Genome-wide association study for the level of serum electrolytes in Italian Large White pigs.

Calcium, magnesium and phosphorus are essential electrolytes involved in a large number of biological processes. Imbalance of these minerals in blood may indicate clinically relevant conditions and are important in inferring acute or chronic pathologies in humans and animals. In this work, we carried out a genome-wide association study (GWAS) for the level of these three electrolytes in the serum of 843 performance-tested Italian Large White pigs. All pigs were genotyped with the Illumina PorcineSNP60 BeadChip, and GWAS was carried out using genome-wide efficient mixed-model association. For the level of Ca(2+) , eight single nucleotide polymorphisms (SNPs) were significant, considering a false discovery rate (FDR) < 0.05, and another eight were above the moderate association threshold (Pnominal value  < 5.00E-05). These SNPs are distributed in four porcine chromosomes (SSC): SSC8, SSC11, SSC12 and SSC13. In particular, a few putative different signals of association detected on SSC13 and one on SSC12 were in genes or close to genes involved in calcium metabolism (P2RY1, RAP2B, SLC9A9, C3orf58, TSC22D2, PLCH1 and CACNB1). Only one SNP (on SSC7) and six SNPs (on SSC2 and SSC7) showed moderate association with the level of magnesium and phosphorus respectively. The association signals for these two latter minerals might identify genes not known thus far for playing a role in their biological functions and regulations. In conclusion, our GWAS contributed to increased knowledge on the role that calcium, magnesium and phosphorus may play in the genetically determined physiological mechanisms affecting the natural variability of mineral levels in mammalian blood.

Metabolomics evidences plasma and serum biomarkers differentiating two heavy pig breeds.

In pigs, many production traits are known to vary among breeds or lines. These traits can be considered end phenotypes or external traits as they are the final results of complex biological interactions and processes whose fine biological mechanisms are still largely unknown. This study was designed to compare plasma and serum metabolomic profiles between animals of two heavy pig breeds (12 Italian Large White and 12 Italian Duroc), testing indirectly the hypothesis that different genetic backgrounds might be the determining factors of differences observed on the level of metabolites in the analyzed biofluids between breeds. We used a targeted metabolomic approach based on mass spectrometric detection of about 180 metabolites and applied a statistical validation pipeline to identify differences in the metabolomic profiles of the two heavy pig breeds. Blood samples were collected after jugulation at the slaughterhouse and prepared for metabolomics analysis that was carried out using the Biocrates AbsoluteIDQ p180 Kit, covering five different biochemical classes: glycerophospholipids, amino acids, biogenic amines, hexoses and acylcarnitines. A statistical pipeline that included the selection of the most relevant metabolites differentiating the two breeds by sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was coupled with a stability test and significance test determined with leave one out and permutation procedures. sPLS-DA plots clearly separated the pigs of the two investigated breeds. A few metabolites (a total of five metabolites considering the two biofluids) involved in key metabolic pathways largely contributed to these differences between breeds. In particular, a higher level of the sphingomyelins SM (OH) C14:1 (both in plasma and serum), SM (OH) C16:1 (in serum) and SM C16:0 (in serum) were observed in Italian Duroc than in Italian Large White pigs and the inverse was for the biogenic amine kynurenine (in plasma). The level of another biogenic amine (acetylornithine) was higher in Italian Large White than in Italian Duroc pigs in both analysed biofluids. These results provided biomarkers that could be important to understand the biological differences between these two heavy pig breeds. In particular, according to the functional role played by sphingomyelins in obesity-induced inflammatory responses, it could be possible to speculate that a higher level of sphingomyelins in Italian Duroc might be related to the higher interrmuscular fat deposition of this breed compared with the Italian Large White. Additional studies will be needed to evaluate the relevance of these biomarkers for practical applications in pig breeding and nutrition.

Twenty years of artificial directional selection have shaped the genome of the Italian Large White pig breed.

