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Recent advances in next generation sequencing (NGS) technology provide a cost-effective approach to large-scale resequencing of livestock samples in order to study several biological phenomena. NGS produces millions of short DNA sequences that require an unbiased way to make possible comprehensive searches for variation to identify putative causative mutations for economically important traits. The aim of this work was to present a bioinformatics pipeline analysis for variants discovery in ovine genome. A total of 30 individuals belonging to Valle del Belice dairy ewes was used for whole genome sequencing of pooled libraries prepared using Illumina Nextera Kit. Paired-end sequencing was carried out in an 8-lanes flow-cell of the Illumina HiScanSQ platform yielding a total of 1,159,664,912, 101 bp length reads. The left and right raw reads were separated into two files, and converted to the fastq format using CASAVA 1.8. The whole procedure was split in different workflows, in order to give more flexibility to end-users. One workflow is aimed to verify the quality of the raw sequencing reads using FastQC and FASTX-Toolkit, in order to keep bases with Phred quality Score greater than 20 and to trim the reads with poor quality. Another step aligns the reads to the Ovis aries 3.1 reference genome using BWAmem with standard parameters. The resulting SAM file was converted in BAM file using the SAMtools software, then unmapped and duplicate reads were removed using the CleanSam and MarkDuplicate commands of the Picard software. Therefore, to get more accurate base qualities, Genetic Analysis Tool Kit (GATK) was used to locally realign reads such that the number of mismatching bases due to indels is minimized across all the reads (IndelRealigner) and to detect systematic errors in base quality scores (BaseRecalibrator). In the last workflow SNPs and indelsare identified using mpileup command of SAMtools software. The resulting BCF file is passed to “bcftools view” tool to be filtered and converted into VCF format. Finally, for variants annotation the SNPSift software was used. A total of 6,357,170 variations, of which 5,265,739 SNPs and 1,091,431 indels, were discovered. About 77% of the SNPs were present in the Ovis aries dbSNP v147 while the remaining were novel SNPs. The discovered SNPs must be validated and then could be used to several applications as phylogenic analysis, genome-wide association studies or genomic selection.

Copy Number Variations (CNVs) are classes of polymorphic genomic regions including deletions, duplications and insertions of DNA fragments from at least 0.5 kb up to several Mb. CNV represents an important source of genetic variability that provides genomics structural information complementary to the single nucleotide polymorphism (SNP) data. Some CNVs have been shown to be important in both normal phenotypic variability and disease susceptibility in livestock. Several approaches to identify CNVs including FISH, aCGH, SNP array or NGS, were proposed and among these SNP genotyping is relatively low cost, high-throughput and high coverage method. The aim of this study was to identify the CNVs in 71 animals of Cinisara breed using Illumina BovineSNP50 BeadChip v2. PennCNV software, which incorporates Log R ratio and B allele frequency at each SNP marker, was used to identify CNVs. Seven animals showed not shared CNVs, as well as autosomes 19, 21, 22. Chromosome 25 presented no CNVs at all. A final number of 322 CNVs were detected. The average number of CNVs was 4.5 per individual, with an average length and median size of 143.04 kb and 122.14 kb, respectively. All CNVs were grouped in CNV regions (CNVRs) and a total of 107 CNVRs, ranged from 50 to ~500 kb, were detected, which covered 4.90 Mb of polymorphic sequence and corresponded to 0.18% of the total genome length. In particular, we found 81 CNVRs with only gain (duplication), 22 with only loss (deletion), and four CNVRs with both. Furthermore, 8 CNVRs with >1%, 77 with >2.5%, and 22 with >5% frequency, were found. CNVRs having the highest frequency were located on Chr3:120501439-120647330 and Chr23:34673581-35007295, whereas the greatest number of genes was mapped in only one CNVR located on Chr 17:74123863-74393620. A total of 241 genes were included in the identified CNVRs. According to KEGG and DAVID database, most of the genes were involved in multiple signaling and signal transduction pathways in a wide variety of cellular and biochemical processes, such as immune response, adaptability, and olfactory receptors pathway. Further studies, using different algorithms and validating the CNVs discovered, will be conducted to corroborate these preliminary results on the CNVRs detected. These results will be used for the investigation of genomic changes and features of interest in the Cinisara breed, such as for association with functional or production traits and for biodiversity studies.

