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Additional file 1: Table S1. Statistics for the window selection analysis. Table S2. Groups of breeds that were compared in this study. Table S3. Estimates of effective population size (Ne) over time (from 13 to 142 generations ago). Table S4. Single-SNP-based FST distances between pairs of rabbit populations. Table S5. Window-based FST distances between pairs of rabbit populations. Table S6. The most relevant results obtained from the PCAdapt analysis that overlap with those of the FST analyses. Table S8. Pearson���s correlations for genome windows FST values obtained from Method 1 and Method 2 (P-value

Additional file 2: Figure S1. Window-based Neighbor Joining tree. Figure S2. Multidimensional scaling plot. The first three components are provided. Figure S3. Scree plot used to identify the number of principal components that describe well the population structure of the investigated rabbit breeds. The plot displays in decreasing order the percentage of variance explained by each principal component. Figure S4. Manhattan plots of the PCAdapt analysis. Each dot represents a 350-kb genome window. The red line identifies the threshold value (0.1 Bonferroni corrected P-value). Unassembled scaffolds are not reported. Figure S5. Genome regions carrying signatures of selection (99.8th percentile; expanded windows) identified in the studied breeds. Only the assembled autosomes are presented and unassembled scaffolds are not reported. Figure S6. Manhattan plots of the genome-wide FST analyses based on Method 1 (M1). Each dot represents a 350-kb genome window. The blue line identifies the threshold value (99.8th percentile of the distribution). Unassembled scaffolds are not reported. Figure S7. Manhattan plots of the genome-wide FST analyses based on Method 2 (M2). Each dot represents a 350-kb genome window. The blue line identifies the threshold value (99.8th percentile of the distribution). Unassembled scaffolds are not reported.

In this study, we identified copy number variants (CNVs) in 19 European autochthonous pig breeds and in two commercial breeds (Italian Large White and Italian Duroc) that represent important genetic resources for this species. The genome of 725 pigs was sequenced using a breed-specific DNA pooling approach (30-35 animals per pool) obtaining an average depth per pool of 42×. This approach maximized CNV discovery as well as the related copy number states characterizing, on average, the analysed breeds. By mining more than 17.5 billion reads, we identified a total of 9592 CNVs (~683 CNVs per breed) and 3710 CNV regions (CNVRs; 1.15% of the reference pig genome), with an average of 77 CNVRs per breed that was considered as private. A few CNVRs were analysed in more details, together with other information derived from sequencing data. For example, the CNVR encompassing the KIT gene was associated with coat colour phenotypes in the analysed breeds, confirming the role of the multiple copies in determining breed-specific coat colours. The CNVR covering the MSRB3 gene was associated with ear size in most breeds. The CNVRs affecting the ELOV6 and ZNF622 genes were private features observed in the Lithuanian Indigenous Wattle and in the Turopolje pig breeds, respectively. Overall, genome variability here unravelled can explain part of the genetic diversity among breeds and might contribute to explain their origin, history and adaptation to a variety of production systems.

The aim of this work was to identify markers associated with production traits in the pig genome using different approaches. We focused the attention on Italian Large White pig breed using Genome Wide Association Studies (GWAS) and applying a selective genotyping approach to increase the power of the analyses. Furthermore, we searched the pig genome using Next Generation Sequencing (NSG) Ion Torrent Technology to combine selective genotyping approach and deep sequencing for SNP discovery. Other two studies were carried on with a different approach. Allele frequency changes for SNPs affecting candidate genes and at Genome Wide level were analysed to identify selection signatures driven by selection program during the last 20 years. This approach confirmed that a great number of markers may affect production traits and that they are captured by the classical selection programs. GWAS revealed 123 significant or suggestively significant SNP associated with Back Fat Thickenss and 229 associated with Average Daily Gain. 16 Copy Number Variant Regions resulted more frequent in lean or fat pigs and showed that different copies of those region could have a limited impact on fat. These often appear to be involved in food intake and behavior, beside affecting genes involved in metabolic pathways and their expression. By combining NGS sequencing with selective genotyping approach, new variants where discovered and at least 54 are worth to be analysed in association studies. The study of groups of pigs undergone to stringent selection showed that allele frequency of some loci can drastically change if they are close to traits that are interesting for selection schemes. These approaches could be, in future, integrated in genomic selection plans.

Application of genomics (next generation sequencing and high throughput genotyping) and metabolomics technologies in farm animals are opening new opportunities for the identification of genetic factors affecting traits of economic relevance. In this study we combined several resources and data with the final aim to identify DNA polymorphisms and metabolites associated with production traits in Italian heavy pigs. Two genome wide association studies were carried out using a selective genotyping approach in Italian Large White pigs based on extreme and divergent estimated breeding values (EBVs) for average daily gain (ADG) and back fat thickness (BFT). Next generation sequencing was carried out using the Ion Torrent technology to identify single nucleotide polymorphisms (SNPs) from two reduced representation libraries constructed from pigs with extreme BFT EBVs. Metabolomics information was obtained using a mass spectrometry (MS/MS) analytical pipeline in a performance tested population. Integration of these data made it possible to identify markers (SNPs, copy number variation and metabolites) associated with ADG, BFT and several other correlated traits.