In this study, we investigated at the genome-wide level if 20 years of artificial directional selection based on boar genetic evaluation obtained with a classical BLUP animal model shaped the genome of the Italian Large White pig breed. The most influential boars of this breed (n = 192), born from 1992 (the beginning of the selection program of this breed) to 2012, with an estimated breeding value reliability of >0.85, were genotyped with the Illumina Porcine SNP60 BeadChip. After grouping the boars in eight classes according to their year of birth, filtered single nucleotide polymorphisms (SNPs) were used to evaluate the effects of time on genotype frequency changes using multinomial logistic regression models. Of these markers, 493 had a PBonferroni  < 0.10. However, there was an increasing number of SNPs with a decreasing level of allele frequency changes over time, representing a continuous profile across the genome. The largest proportion of the 493 SNPs was on porcine chromosome (SSC) 7, SSC2, SSC8 and SSC18 for a total of 204 haploblocks. Functional annotations of genomic regions, including the 493 shifted SNPs, reported a few Gene Ontology terms that might underly the biological processes that contributed to increase performances of the pigs over the 20 years of the selection program. The obtained results indicated that the genome of the Italian Large White pigs was shaped by a directional selection program derived by the application of methodologies assuming the infinitesimal model that captured a continuous trend of allele frequency changes in the boar population.

Deconstructing the pig sex metabolome: Targeted metabolomics in heavy pigs revealed sexual dimorphisms in plasma biomarkers and metabolic pathways.

Metabolomics has opened new possibilities to investigate metabolic differences among animals. In this study, we applied a targeted metabolomic approach to deconstruct the pig sex metabolome as defined by castrated males and entire gilts. Plasma from 545 performance-tested Italian Large White pigs (172 castrated males and 373 females) sampled at about 160 kg live weight were analyzed for 186 metabolites using the Biocrates AbsoluteIDQ p180 Kit. After filtering, 132 metabolites (20 AA, 11 biogenic amines, 1 hexose, 13 acylcarnitines, 11 sphingomyelins, 67 phosphatidylcholines, and 9 lysophosphatidylcholines) were retained for further analyses. The multivariate approach of the sparse partial least squares discriminant analysis was applied, together with a specifically designed statistical pipeline, that included a permutation test and a 10 cross-fold validation procedure that produced stability and effect size statistics for each metabolite. Using this approach, we identified 85 biomarkers (with metabolites from all analyzed chemical families) that contributed to the differences between the 2 groups of pigs ( < 0.05 at the stability statistic test). All acylcarnitines and almost all biogenic amines were higher in castrated males than in gilts. Metabolites involved in tryptophan catabolism had the largest differences (i.e., delta = 20% for serotonin) between castrated males (higher) and gilts (lower). The level of several AA (Ala, Arg, Gly, His, Lys, Ser, Thr, and Trp) was higher in gilts (delta was from approximately 1.0 to approximately 4.8%) whereas products of AA catabolism (taurine, 2-aminoadipic acid, and methionine sulfoxide) were higher in castrated males (delta was approximately 5.0-6.0%), suggesting a metabolic shift in castrated males toward energy storage and lipid production. Similar general patterns were observed for most sphingomyelins, phosphatidylcholines, and lysophosphatidylcholines. Metabolomic pathway analysis and pathway enrichment identified several differences between the 2 sexes. This metabolomic overview opened new clues on the biochemical mechanisms underlying sexual dimorphism that, on one hand, might explain differences in terms of economic traits between castrated male pigs and entire gilts and, on the other hand, could strengthen the pig as a model to define metabolic mechanisms related to fat deposition.

A retrospective analysis of allele frequency changes of major genes during 20 years of selection in the Italian Large White pig breed.

In this study, we investigated whether a selection programme based on boar genetic evaluation obtained with a classical BLUP animal model can change allele frequencies in a pig population. All Italian Large White boars born from 1992 to 2012 with estimated breeding value reliability >0.85 (n = 200) were selected among all boars of this breed. Boars were genotyped with markers in major genes (IGF2 intron3-g.3072G>A, MC4R p.D298N, VRTN PRE1 insertion, PRKAG3 p.I199V and FTO g.276T>G). Genotyping data were analysed grouping boars in eight classes according to their year of birth. To evaluate the influence of time on allele frequencies of the genotyped markers, multinomial logistic regression models were computed. Four of five polymorphic sites (IGF2, MC4R, VRTN and FTO) showed significant (p < 0.01) changes in allele frequencies over time due to a progressive and continuous increase of one allele (associated with higher lean meat content, higher average daily gain and favourable feed: gain ratio) and, consequently, decrease of the other one, following the directional selection of the selection programme of this pig breed. The retrospective analysis that was carried out in Italian Large White boars suggests that selection based on methodologies assuming the infinitesimal model is able to modify in a quite short period of time allele frequencies in major genes, increasing the frequency of alleles explaining a relevant (non-infinitesimal) fraction of the overall genetic variability for production traits.