Several methods have been used to identify genomic regions subjected to selective sweeps. An alternative method can make use of runs of homozygosity (ROH), defined as stretches of homozygous regions in a genome. The aim of the present study was to detect selective sweeps using ROH in four cattle breeds. Individuals of Cinisara (71), Modicana (72),Reggiana (168) and Italian Holstein (96) were genotyped with the Illumina Bovine50SNP v2 BeadChip. To identify genomic regions that were most commonly associated with ROH within each breed, the percentage of occurrences of a single nucleotide polymorphism (SNP) in ROH was calculated across animals. The genomic regions most commonly associated with ROH were identified by selecting the top 1% of the SNPs most commonly observed in ROH in each breed. This approach resulted in the identification of 11 genomic regions in the Cinisara and Italian Holstein, and 8 in Modicana and Reggiana showing increased frequency of ROH. Generally, ROH patterns differed between breeds. There were two commons genomic regions between breed pairs, and in particular one in BTA6 between Modicana and Reggiana and one in BTA10 between Cinisara and Italian Holstein. A highly homozygous region (> 45% of individuals with ROH) was found only in Modicana breed in BTA6 (6:37,019,972- 39,069,719) within a QTL affecting milk fat and protein concentration. In these genomic regions we identified from 126 to 347 genes for each breed. According to Panther and KEGG database, a majority of the genes was involved in multiple signaling and signal transduction pathways in a wide variety of cellular and biochemical processes. Several of these genes were also comprised in a list of genes related to phenotypes for which cattle breeds have been subjected to strong positive selection. For most genes associated with ROH islands, a biological link to traits such as milk yield and composition, reproduction, immune response, coat colour, genetic disorders and resistance/susceptibility to infectious and diseases, which are known to be under selection, can be hypothesized. These results showed that selective sweeps detected with ROH approach are shared among breeds and that scanning the genome for ROH might be an alternative or complementary strategy to detect selective sweep related with important economically traits.

Over the last years, high-throughput technologies have provided the opportunity to explore the genomes of livestock species to identify regions influencing traits of economic interest. Genome wide association studies (GWAS) have been widely used to disentangle the genetic variation in complex phenotypes, such as milk production traits in sheep. The study presented here investigated the possible association of genome-wide SNPs and breeding values for milk production traits in the Valle del Belice sheep. The studied population consisted of a total of 481 ewes belonging to 9 half-sib families with available records for milk yield (MY), protein and fat percentage (P% and F%, respectively). All animals were genotyped for 54,241 SNPs, using the Illumina OvineSNP50K Genotyping BeadChip. During quality control, SNPs with minor allele frequency (MAF)

Mastitis is the most common and costly pathology affecting dairy animals. The identification of genetic markers that allow the inclusion of mastitis resistance in selection programs would help to reduce the costs due to the disease and the use of antibiotics. In recent years, β-defensin genes have been studied in several domestic species due to their important role in the innate immune response. The aim of this study was to determine whether β-defensin genes variability, β-defensin 1 (SBD1) and β-defensin 2 (SBD2), could distinguish between infected cases and healthy controls in Valle del Belice dairy sheep. Somatic cell count (SCC) is a powerful tool to identify animals with intramammary infection. Ewes were classified as affected by mastitis due to major (MAJP) and minor pathogens (MINP) within a lactation, based on having more than 645x103 or 2138x103 somatic cells, respectively, as reported in a previous study. Two single nucleotide polymorphisms (SNPs) in the SBD1 and five SNPs in the SBD2 genes were genotyped in 300 individuals by PCR and Single Nucleotide Primer Extension assay. All ewes had SCC test day registration. Association analysis were performed using the Case Control procedure in SAS 9.2, with the overall association with genotype based on the Armitage trend test and odd ratios (OR) based on allele counts, reflecting additive effects. Frequency differences indicated an association of the SNPs with the disease. On the basis of minor allele frequency for control group, both SNPs of SBD1 (A/G 1747 and T/C 1757) were associated with MAJP (P