The development of high throughput genomics (next generation sequencing and high throughput genotyping) and metabolomics platforms has opened new perspectives for the identification of the genetics factors affecting traits of biological relevance in all species, including production traits in farm animals. In pigs, benefits derived from the recent sequencing of the pig genome can be fully exploited by combining advanced genomics and metabolomics approaches. In this work we integrated several resources, experiments and data with the final aim to identify markers (DNA polymorphisms and metabolites) associated with production traits in Italian Large White pigs. High throughput genotyping was carried out using the Illumina Porcine60SNP BeadChip array and customized Golden Gate system on extreme and divergent pigs for back fat thickness (BFT) estimated breeding values (EBVs) (300-560 animals) and average daily gain (ADG) EBVs (360 pigs), chosen among a population of about 12,000 performance tested pigs. Next generation sequencing was carried out using the Ion Torrent PGM machine to identify single nucleotide polymorphisms (SNPs) from two reduced representation libraries developed from pooled genomic DNA constructed from 50 pigs with most positive and 50 pigs with most negative BFT EBVs, respectively. A total of 7,510,918 reads were produced and 447,031 SNPs were called, using stringent criteria. Genome wide association studies made it possible to identify a quite large number of significant SNPs affecting BFT, ADG and correlated traits. In addition, several genome regions containing significant SNPs for BFT were enriched of SNPs identified from the Next Generation Sequencing experiment. Metabolomics information was obtained from 800 performance tested pigs using a mass spectrometry (MS/MS) analytical pipeline to measure 180 blood plasma metabolites. Estimated heritability and correlation among all these parameters and production traits indicated that a few metabolites could be useful predictors of EBVs for production traits. All these data will be used to develop a first systems biology platform to understand the fine biological mechanisms affecting production traits in pigs.

Next generation sequencing (NGS) is changing the way to analyse and extract genetic information from all species. One of the most promising NGS platforms is represented by the Ion Torrent PGM (Personal Genome Machine) technology. The sequencing process of this machine is based on the detection of pH variation which occurs when H+ is released during a nucleotide incorporation in the sequencing reaction. This chemical signal is then converted directly to a digital information. In this study we applied the Ion Torrent PGM technology to identify single nucleotide polymorphisms (SNPs) in the rabbit genome, a species for which massive SNP information is not available yet. Two reduced representation libraries (RRLs) were prepared. Genomic DNA pools were constructed with equimolar DNA of 10 rabbits from 4 breeds (Burgundy Fawn, Champagne d’Argent, Checkered Giant and Rhinelander) and from a commercial line. Pooled DNA was digested separately with two different restriction enzymes (HaeIII) and (RsaI). Digested DNA was electrophoresed on agarose gels from which a slice containing DNA fragments of about 500-600 bp was cut out and used to extract DNA. The isolated DNA was then prepared for the sequencing on 318 Ion Torrent chips following manufacturer instructions. From the two RRLs a total of 697.79 Mb (609.38 Mb with base quality > 20), derived from 6,964,750 reads (with a mean length of 100 bp) were sequenced. Of these reads, 6,312,660 were mapped on the reference rabbit genome sequence (oryCun2.0) and then used for variant calling analysis. Retaining only single nucleotide variation with a mapping quality >10 and detected in at least 4 bases, 65,695 SNPs were identified, with a mean distribution of 1 SNP every 287 bp. Annotation of these SNPs was based on the oryCun2.0 genome version. Some of the putative SNPs were validated by visual inspection using IGV (Integrative Genomics Viewer) and by Sanger sequencing. These SNPs could be useful to design a commercial SNP genotyping platform for the rabbit.

Combining different approaches (resequencing of portions of 54 obesity candidate genes, literature mining for pig markers associated with fat deposition or related traits in 77 genes, and in silico mining of porcine expressed sequence tags and other sequences available in databases), we identified and analyzed 736 SNP within candidate genes to identify markers associated with back fat thickness (BFT) in Italian Large White sows. Animals were chosen using a selective genotyping approach according to their EBV for BFT (276 with most negative and 279 with most positive EBV) within a population of similar to 12,000 pigs. Association analysis between the SNP and BFT has been carried out using the MAX test proposed for case-control studies. The designed assays were successful for 656 SNP: 370 were excluded (low call rate or minor allele frequency A polymorphism (P-nominal < 1.0E-50). The second most significant SNP was the MC4R c.1426A>G polymorphism (P-nominal = 8.0E-05). The third top SNP (P-nominal = 6.2E04) was the intronic TBC1D1 g.219G>A polymorphic site, in agreement with our previous results obtained in an independent study. The list of significant markers also included SNP in additional genes (ABHD16A, ABHD5, ACP2, ALMS1, APOA2, ATP1A2, CALR, COL14A1, CTSF, DARS, DECR1, ENPP1, ESR1, GH1, GHRL, GNMT, IKBKB, JAK3, MTTP, NFKBIA, NT5E, PLAT, PPARG, PPP2R5D, PRLR, RRAGD, RFC2, SDHD, SERPINF1, UBE2H, VCAM1, and WAT). Functional relationships between genes were obtained using the Ingenuity Pathway Analysis (IPA) Knowledge Base. The top scoring pathway included 19 genes with a P-nominal < 0.1, 2 of which (IKBKB and NFKBIA) are involved in the hypothalamic IKK beta/NF kappa B program that could represent a key axis to affect fat deposition traits in pigs. These results represent a starting point to plan marker-assisted selection in Italian Large White nuclei for BFT. Because of similarities between humans and pigs, this study might also provide useful clues to investigate genetic factors affecting human obesity.