Combined use of principal component analysis and random forests identify population-informative single nucleotide polymorphisms: application in cattle breeds.

The genetic identification of the population of origin of individuals, including animals, has several practical applications in forensics, evolution, conservation genetics, breeding and authentication of animal products. Commercial high-density single nucleotide polymorphism (SNP) genotyping tools that have been recently developed in many species provide information from a large number of polymorphic sites that can be used to identify population-/breed-informative markers. In this study, starting from Illumina BovineSNP50 v1 BeadChip array genotyping data available from 3711 cattle of four breeds (2091 Italian Holstein, 738 Italian Brown, 475 Italian Simmental and 407 Marchigiana), principal component analysis (PCA) and random forests (RFs) were combined to identify informative SNP panels useful for cattle breed identification. From a PCA preselected list of 580 SNPs, RFs were computed using ranking methods (Mean Decrease in the Gini Index and Mean Accuracy Decrease) to identify the most informative 48 and 96 SNPs for breed assignment. The out-of-bag (OOB) error rate for both ranking methods and SNP densities ranged from 0.0 to 0.1% in the reference population. Application of this approach in a test population (10% of individuals pre-extracted from the whole data set) achieved 100% of correct assignment with both classifiers. Linkage disequilibrium between selected SNPs was relevant (r(2) > 0.6) only in few pairs of markers indicating that most of the selected SNPs captured different fractions of variance. Several informative SNPs were in genes/QTL regions that affect or are associated with phenotypes or production traits that might differentiate the investigated breeds. The combination of PCA and RF to perform SNP selection and breed assignment can be easily implemented and is able to identify subsets of informative SNPs useful for population assignment starting from a large number of markers derived by high-throughput genotyping platforms.

Copy number variants in Italian Large White pigs detected using high-density single nucleotide polymorphisms and their association with back fat thickness.

The aim of this study was to identify copy number variants (CNVs) in Italian Large White pigs and test them for association with back fat thickness (BFT). Within a population of 12 000 performance-tested pigs, two groups of animals with extreme and divergent BFT estimated breeding values (EBVs; 147 with negative and 150 with positive EBVs) were genotyped with the Illumina Porcine SNP60 BeadChip. CNVs were detected with PENNCNV software. We identified a total of 4146 CNV events in 170 copy number variation regions (CNVRs) located on 15 porcine autosomes. Validation of detected CNVRs was carried out (i) by comparing CNVRs already detected by other studies and (ii) by semiquantitative fluorescent multiplex (SQFM) PCR of a few CNVRs. Most of CNVRs detected in Italian Large White pigs (71.2%) were already reported in other pig breeds/populations, and 82.1% of the CNV events detected by PENNCNV were confirmed by SQFM PCR. For each CNVR, we compared the occurrence of CNV events between the pigs of the high and low BFT EBV tails. Sixteen regions showed significance at P < 0.10, and seven were significant at P < 0.05 but were not significant after Bonferroni correction (Fisher's exact test). These results indicated that CNVs could explain a limited fraction of the genetic variability of fat deposition in Italian Large White pigs. However, it was interesting to note that one of these CNVRs encompassed the ZPLD1 gene. In humans, a rare CNV event including this gene is associated with obesity. Studies identifying CNVs in pigs could assist in elucidating the genetic mechanisms underlying human obesity.

A candidate gene association study for nine economically important traits in Italian Holstein cattle.