The great achievements in livestock species selection during the last 50 years largely relied on quantitative genetic theory and infinitesimal genetic model. In the last 20 years, due to the application of advanced techniques in molecular genetics and statistics, several chromosomal regions that influence quantitative traits have been discovered. Combinations of molecular and classical quantitative information in a composite selection index have been proposed to increase the accuracy of selection. Nowadays, many genotyping arrays for thousands of SNPs are available for several livestock species, such as: cattle, sheep, pigs, horses, goat and chickens. The overall aim of this thesis is the comparison of different GWAS approaches to identify SNPs associated with milk production traits in Valle del Belice dairy sheep. In particular different genetic merit indices (breeding values and their deregressed and weighted values) and single test day observations will be evaluated to identify SNPs associated with milk yield (MY), fat yield (FY), fat percentage (F%), protein yield (PY) and protein percentage (P%) using different statistical approaches. The raw phenotypes data set included 5,586 observations of 481 ewes for MY, F%, FY, P% and PY traits. All animals were genotyped using the Illumina OvineSNP50K BeadChip. A single trait repeatability test-day animal model was performed to estimate the breeding values. The EBVs for MY, FY, F%, PY and P% estimated with the mixed model were deregressed (DEBV) and weighted (DEBVw) to obtain a more accurate estimate of the expected phenotype according to Garrick et al. (2009). Genome-wide association analysis was carried out based on regression of phenotypes (EBVs, DEBVs and DEBVw) with the genotypes of animals for one SNP at a time. For single-marker GWAS, we used a three-step approach referred to as genomic GRAMMAR-GC as implemented in GenABEL package. Other two approaches for genome wide association study were used. The first accounts the covariances between repeated measures for each individual using the RepeatABEL package was used. The last approach known as Regional Genomic Relationship Mapping or Regional Heritability Mapping (RHM) provides heritability estimates attributable to small genomic regions, and it has the power to detect regions containing multiple alleles that individually contribute too little variance to be detected by standard GWA studies. Comparison among the estimated breeding values and their deregressed and weighted values as responsible variables, respect to their influence on our GWAS results, has demonstrated that DEBVs and DEBVw allow identifying a greater numbers of SNPs than using EBVs. Several SNPs using different approaches were identified and some of these SNPs are mapped within the previously reported QTL regions and within candidate genes for milk production traits. The results confirmed the roles of LALBA gene and AQP genes, on OAR 3, as candidate genes for milk production traits in sheep. Moreover some genomic regions identified by close SNPs associated with a specific trait should be further investigated to verify their effect on the traits. The general consistence of the significant SNPs detected herein with the reported QTL and candidate genes for milk traits allow us to be confident of the results obtained. The information generated from this thesis has important implications for the design and applications of association studies as well as for the development of selection breeding programs for the Valle del Belice sheep breeds.

Copy number variation (CNV) is a major source of genomic structural variation. The aim of this study was to detect genomic CNV regions (CNVR) in Valle del Belice dairy sheep population and to identify those affecting milk production traits. The GO analysis identified possible candidate genes and pathways related to the selected traits. We identified CNVs in 416 individuals genotyped using the Illumina OvineSNP50 BeadChip array. The CNV association using a correlation-trend test model was examined with the Golden Helix SVS 8.7.0 tool. Significant CNVs were detected when their adjusted p-value was

