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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.

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.

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.

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.

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.

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.

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.