, Animal Science and Food Safety, Vol 2, No 1s (2015): Proceeding of Veterinary and Animal Science Days

The thesis identify CNV structural variants as possible markers for genomic selection and identify QTL regions for Fatty Acid Content in the Italian Brown Swiss population. Additionally it maps the QTL for mastitis resistance in the Valdostana Red Pied cattle.

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Advantages of using whole genome sequence data to predict genomic estimated breeding values (GEBV) include better persistence of accuracy of GEBV across generations and more accurate GEBV across breeds. The 1000 Bull Genomes Project provides a database of whole genome sequenced key ancestor bulls, for imputing sequence variant genotypes into reference sets for genomic prediction. Run 3.0 included 429 sequences, with 31.8 million variants detected. BayesRC, a new method for genomic prediction, addresses some challenges associated with using the sequence data, and takes advantage of biological information. In a dairy data set, predictions using BayesRC and imputed sequence data from 1000 Bull Genomes were 2% more accurate than with 800k data. We could demonstrate the method identified causal mutations in some cases. Further improvements will come from more accurate imputation of sequence variant genotypes and improved biological information

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