Copy number variants (CNVs) are a major source of genetic variability in mammalian genomes. CNVs are involved in many human disorders, including obesity. For several biological reasons pig could be a biomedical model for human obesity and associated diseases. Fat deposition is a key process with practical and economical implications in pig breeding. This trait determines carcass value and consumers’ acceptance of pork. In this study we applied a selective genotyping approach to identify CNVs associated with backfat thickness (BFT) in Italian Large White pigs. Pigs with extreme and divergent estimated breeding values (EBVs) for BFT were selected among a performance-tested population of ≈12,000 animals and genotyped with the Illumina PorcineSNP60k Beadchip. CNVs were called using pennCNV using strict criteria. Fifteen copy number variation regions (CNVRs) (in at least 4 pigs) were present only in the positive BFT-EBV group whereas 12 CNVRs were reported only in the negative BFT-EBV tail. Other CNVRs differed in frequency (P < 0.05) between the tails. Identified CNVRs include genes involved in fat metabolism, growth regulation, immune system, and neuronal regulation of eating behavior. These results provide additional insights into mechanisms affecting fat deposition in pigs

Additional file 2: Figure S1. Evaluation of the D-statistics for the Kolmogorov–Smirnov test. Figure S2. Selection of the window size. (a) The number of windows with less than 10 SNPs over windows of variable size (in the range from 50 to 300-kb) is presented. Red dots represent windows larger than 100 kb, for which the number of windows with less than 10 SNPs started to asymptotically decrease. (b to d) Distribution of the number of SNPs contained in the 50-, 100- and 150-kb windows, respectively. Figure S3. FST based Neighbour-Joining tree. Next to the branches, the bootstrap test values expressed as percentage over 10,000 replicates are indicated in red. Figure S4. Mantel test between FST distance and the geographical distances (based on longitudinal and latitudinal coordinates) among autochthonous pig populations. Figure S5. Manhattan plots of the genome-wide HP analyses. Each dot represents a 100-kb genome window. Figure S6. Manhattan plots of the genome-wide FST analyses. Each dot represents a 100-kb genome window. Figure S7. Manhattan plots of the genome-wide FST analysis of breed groups Each dot represents a 100-kb genome window. Figure S8. Allele frequencies of SNPs in putative regions of signatures of selection detected in the FST analysis of middle vs large-sized pig breeds. Major signals were detected on: (a) SSC15 that carries the CASP10 gene, (b) SSC1 that carries the ARID1B gene, (c) SSC1 that carries the MAP3K5 gene (and the nearby PEX7) and (d) SSC2 that carries the PIK3C2A gene.

Additional file 1: Table S1. Details on the animals analysed and breeds investigated, including geographical distribution and phenotypic description. Table S2. Summary of whole-genome sequencing statistics. Table S3. Statistics on SNPs detected in this study. Table S4. Statistics on annotated SNPs. Annotation was performed with the Variant Effect Predictor (VEP) tool. Table S5. Statistics on the window selection analysis. Table S6. Groups of breeds/populations compared in the current study. Table S7. Statistics of the genome-wide window-based heterozygosity (HP) values and fixation index (FST) values. Table S8. Statistics of the genome-wide FST values between groups of pig breeds/populations based on 100-kb windows. Table S9. Pearson’s correlation coefficient (r) based on the frequency of the alternative allele. Table S10. Single SNP FST distances between pairs of pig populations. Table S11. Within-breed average pooled heterozygosity (HP) and fixation index (FST) values. Table S12. HP analysis. The genome windows at the extreme lower end of the distributions (99.95th percentile) are presented. Table S13. Single-breed FST analysis. The genome windows at the extreme lower end of the distributions (99.95th percentile) are presented. Table S14. Comparative FST analysis of breed groups. The genome windows at the extreme lower end of the distributions (99.95th percentile) are presented. Table S15. Putative deleterious variants that showed a marked allele frequency difference between pig breeds and wild boars (> v80% in one group

Additional file 1: Table S1. Details on the animals analysed and breeds investigated, including geographical distribution and phenotypic description. Table S2. Summary of whole-genome sequencing statistics. Table S3. Statistics on SNPs detected in this study. Table S4. Statistics on annotated SNPs. Annotation was performed with the Variant Effect Predictor (VEP) tool. Table S5. Statistics on the window selection analysis. Table S6. Groups of breeds/populations compared in the current study. Table S7. Statistics of the genome-wide window-based heterozygosity (HP) values and fixation index (FST) values. Table S8. Statistics of the genome-wide FST values between groups of pig breeds/populations based on 100-kb windows. Table S9. Pearson’s correlation coefficient (r) based on the frequency of the alternative allele. Table S10. Single SNP FST distances between pairs of pig populations. Table S11. Within-breed average pooled heterozygosity (HP) and fixation index (FST) values. Table S12. HP analysis. The genome windows at the extreme lower end of the distributions (99.95th percentile) are presented. Table S13. Single-breed FST analysis. The genome windows at the extreme lower end of the distributions (99.95th percentile) are presented. Table S14. Comparative FST analysis of breed groups. The genome windows at the extreme lower end of the distributions (99.95th percentile) are presented. Table S15. Putative deleterious variants that showed a marked allele frequency difference between pig breeds and wild boars (> v80% in one group

