Daniela G. Calò

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 approach1

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 IKKβ/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.

A genome wide association study for backfat thickness in Italian Large White pigs highlights new regions affecting fat deposition including neuronal genes

BackgroundCarcass fatness is an important trait in most pig breeding programs. Following market requests, breeding plans for fresh pork consumption are usually designed to reduce carcass fat content and increase lean meat deposition. However, the Italian pig industry is mainly devoted to the production of Protected Designation of Origin dry cured hams: pigs are slaughtered at around 160 kg of live weight and the breeding goal aims at maintaining fat coverage, measured as backfat thickness to avoid excessive desiccation of the hams. This objective has shaped the genetic pool of Italian heavy pig breeds for a few decades. In this study we applied a selective genotyping approach within a population of ~ 12,000 performance tested Italian Large White pigs. Within this population, we selectively genotyped 304 pigs with extreme and divergent backfat thickness estimated breeding value by the Illumina PorcineSNP60 BeadChip and performed a genome wide association study to identify loci associated to this trait.ResultsWe identified 4 single nucleotide polymorphisms with P≤5.0E-07 and additional 119 ones with 5.0E-07<P≤5.0E-05. These markers were located throughout all chromosomes. The largest numbers were found on porcine chromosomes 6 and 9 (n=15), 4 (n=13), and 7 (n=12) while the most significant marker was located on chromosome 18. Twenty-two single nucleotide polymorphisms were in intronic regions of genes already recognized by the Pre-Ensembl Sscrofa10.2 assembly. Gene Ontology analysis indicated an enrichment of Gene Ontology terms associated with nervous system development and regulation in concordance with results of large genome wide association studies for human obesity.ConclusionsFurther investigations are needed to evaluate the effects of the identified single nucleotide polymorphisms associated with backfat thickness on other traits as a pre-requisite for practical applications in breeding programs. Reported results could improve our understanding of the biology of fat metabolism and deposition that could also be relevant for other mammalian species including humans, confirming the role of neuronal genes on obesity.

Gene expression profiling in glioblastoma and immunohistochemical evaluation of IGFBP-2 and CDC20.

Thirty-nine glial tumours (28 glioblastomas (GB) and 11 low-grade gliomas) were investigated with DNA microarrays to reveal a possible specific gene expression profile. Unsupervised classification through hierarchical cluster analysis identified two groups of tumours, the first composed of low-grade gliomas and the second mainly composed of GB. Nine genes were identified as most informative: seven were over-expressed in low-grade gliomas and under-expressed in GB; on the contrary, two genes, insulin-like growth factor binding protein 2 (IGFBP-2) and cell division cycle 20 homologue (CDC20), were over-expressed in GB and under-expressed in low-grade tumours. This same genetic profile was confirmed by reverse transcriptase polymerase chain reaction. Immunohistochemistry for IGFBP-2 was positive in 88.8% of the cases of GB and in only one low-grade glioma, whilst CDC20 immunostained 74.1% of the cases of GB and none low-grade glioma. This was confirmed in an additional series of cases studied with immunohistochemistry only. In conclusion, over-expression of mRNA levels of IGFBP-2 and CDC20 is highly related to GB, IGFBP-2 and CDC-20 gene and protein expressions are strongly correlated, and IGFBP-2 and CDC20 immunopositivity can be useful for the identification of GB in small biopsies.

A genomewide association study for average daily gain in Italian Large White pigs1

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.

Independent factor discriminant analysis

In the general classification context the recourse to the so-called Bayes decision rule requires to estimate the class conditional probability density functions. A mixture model for the observed variables which is derived by assuming that the data have been generated by an independent factor model is proposed. Independent factor analysis is in fact a generative latent variable model whose structure closely resembles the one of the ordinary factor model, but it assumes that the latent variables are mutually independent and not necessarily Gaussian. The method therefore provides a dimension reduction together with a semiparametric estimate of the class conditional probability density functions. This density approximation is plugged into the classic Bayes rule and its performance is evaluated both on real and simulated data.

Gaussian mixture model classification: A projection pursuit approach

Abstract Gaussian mixture models (GMM) are commonly employed in nonparametric supervised classification. In high-dimensional problems it is often the case that information relevant to the separation of the classes is contained in a few directions. A GMM fitting procedure oriented to supervised classification is proposed, with the aim of reducing the number of free parameters. It resorts to projection pursuit as a dimension reduction method and combines it with GM modelling of class-conditional densities. In its derivation, issues regarding the forward and backward projection pursuit algorithms are discussed. The proposed procedure avoids the “curse of dimensionality”, is able to model structure in subspaces and regularizes the classification model. Its performance is illustrated on a simulation experiment and on a real data set, in comparison with other GMM-based classification methods.

Variable Selection in Cell Classification Problems: A Strategy Based on Independent Component Analysis

In this paper the problem of cell classification using gene expression data is addressed. One of the main features of this kind of data is the very large number of variables (genes), relative to the number of observations (cells). This condition makes most of the standard statistical methods for classification difficult to employ. The proposed solution consists of building classification rules on subsets of genes showing a behavior across the cells that differs most from that of all the other ones. This variable selection procedure is based on suitable linear transformations of the observed data: a strategy resorting to independent component analysis is explored. Our proposal is compared with the nearest shrunken centroid method (Tibshirani et al. (2002)) on three publicly available data sets.

On a Transvariation Based Measure of Group Separability

In this paper, the potentialities of transvariation (Gini, 1959) in measuring the separation between two groups of multivariate observations are explored. With this aim, a modified version of Gini’s notion of multidimensional transvariation is proposed. According to Gini (1959), two groups G1 and G2 are said to transvary on the k-dimensional variable X = (X1,...,Xh,...,Xk) if there exists at least one pair of units, belonging to different groups, such that for h = 1,...,k the sign of the difference between their Xh values is opposite to that of m1h −m2h, where m1h and m2h are the corresponding group mean values of Xh. We introduce a modification that allows us to derive a measure of group separation, which can be profitably used in discriminating between two groups. The performance of the measure is tested through simulation experiments. The results show that the proposed measure is not sensitive to distributional assumptions and highlight its robustness against outliers.

Model-based clustering of probability density functions

Complex data such as those where each statistical unit under study is described not by a single observation (or vector variable), but by a unit-specific sample of several or even many observations, are becoming more and more popular. Reducing these sample data by summary statistics, like the average or the median, implies that most inherent information (about variability, skewness or multi-modality) gets lost. Full information is preserved only if each unit is described by a whole distribution. This new kind of data, a.k.a. “distribution-valued data”, require the development of adequate statistical methods. This paper presents a method to group a set of probability density functions (pdfs) into homogeneous clusters, provided that the pdfs have to be estimated nonparametrically from the unit-specific data. Since elements belonging to the same cluster are naturally thought of as samples from the same probability model, the idea is to tackle the clustering problem by defining and estimating a proper mixture model on the space of pdfs. The issue of model building is challenging here because of the infinite-dimensionality and the non-Euclidean geometry of the domain space. By adopting a wavelet-based representation for the elements in the space, the task is accomplished by using mixture models for hyper-spherical data. The proposed solution is illustrated through a simulation experiment and on two real data sets.

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.