We genotyped 58 single nucleotide polymorphisms (SNPs) in 25 candidate genes in about 800 Italian Holstein sires. Fifty-six (minor allele frequency >0.02) were used to evaluate their association with single traits: milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP), milk protein percentage (PP), milk somatic cell count (MSCC); and complex indexes: longevity, fertility and productivity-functionality type (PFT), using deregressed proofs, after adjustment for familial relatedness. Thirty-two SNPs were significantly associated (proportion of false positives <0.05) with different traits: 16 with MSCC, 15 with PY, 14 with MY, 12 with PFT, eight with longevity, eight with FY, eight with PP, five with FP and two with fertility. In particular, a SNP in the promoter region of the PRLR gene was associated with eight of nine traits. DGAT1 polymorphisms were highly associated with FP and FY. Casein gene markers were associated with several traits, confirming the role of the casein gene cluster in affecting milk yield, milk quality and health traits. Other SNPs in genes located on chromosome 6 were associated with PY, PP, PFT, MY (PPARGC1A) and MSCC (KIT). This latter association may suggest a biological link between the degree of piebaldism in Holstein and immunological functions affecting somatic cell count and mastitis resistance. Other significant SNPs were in the ACACA, CRH, CXCR1, FASN, GH1, LEP, LGB (also known as PAEP), MFGE8, SRC, TG, THRSP and TPH1 genes. These results provide information that can complement QTL mapping and genome-wide association studies in Holstein.

A genomewide association study for average daily gain in Italian Large White pigs.

Average daily gain is an important target trait in pig breeding programs. In this study we performed a genomewide association study for ADG in Italian Large White pigs using a selective genotyping approach. Two extreme and divergent groups of Italian Large White pigs (number 190 + 190) were selected among a population of about 10,000 performance tested gilts (EBV for ADG in the 2 groups were -30 ± 14 g and 81 ± 12 g, respectively) and genotyped with the Illumina PorcineSNP60 BeadChip. Association analysis was performed treating the pigs of the 2 extreme groups as cases and controls after correction for family-based stratification. A total of 127 SNP resulted significantly associated with ADG (P nominal value [P(raw)] < 2.0 × 10(-7), P < 0.01 Bonferroni corrected [P(Bonferroni)] < 0.01, false discovery rate < 7.76 × 10(-5)). Another 102 SNP were suggestively associated with the target trait (P(raw) between 2.0 × 10(-7) and 2.02 × 10(-6), P(Bonferroni) < 0.10, false discovery rate < 4.19 × 10(-4)). These SNP were located on all autosomes and on porcine chromosome (SSC) X. The largest number of SNP within this list was on SSC5 (n = 42), SSC7 (34), SSC6 (30), SSC4 (23), and SSC16 (16). These chromosomes were richer in significant or suggestively significant markers than expected (P < 0.001). A quite high number of these SNP (n = 23) were associated with backfat thickness in a previous genomewide association study performed in the same pig population, confirming the negative correlation between the 2 traits. Two or more SNP targeted the same gene: IGSF3 and HS2ST1 (SSC4), OTOGL (SSC5), FTO region (SSC6), and MYLK4 and MCUR1 (SSC7). Other regions that were associated with ADG in previous candidate gene studies (e.g., MC4R on SSC1, IGF2 and LDHA on SSC2, MUC4 on SSC13) 1) included markers with P(raw) < 0.01 that, however, did not pass the stringent threshold of significance adopted in this study or 2) could not be tested because not assigned to the Sscrofa10.2 genome version. Functional annotation of the significant regions using Gene Ontology suggested that many and complex processes at different levels are involved in affecting ADG, indicating the complexity of the genetic factors controlling this ultimate phenotype. The obtained results may contribute to understand the genetic mechanisms determining ADG that could open new perspectives to improve selection efficiency in this breed.

Identification and association analysis of several hundred single nucleotide polymorphisms within candidate genes for back fat thickness in Italian Large White pigs using a selective genotyping approach.

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 ≈ 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 <5%), whereas the remaining 286 in 212 genes were taken for subsequent analyses, among which 64 showed a P(nominal) value <0.1. To deal with the multiple testing problem in a candidate gene approach, we applied the proportion of false positives (PFP) method. Thirty-eight SNP were significant (P(PFP) < 0.20). The most significant SNP was the IGF2 intron3-g.3072G>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) = 6.2E-04) 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 IKKß/NFκB 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.

Microarray gene expression analysis of porcine skeletal muscle sampled at several post mortem time points.

A pilot study using Affymetrix Gene Chip(®) Porcine Genome Arrays was set up to evaluate the impact of time lags from death on gene expression profiling of porcine skeletal muscle at four post mortem times (up to 24h) during the routine processing of fresh thighs. All post chip parameters and data analyses (Average background, Scale Factors, Percent Presence, 3'/5' ratios of ß-actin and glyceraldehyde-3-phosphase dehydrogenase control genes, RNA degradation diagnostics, principal component analysis, hierarchical clustering, mixed regression models with time effects) did not show any effect of post mortem time. Therefore, microarray data obtained from muscle specimens collected in a processing plan over a quite long period have the potential to identify treatments or pre mortem conditions without any potential bias derived from subtle RNA degradation. These results open new perspectives to develop and analyse gene expression biomarkers for pig production and product authentication.