Additional file 2: Figure S1. Evaluation of the D-statistics for the Kolmogorov–Smirnov test. Figure S2. Selection of the window size. (a) The number of windows with less than 10 SNPs over windows of variable size (in the range from 50 to 300-kb) is presented. Red dots represent windows larger than 100 kb, for which the number of windows with less than 10 SNPs started to asymptotically decrease. (b to d) Distribution of the number of SNPs contained in the 50-, 100- and 150-kb windows, respectively. Figure S3. FST based Neighbour-Joining tree. Next to the branches, the bootstrap test values expressed as percentage over 10,000 replicates are indicated in red. Figure S4. Mantel test between FST distance and the geographical distances (based on longitudinal and latitudinal coordinates) among autochthonous pig populations. Figure S5. Manhattan plots of the genome-wide HP analyses. Each dot represents a 100-kb genome window. Figure S6. Manhattan plots of the genome-wide FST analyses. Each dot represents a 100-kb genome window. Figure S7. Manhattan plots of the genome-wide FST analysis of breed groups Each dot represents a 100-kb genome window. Figure S8. Allele frequencies of SNPs in putative regions of signatures of selection detected in the FST analysis of middle vs large-sized pig breeds. Major signals were detected on: (a) SSC15 that carries the CASP10 gene, (b) SSC1 that carries the ARID1B gene, (c) SSC1 that carries the MAP3K5 gene (and the nearby PEX7) and (d) SSC2 that carries the PIK3C2A gene.

Runs of homozygosity (ROH) are defined as long stretches of DNA homozygous at each polymorphic position. The proportion of genome covered by ROH and their length are indicators of the level and origin of inbreeding. In this study, we analysed SNP chip datasets (obtained using the Axiom OrcunSNP Array) of a total of 702 rabbits from 12 fancy breeds and four meat breeds to identify ROH with different approaches and calculate several genomic inbreeding parameters. The highest average number of ROH per animal was detected in Belgian Hare (~150) and the lowest in Italian Silver (~106). The average length of ROH ranged from 4.001 ± 0.556Mb in Italian White to 6.268 ± 1.355Mb in Ermine. The same two breeds had the lowest (427.9 ± 86.4 Mb, Italian White) and the highest (921.3 ± 179.8 Mb, Ermine) average values of the sum of all ROH segments. More fancy breeds had a higher level of genomic inbreeding (as defined by ROH) than meat breeds. Several ROH islands contain genes involved in body size, body length, pigmentation processes, carcass traits, growth, and reproduction traits (e.g.: AOX1, GPX5, IFRD1, ITGB8, NELL1, NR3C1, OCA2, TRIB1, TRIB2). Genomic inbreeding parameters can be useful to overcome the lack of information in the management of rabbit genetic resources. ROH provided information to understand, to some extent, the genetic history of rabbit breeds and to identify signatures of selection in the rabbit genome.

Background Domestication of the rabbit (Oryctolagus cuniculus) has led to a multi-purpose species that includes many breeds and lines with a broad phenotypic diversity, mainly for external traits (e.g. coat colours and patterns, fur structure, and morphometric traits) that are valued by fancy rabbit breeders. As a consequence of this human-driven selection, distinct signatures are expected to be present in the rabbit genome, defined as signatures of selection or selective sweeps. Here, we investigated the genome of three Italian commercial meat rabbit breeds (Italian Silver, Italian Spotted and Italian White) and 12 fancy rabbit breeds (Belgian Hare, Burgundy Fawn, Champagne d’Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex, Rhinelander and Thuringian) by using high-density single nucleotide polymorphism data. Signatures of selection were identified based on the fixation index (FST) statistic with different approaches, including single-breed and group-based methods, the latter comparing breeds that are grouped based on external traits (different coat colours and body sizes) and types (i.e. meat vs. fancy breeds). Results We identified 309 genomic regions that contained signatures of selection and that included genes that are known to affect coat colour (ASIP, MC1R and TYR), coat structure (LIPH), and body size (LCORL/NCAPG, COL11A1 and HOXD) in rabbits and that characterize the investigated breeds. Their identification proves the suitability of the applied methodologies for capturing recent selection events. Other regions included novel candidate genes that might contribute to the phenotypic variation among the analyzed breeds, including genes for pigmentation-related traits (EDNRA, EDNRB, MITF and OCA2) and body size, with a strong candidate for dwarfism in rabbit (COL2A1). Conclusions We report a genome-wide view of genetic loci that underlie the main phenotypic differences in the analyzed rabbit breeds, which can be useful to understand the shift from the domestication process to the development of breeds in O. cuniculus. These results enhance our knowledge about the major genetic loci involved in rabbit external traits and add novel information to understand the complexity of the genetic architecture underlying body size in mammals.