The knowledge of milk proteome has been greatly enhanced by technological advances in the proteomics field as the use of the two-dimensional differential in-gel electrophoresis, a gel-based approach which allowed the analysis of proteins from complex mixtures and the comparing of several protein samples in the same experiment. The aim of this study was to characterise the whole milk proteomic profile in Girgentana dairy goat breed by two-dimensional differential in-gel elecrophoresis. The obtained representative 2D whole milk proteomic map showed a general picture of the protein distributions over the pH 3–10 NL including about 100 spots, most of them organised like a spot train. Among differentially abundant spots in the three experimental conditions, milk fat globule EGF factor 8 protein, β-lactoglobulin, β-casein and serum albumin were successfully identified. The three-dyes system employed in two-dimensional differential in-gel electrophoresis (2D-DIGE) analysis allowed us to obtain a global representation of the Girgentana whole milk proteome. These preliminary results could be used to generate a milk reference proteomics map for the Girgentana goat breed.

The somatic cell count (SCC) of milk is widely used to monitor udder health and the milk quality and because of its positive genetic correlation with mastitis this trait was included in breeding schemes of dairy sheep. The aim of this study was to estimate the loss in milk yield (MY) and related composition resulting from different levels of somatic cell count in Valle del Belice dairy sheep. Data were collected between 2006 and 2016 in 15 flocks following an A4 recording scheme. Somatic cell count (SCC), fat and protein percentage (F% and P%) were determined using mid-infrared spectroscopy. To evaluate loss in test day MY, F% and P%, five different classes of SCC were arbitrarily defined: SCC1 ≤ 500 × 103, 500 × 103< SCC2 ≤ 1000 × 103, 1000 × 103< SCC3 ≤ 1500 × 103, 1500 × 103< SCC4 ≤ 2000 × 103and SCC5 > 2000 × 103. To estimate the loss of milk production and quality a linear model, with test day milk production traits as dependent variable, was used. Furthermore, the effect of order of parity and season of lambing were investigated to study the effects on milk production traits. Least squares means were computed for milk production traits and the differences between means were determined by Fischer's least significant difference. The estimated losses in MY according to the level of used SCC were approximately 16% whereas there was an increase of 0.06% and 0.29% for fat and protein percentage, respectively. Apart from environmental factors and management, this study confirms that high levels of somatic cell count in sheep milk are associated with milk yield losses and changes in milk composition. Results suggest that it is necessary to implement a program aimed to reduce the milk somatic cell count in ewes' milk, with the aim of improving the quality of ewes' milk and dairy products.

The aim of the present study was to identify copy-number variations (CNVs) in Cinisara (CIN) and Modicana (MOD) cattle breeds on the basis of signal intensity (logR ratio) and B allele frequency of each marker, using Illumina’s BovineSNP50K Genotyping BeadChip. The CNVs were detected with the PennCNV and SVS 8.7.0 software and were aggregated into CNV regions (CNVRs). PennCNV identified 487 CNVs in CIN that aggregated into 86 CNVRs, and 424 CNVs in MOD that aggregated into 81 CNVRs. SVS identified a total of 207 CNVs in CIN that aggregated into 39 CNVRs, and 181 CNVs in MOD that aggregated into 41 CNVRs. The CNVRs identified with the two softwares contained 29 common CNVRs in CIN and 17 common CNVRs in MOD. Only a small number of CNVRs identified in the present study have been identified elsewhere, probably because of the limitations of the array used. In total, 178 and 208 genes were found within the CNVRs of CIN and MOD respectively. Gene Ontology and KEGG pathway analyses showed that several of these genes are involved in milk production, reproduction and behaviour, the immune response, and resistance/susceptibility to infectious diseases. Our results have provided significant information for the construction of more-complete CNV maps of the bovine genome and offer an important resource for the investigation of genomic changes and traits of interest in the CIN and MOD cattle breeds. Our results will also be valuable for future studies and constitute a preliminary report of the CNV distribution resources in local cattle genomes.