Toilet training started during the first year of life: a report on elimination signals, stool toileting refusal and completion age.

AIM: The aim of this study was to examine an international population of children who started toilet training in the first year of life. METHODS: Two hundred eighty-six participants completed an anonymous questionnaire. Main outcomes variables were: presence of elimination signals (ES), elimination pattern consistency (EPC), stool toileting refusal (STR), and toilet training completion age. The analysis included the differences in completion age regarding each of the following variables: start age range, presence of ES, EPC, STR and country of residency for those who completed either bowel or bladder training at the time of survey completion. RESULTS: Over 90% of the respondents reported that their children showed ES. STR was nearly 12%. For those who completed toilet training at the time of survey completion mean completion ages for daytime dryness and bowel control were 17.4 and 15.0 months, respectively; those who initiated toilet training during the first 6 months completed training earlier than those who started later; those who showed STR at the beginning of training completed bowel training later than those who did not (P<0.001); those who exhibited ES for voiding or bowel movements completed day-dryness and bowel training earlier than those who did not (P<0.001). Among countries of residency, those children who resided in the USA and Canada completed bowel training the earliest (P<0.001). CONCLUSION: This is the first report which provides data on the current infant toilet training method, which is based mainly on ES and patterns, and practiced by motivated caregivers. Notable side effects were not observed.

Contact dermatitis due to ciclopiroxolamine.

We report a case of contact dermatitis because of ciclopiroxolamine milk in a 43-year-old woman with tinea pedis and onychomycosis caused by Trichophyton rubrum. Ciclopiroxolamine is regarded as a weak sensitiser because of the few allergic reactions described in relation to its widespread use.

The mitBASE human dataset structure.

MitBASE is a comprehensive and integrated mitochondrial genome database funded within the EU BIOTECH PROGRAM. It is a project for the development and implementation of an integrated and comprehensive database of mitochondrial data which will collect all available information from different organisms and from intraspecies variants and mutants. The present paper describes the structure of the Human dataset in mitBASE where human molecular data are distinguished from clinical and pathological data. MitBASE home page address is: http://www.ebi.ac.uk/htbin/Mitbase/mitb ase.pl

Update of MmtDB: a Metazoa mitochondrial DNA variants database.

The present paper describes the improvements in MmtDB, a specialised database designed to collect Metazoa mitochondrial DNA variants. Priority in the data collection has been given to Metazoa for which a large amount of variants is available, e.g., for humans. Starting from the sequences available in the Nucleotide Sequence Databases, the redundant sequences have been removed and new sequences from other sources have been added. Value-added information is associated to each variant sequence, e.g., analysed region, experimental method, tissue and cell lines, population data, sex, age, family code and information about the variation events (nucleotide position, involved gene, restriction site gain or loss). Cross-references are introduced to the EMBL Data Library, as well as an internal cross-referencing among MmtDB entries according to tissual, heteroplasmic, familiar and aplotypical correlation. Furthermore MmtDB has a new section, AMmtDB: Aligned Metazoan mitochondrial biosequences. MmtDB can be accessed through the World Wide Web at URL http://WWW.ba.cnr.it/[symbol: see text]areamt08/MmtDBWWW.htm

MmtDB: a Metazoa mitochondrial DNA variants database.

The present paper describes the structure of MmtDB-a specialized database designed to collect Metazoa mitochondrial DNA variants. Priority in the data collection is given to the Metazoa species for which a large amount of variants is available, as it is the case for human variants. Starting from the sequences available in the Nucleotide Sequence Databases, the redundant sequences are removed and new sequences from other sources are added. Value-added information are associated to each variant sequence, e.g. analysed region, experimental method, tissue and cell lines, population data, sex, age, family code and information about the variation events (nucleotide position, involved gene, restriction site's gain or loss). Cross-references are introduced to the EMBL Data Library, as well as an internal cross-referencing among MmtDB entries according to their tissual, heteroplasmic, familiar and aplotypical correlation. MmtDB can be accessed through the World Wide Web at URL [see text].