Awareness has been raised over the last years on the genetic integrity of autochthonous honey bee subspecies. Genomic tools available in Apis mellifera can make it possible to measure this information by targeting individual honey bee DNA. Honey contains DNA traces from all organisms that contributed or were involved in its production steps, including the honey bees of the colony. In this study, we designed and tested a genotyping by sequencing (GBS) assay to analyse single nucleotide polymorphisms (SNPs) of A. mellifera nuclear genome using environmental DNA extracted from honey. A total of 121 SNPs (97 SNPs informative for honey bee subspecies identification and 24 SNPs associated with relevant traits of the colonies) were used in the assay to genotype honey DNA, which derives from thousands of honey bees. Results were integrated with information derived from previous studies and whole genome resequencing datasets. This GBS method is highly reliable in estimating honey bee SNP allele frequencies of the whole colony from which the honey derived. This assay can be used to identify the honey bee subspecies of the colony that produced the honey and, in turn, to authenticate the entomological origin of the honey.

Epidemiological and biological characteristics of coronaviruses and their ability to cross species barriers are a matter of increasing concerns for these zoonotic agents. To prevent their spread, One Health approaches should be designed to include the host (animal) genome variability as a potential risk factor that might confer genetic resistance or susceptibility to coronavirus infections. At present, there is no example that considers cattle genetic resources for this purpose. In this study, we investigated the variability of six genes (ACE2, ANPEP, CEACAM1 and DPP4 encoding for host receptors of coronaviruses; FURIN and TMPRSS2 encoding for host proteases involved in coronavirus infection) by mining whole genome sequencing datasets from more than 500 cattle of 34 Bos taurus breeds and three related species. We identified a total of 180 protein variants (44 already known from the ARS-UCD1.2 reference genome). Some of them determine altered protein functions or the virus–host interaction and the related virus entry processes. The results obtained in this study constitute a first step towards the definition of a One Health strategy that includes cattle genetic resources as reservoirs of host gene variability useful to design conservation and selection programs to increase resistance to coronavirus diseases.

14 Pág. Departamento de ​Mejora Genética Animal, Coronaviruses silently circulate in human and animal populations, causing mild to severe diseases. Therefore, livestock are important components of a "One Health" perspective aimed to control these viral infections. However, at present there is no example that considers pig genetic resources in this context. In this study, we investigated the variability of four genes (ACE2, ANPEP and DPP4 encoding for host receptors of the viral spike proteins and TMPRSS2 encoding for a host proteinase) in 23 European (19 autochthonous and three commercial breeds and one wild boar population) and two Asian Sus scrofa populations. A total of 2229 variants were identified in the four candidate genes: 26% of them were not previously described; 29 variants affected the protein sequence and might potentially interact with the infection mechanisms. The results coming from this work are a first step towards a "One Health" perspective that should consider conservation programs of pig genetic resources with twofold objectives: (i) genetic resources could be reservoirs of host gene variability useful to design selection programs to increase resistance to coronaviruses; (ii) the described variability in genes involved in coronavirus infections across many different pig populations might be part of a risk assessment including pig genetic resources., This work has received funding from the University of Bologna RFO 2016-2019 programmes, the Italian MIUR 2017 PigPhenomics project, from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634476 for the project with acronym TREASURE, from the European Open Science Cloud (EOSC) Secretariat, project “Application of animal genomics and data mining to predict and monitor novel coronavirus potential infections (VirAnimalOne)”, the EGI call for COVID-19 research projects (AnGen1H project) and from the Por Fesr Emilia-Romagna 2014-2020 (actions 1.1.4 and 1.2.2—Bando per sostenere progetti di ricerca ed innovazione per lo sviluppo di soluzioni finalizzate al contrasto dell’epidemia da COVID-19—Project LIVESTOCK-STOP-COVI). The content of this article reflects only the authors' view and the European Union Agency is not responsible for any use that may be made of the information it contains.

Most of the Italian pig genetic resources that existed at the beginning of the last century have been substituted by more productive populations. At present, only six original autochthonous pig breeds are officially recognized in Italy and have their own herd books managed by the National Pig Breeders Association. Another section of the breed herd book includes breeds that were completely lost but that were subsequently reconstructed through specific crossbreeding programmes. This is the case of the Nero di Parma breed that was officially recognized as reconstructed breed in 2016. This is a small breed, reared in the province of Parma (North of Italy), that accounts for 103 and 12 registered sow and boars, respectively. In this study, using single nucleotide polymorphisms genotyped with the Illumina PorcineSNP60 BeadChip array, we compared, at the genome level, the reconstructed Nero di Parma pig breed with three Italian commercial (Italian Large White, Italian Duroc and Italian Landrace) and four autochthonous (Apulo-Calabrese, Casertana, Cinta Senese and Nero Siciliano) pig breeds. Nero di Parma showed the highest level of linkage disequilibrium among all analysed breeds. Effective population size estimated over past generations was the lowest in this reconstructed breed. However, genomic inbreeding parameters did not suggest a high level of inbreeding in Nero di Parma. The comparative analysis against several other Italian pig breeds made it possible to evaluate the obtained information that could be helpful to define potential future conservation and breeding strategies for this small reconstructed breed.

Honey is one of the most frauded food products. Therefore, it is important to develop new analytical systems useful for its authentication. Honey contains intrinsic markers that can be used to identify and monitor its origin, including plant DNA mainly derived by pollen. In this study, we applied a next generation sequencing approach for honey authentication by detecting the prevalent botanical contribution and botanical composition of honeys of different origin. DNA was isolated from nine honeys (six monofloral honeys produced in Italy, two polyfloral honeys produced in East Europe and Chile respectively, and one honeydew honey) and PCR amplified for a chloroplast trnL barcoding fragment. Obtained amplicons were sequenced using the Ion Torrent sequencing platform. Sequence data was interpreted using a customized bioinformatic pipeline that used a reference plant sequence dataset derived by more than 150,000 entries. A total of 254 botanical groups were identified from the nine analysed samples, ranging from 37 groups in orange tree blossom honey to 74 in eucalyptus tree blossom honey. The prevalent expected botanical origin was confirmed in five out of six monofloral honeys. The plant signature of the labelled lime tree blossom honey did not confirm the expected botanical prevalence. The most represented botanical group in the honeydew honey was Castanea. The botanical composition of monofloral and polyfloral honey samples was useful to infer their geographical origin. The metabarcoding based system applied in this study captured the botanical signature of all analysed honey samples and provided information useful for their authentication.

Runs of homozygosity (ROH) in a diploid organism can be defined as continuous chromosome regions in which all loci have a homozygous genotype. Shared ROH within a livestock population identify chromosome regions in which a reduced haplotype variability produces ROH islands. ROH islands can provide information on hotspot of selection putatively derived from different selection history, genetic events and adaptation to several production systems. In this study we evaluated the distribution of ROH in the genome of a total of 2860 pigs belonging to seven Italian breeds, three commercial breeds (Italian Large White, Italian Duroc and Italian Landrace) and four autochthonous breeds (Apulo-Calabrese, Casertana, Cinta Senese and Nero Siciliano). All animals were genotyped with the Illumina PorcineSNP60 BeadChip array. PLINK software was used to call ROH. The largest number of ROH per animal was observed in the Italian Duroc breed. The mean largest size of ROH was detected in Apulo-Calabrese pigs. Nero Siciliano pigs had the lowest mean number of ROH per animal. Italian Large White pigs had the lowest mean length of ROH. ROH islands were identified in all breeds except in Nero Siciliano. ROH islands spanned from a total of 25.5 (Cinta Senese) to 33.1 Mbp (Italian Landrace) of genomic regions distributed from four to ten autosomes and encompassing from a total of 126 to 262 annotated genes. These selection hotspot regions differed among breeds. Functional inference of the observed ROH islands provided some insights into the mechanisms of adaptation of these pig genetic resources.

Nero Siciliano (or Sicilian Black) is an Italian autochthonous pig breed reared in the Sicily island, mainly under extensive management systems. Nero Siciliano pigs are black (with black skin and black hair), but animals with white face or partially white face ("suino facciolo") can be registered to the breed herd book. Sometimes, other white patterns on extreme portions of legs could appear in this population. This study took advantage from the rare occurrence of pigs with white patterns in the Nero Siciliano population to carry out a genome-wide association study and comparative genome-wide Fixation index (FST) analysis to identify genomic regions that could affect coat colour variability (solid black vs white patterns over black) in this autochthonous pig breed. Analyses have been conducted on 66 Nero Siciliano pigs: 30 completely black and 36 black with white patterns. All samples have been genotyped for the KIT gene duplication and MC1R mutations, two genes well known to affect coat colours in pigs. Only pigs that did not carry any duplication of the KIT gene and were homozygous for the ED2 black dominant MC1R gene allele (n = 26 completely black and n. 22 with white patterns) were genotyped with the Illumina PorcineSNP60 BeadChip. The genome-wide analyzes identified on chromosome 2 a significant marker (rs81329493) associated with the coat colour white patterns in this breed. The homologous chromosome region in felids contains the gene responsible for the blotched tabby and striped coat colour patterns. Further studies, including a larger number of pigs, are needed to confirm this result and identify the causative mutation(s) affecting this coat colour diversity, which might be used to design a conservation programme in this breed aiming to maintain phenotypic homogeneity (i.e. solid black) that is typically associated with Nero Siciliano pigs. This study demonstrated how genetic diversity segregating in an autochthonous genetic resource can be explored to understand the genetic mechanisms affecting phenotypic traits in a livestock species.

Honeydew produced from the excretion of plant-sucking insects (order Hemiptera) is a carbohydrate-rich material that is foraged by honey bees to integrate their diets. In this study, we used DNA extracted from honey as a source of environmental DNA to disclose its entomological signature determined by honeydew producing Hemiptera that was recovered not only from honeydew honey but also from blossom honey. We designed PCR primers that amplified a fragment of mitochondrial cytochrome c oxidase subunit 1 (COI) gene of Hemiptera species using DNA isolated from unifloral, polyfloral and honeydew honeys. Ion Torrent next generation sequencing metabarcoding data analysis assigned Hemiptera species using a customized bioinformatic pipeline. The forest honeydew honeys reported the presence of high abundance of Cinara pectinatae DNA, confirming their silver fir forest origin. In all other honeys, most of the sequenced reads were from the planthopper Metcalfa pruinosa for which it was possible to evaluate the frequency of different mitotypes. Aphids of other species were identified from honeys of different geographical and botanical origins. This unique entomological signature derived by environmental DNA contained in honey opens new applications for honey authentication and to disclose and monitor the ecology of plant-sucking insects in agricultural and forest landscapes.

Casertana is a local pig breed mainly raised in Central-South regions of Italy. Pigs of this breed are considered the descendants of the ancient Neapolitan population that largely influenced the constitution of the modern commercial pigs. The pigs of this breed are usually curly-tailed, like several other domestic pig populations. However, Casertana population shows some variability for this trait, including animals having straight tail as observed in wild boars. In this study, we run, for the first time, a genome wide association study (GWAS) comparing the curly tailed (no. = 53) and straight tailed (no. = 19) Casertana pigs to identify genomic regions associated with the tail shape phenotype in Sus scrofa. All animals were genotyped with the Illumina PorcineSNP60 BeadChip v.2. GEMMA software was used in the GWAS for which we were able to correct for stratification in the analysed cohort. A single nucleotide polymorphism (rs81439488), located on porcine chromosome 12, was significantly associated with the investigated trait. This marker is close to the SRY-box 9 (SOX9) gene that encodes for a transcription factor that is required during sequential steps of the chondrocyte differentiation pathway, notochord maintenance and skeletogenesis. As the shape of the tail could be important in relation to the problem of tail biting in pigs, the obtained results might open new perspectives for defining selection programs answering indirectly animal welfare issues. This work demonstrated that autochthonous animal genetic resources might be used to disclose genetic factors affecting peculiar traits by exploiting segregating phenotypes and genetic variability.

Honey bees are considered large-scale monitoring tools due to their environmental exploration and foraging activities. Traces of these activities can be recovered in the honey that also may reflect the hive ecological micro-conditions in which it has been produced. This study applied a next generation sequencing platform (Ion Torrent) for shotgun metagenomic analysis of honey environmental DNA (eDNA). The study tested a methodological framework to interpret DNA sequence information useful to describe the complex ecosystems of the honey bee colony superorganism, its pathosphere and the heterogeneity of the agroecological environments and environmental sources that left DNA marks in the honey. Analysis of two honeys reported sequence reads from five main organism groups (kingdoms or phyla): arthropods (that mainly included reads from Apis mellifera, several other members of the Hymenotpera, in addition to members of the Diptera, Coleoptera and Lepidoptera, as well as aphids and mites), plants (that clearly confirmed the botanical origin of the two honeys, i.e. orange tree blossom and eucalyptus tree blossom honeys), fungi and bacteria (including common hive and honey bee gut microorganisms, honey bee pathogens and plant pathogens), and viruses (which accounted for the largest number of reads in both honeys, mainly assigned to Apis mellifera filamentous virus). The shotgun metagenomic approach that was used in this study can be applied in large scale experiments that might have multiple objectives according to the multi-kingdom derived eDNA that is contained in the honey.

In this study, we tested the Ion Torrent next generation semiconductor based sequencing technology for meat species identification in several highly processed and complex meat products and meat derived broths (a doner kebab, a beef/pork pate, a meat based filling of tortellini, one instantaneous granular preparation of broth stock made by meat and two ready to use meat broths from different producers). The detection protocol included the sequencing of targeted mitochondrial DNA (mtDNA) regions amplified with universal primer pairs and a bioinformatic pipeline designed to interpret sequencing results. Six libraries were sequenced producing a total of 1,363,351 filtered reads. Data mining detected expected and unexpected meat species in the analysed products. Pork was identified in the kebab and Bubalus bubalis DNA was identified in the beef/pork pate. For products for which the precise meat species ingredient information was not available, it was possible to obtain it. Human contamination based on human detected reads could be useful to evaluate the hygienic level of highly processed products. Mitochondrial haplotypes (mitotypes) were identified for several mtDNA-species combinations providing another level of information useful for the authentication of meat derived products. This work defined a methodological framework to establish assays using this sequencing platform for routine species identification in complex and highly processed food.

In this study, we applied a next generation sequencing (NGS) technology (Ion Torrent) for species identification based on three mitochondrial DNA (mtDNA) regions amplified on DNA extracted from dairy products. Sequencing reads derived from three libraries, obtained from artificial DNA pools or from pooled amplicons, were used to test the method. Then, sequencing results from five libraries obtained from two mixed goat and cow milk samples, one buffalo mozzarella cheese, one goat crescenza cheese and one artisanal cured ricotta cheese, were able to detect all expected species in addition to undeclared species in a few of them. Mining generated reads it was possible to identify different dairy species mitotypes and the presence of human DNA that could constitute a potential marker to monitor the hygienic level of dairy products. Overall results demonstrated the usefulness of NGS for species identification in food products and its possible application for food authentication.

The GPR120 gene (also known as FFAR4 or O3FAR1) encodes for a functional omega-3 fatty acid receptor/sensor that mediates potent insulin sensitizing effects by repressing macrophage-induced tissue inflammation. For its functional role, GPR120 could be considered a potential target gene in animal nutrigenetics. In this work we resequenced the porcine GPR120 gene by high throughput Ion Torrent semiconductor sequencing of amplified fragments obtained from 8 DNA pools derived, on the whole, from 153 pigs of different breeds/populations (two Italian Large White pools, Italian Duroc, Italian Landrace, Casertana, Pietrain, Meishan, and wild boars). Three single nucleotide polymorphisms (SNPs), two synonymous substitutions and one in the putative 3'-untranslated region (g.114765469C > T), were identified and their allele frequencies were estimated by sequencing reads count. The g.114765469C > T SNP was also genotyped by PCR-RFLP confirming estimated frequency in Italian Large White pools. Then, this SNP was analyzed in two Italian Large White cohorts using a selective genotyping approach based on extreme and divergent pigs for back fat thickness (BFT) estimated breeding value (EBV) and average daily gain (ADG) EBV. Significant differences of allele and genotype frequencies distribution was observed between the extreme ADG-EBV groups (P < 0.001) whereas this marker was not associated with BFT-EBV.

Shot-gun next generation sequencing (NGS) on whole DNA extracted from specimens collected from mammals often produces reads that are not mapped (i.e. unmapped reads) on the host reference genome and that are usually discarded as by-products of the experiments. In this study, we mined Ion Torrent reads obtained by sequencing DNA isolated from archived blood samples collected from 100 performance tested Italian Large White pigs. Two reduced representation libraries were prepared from two DNA pools constructed each from 50 equimolar DNA samples. Bioinformatic analyses were carried out to mine unmapped reads on the reference pig genome that were obtained from the two NGS datasets. In silico analyses included read mapping and sequence assembly approaches for a viral metagenomic analysis using the NCBI Viral Genome Resource. Our approach identified sequences matching several viruses of the Parvoviridae family: porcine parvovirus 2 (PPV2), PPV4, PPV5 and PPV6 and porcine bocavirus 1-H18 isolate (PBoV1-H18). The presence of these viruses was confirmed by PCR and Sanger sequencing of individual DNA samples. PPV2, PPV4, PPV5, PPV6 and PBoV1-H18 were all identified in samples collected in 1998-2007, 1998-2000, 1997-2000, 1998-2004 and 2003, respectively. For most of these viruses (PPV4, PPV5, PPV6 and PBoV1-H18) previous studies reported their first occurrence much later (from 5 to more than 10 years) than our identification period and in different geographic areas. Our study provided a retrospective evaluation of apparently asymptomatic parvovirus infected pigs providing information that could be important to define occurrence and prevalence of different parvoviruses in South Europe. This study demonstrated the potential of mining NGS datasets non-originally derived by metagenomics experiments for viral metagenomics analyses in a livestock species.

Microbiota plays an important role in the homeostasis of the gastrointestinal tract. Understanding the variations of the commensal microbiota composition is crucial for a more efficient control of enteric infectious diseases and for the reduction of the use of antibiotics in animal production, which are the main points of interest for improved animal healthcare and welfare and for consumer health protection. Even though the intestinal microbiota has been extensively studied, little is known about the gastric microbiota. This pilot study was aimed at a descriptive analysis of the gastric microbiota in healthy pigs and at the identification of any differences among four potentially distinct microbial niches in the stomach. Gastric mucosal samples from the oxyntic area, the pylorus and the gastric groove, and a sample of gastric contents were collected from four healthy weaned pigs. Bacterial DNA was isolated and extracted from each sample and amplicons from the V6 region of the 16S rRNA gene were sequenced using Ion Torrent PGM. The data were analysed by an "unsupervised" and a "supervised" approach in the Ribosomal Database Project (RDP) pipeline. Proteobacteria was the dominant phylum in all the samples. Differences in bacterial community composition were found between mucosal and content samples (one-way ANOSIM pairwise post hoc test, p < 0.05); instead, the different mucosal regions did not show differences between them. The mucosal samples were characterised by Herbiconiux and Brevundimonas, two genera which include cellulolytic and xylanolytic strains. Nevertheless, additional larger trials are needed to support the data presented in this pilot study and to increase the knowledge regarding the resident microbiota of the stomach.

Nuclear DNA sequences of mitochondrial origin (numts) are derived by insertion of mitochondrial DNA (mtDNA), into the nuclear genome. In this study, we provide, for the first time, a genome picture of numts inserted in the pig nuclear genome. The Sus scrofa reference nuclear genome (Sscrofa10.2) was aligned with circularized and consensus mtDNA sequences using LAST software. A total of 430 numt sequences that may represent 246 different numt integration events (57 numt regions determined by at least two numt sequences and 189 singletons) were identified, covering about 0.0078% of the nuclear genome. Numt integration events were correlated (0.99) to the chromosome length. The longest numt sequence (about 11 kbp) was located on SSC2. Six numts were sequenced and PCR amplified in pigs of European commercial and local pig breeds, of the Chinese Meishan breed and in European wild boars. Three of them were polymorphic for the presence or absence of the insertion. Surprisingly, the estimated age of insertion of two of the three polymorphic numts was more ancient than that of the speciation time of the Sus scrofa, supporting that these polymorphic sites were originated from interspecies admixture that contributed to shape the pig genome.

The aim of this study was to identify single nucleotide polymorphisms (SNPs) that could be associated with back fat thickness (BFT) in pigs. To achieve this goal, we evaluated the potential and limits of an experimental design that combined several methodologies. DNA samples from two groups of Italian Large White pigs with divergent estimating breeding value (EBV) for BFT were separately pooled and sequenced, after preparation of reduced representation libraries (RRLs), on the Ion Torrent technology. Taking advantage from SNAPE for SNPs calling in sequenced DNA pools, 39,165 SNPs were identified; 1/4 of them were novel variants not reported in dbSNP. Combining sequencing data with Illumina PorcineSNP60 BeadChip genotyping results on the same animals, 661 genomic positions overlapped with a good approximation of minor allele frequency estimation. A total of 54 SNPs showing enriched alleles in one or in the other RRLs might be potential markers associated with BFT. Some of these SNPs were close to genes involved in obesity related phenotypes.