Prediction of Milk Coagulation Properties and Individual Cheese Yield in Sheep Using Partial Least Squares Regression

The objectives of this study were (i) the prediction of sheep milk coagulation properties (MCP) and individual laboratory cheese yield (ILCY) from mid-infrared (MIR) spectra by using partial least squares (PLS) regression, and (ii) the comparison of different data pre-treatments on prediction accuracy. Individual milk samples of 970 Sarda breed ewes were analyzed for rennet coagulation time (RCT), curd-firming time (k20), and curd firmness (a30) using the Formagraph instrument; ILCY was measured by micro-manufacturing assays. An Furier-transform Infrared (FTIR) milk-analyzer was used for the estimation of the milk gross composition and the recording of MIR spectrum. The dataset (n = 859, after the exclusion of 111 noncoagulating samples) was divided into two sub-datasets: the data of 700 ewes were used to estimate prediction model parameters, and the data of 159 ewes were used to validate the model. Four prediction scenarios were compared in the validation, differing for the use of whole or reduced MIR spectrum and the use of raw or corrected data (locally weighted scatterplot smoothing). PLS prediction statistics were moderate. The use of the reduced MIR spectrum yielded the best results for the considered traits, whereas the data correction improved the prediction ability only when the whole MIR spectrum was used. In conclusion, PLS achieves good accuracy of prediction, in particular for ILCY and RCT, and it may enable increasing the number of traits to be included in breeding programs for dairy sheep without additional costs and logistics

Use of multivariate factor analysis to characterize the fatty acid profile of buffalo milk

The suitability of multivariate factor analysis (MFA) to extract a small number of latent variables able to explain the correlation pattern among fatty acids (FA) in buffalo milk was evaluated. FA profile of milk samples from 214 Italian water buffaloes was analysed by gas chromatography. MFA, performed on the correlation matrix of 52 FA, was able to extract 10 latent factors with specific biological meaning related to a common metabolic origin for FA associated with the same factor. Scores of the factors were treated as new quantitative phenotypes to evaluate the effect of age, month of calving and lactation stage. MFA approach was effective in describing the FA profile of buffalo milk by using a low number of new latent variables that clustered FA having similar metabolic origin and function. The new variables were also useful to test the effect of environmental and individual animal factors on milk FA composition

Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle

The aim of this study was to perform genetic, genome-wide association (GWAS), and gene-set enrichment analyses with latent variables related to milk fatty acid profile (i.e., fatty acids factor scores; FAF), milk composition, and udder health in a cohort of 1,158 Italian Brown Swiss cows. The phenotypes under study were 12 FAF previously identified through factor analysis and classified as follows: de novo FA (F1), branched-chain FA-milk yield (F2), biohydrogenation (F3), long-chain fatty acids (F4), desaturation (F5), short-chain fatty acids (F6), milk protein and fat contents (F7), odd fatty acids (F8), conjugated linoleic acids (F9), linoleic acid (F10), udder health (F11) and vaccelenic acid (F12). (Co)variance components were estimated for factor scores using a Bayesian linear animal model via Gibbs sampling. The animals were genotyped with the Illumina BovineSNP50 BeadChip v.2 (Illumina Inc., San Diego, CA). A single marker regression model was fitted for GWAS analysis. The gene-set enrichment analysis was run on the GWAS results using the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway databases to identify the ontologies and pathways associated with the FAF. Marginal posterior means of the heritabilities of the aforementioned FAF ranged from 0.048 for F12 to 0.310 for F5. Factors F1 and F6 had the highest number of relevant genetic correlations with the other traits. The genomic analysis detected a total of 39 significant SNP located on 17 Bos taurus autosomes. All latent variables produced signals except for F2 and F10. The traits with the highest number of significant associations were F11 (17) and F12 (7). Gene-set enrichment analyses identified significant pathways (false discovery rate 5%) for F3 and F7. In particular, systemic lupus erythematosus was enriched for F3, whereas the MAPK (mitogen-activated protein kinase) signaling pathway was overrepresented for F7. The results support the existence of important and exploitable genetic and genomic variation in these latent explanatory phenotypes. Information acquired might be exploited in selection programs and when designing further studies on the role of the putative candidate genes identified in the regulation of milk composition and udder health

Conservation status and historical relatedness of Italian cattle breeds

Abstract Background: In the last 50 years, the diversity of cattle breeds has experienced a severe contraction. However, in spite of the growing diffusion of cosmopolite specialized breeds, several local cattle breeds are still farmed in Italy. Genetic characterization of breeds represents an essential step to guide decisions in the management of farm animal genetic resources. The aim of this work was to provide a high-resolution representation of the genome-wide diversity and population structure of Italian local cattle breeds using a medium-density single nucleotide polymorphism (SNP) array. Results: After quality control filtering, the dataset included 31,013 SNPs for 800 samples from 32 breeds. Our results on the genetic diversity of these breeds agree largely with their recorded history. We observed a low level of genetic diversity, which together with the small size of the effective populations, confirmed that several breeds are threatened with extinction. According to the analysis of runs of homozygosity, evidence of recent inbreeding was strong in some local breeds, such as Garfagnina, Mucca Pisana and Pontremolese. Patterns of genetic differentiation, shared ancestry, admixture events, and the phylogenetic tree, all suggest the presence of gene flow, in particular among breeds that originate from the same geographical area, such as the Sicilian breeds. In spite of the complex admixture events that most Italian cattle breeds have experienced, they have preserved distinctive characteristics and can be clearly discriminated, which is probably due to differences in genetic origin, environment, genetic isolation and inbreeding. Conclusions: This study is the first exhaustive genome-wide analysis of the diversity of Italian cattle breeds. The results are of significant importance because they will help design and implement conservation strategies. Indeed, efforts to maintain genetic diversity in these breeds are needed. Improvement of systems to record and monitor inbreeding in these breeds may contribute to their in situ conservation and, in view of this, the availability of genomic data is a fundamental resource

Il disegno sperimentale

L'elaborazione e l'interpretazione dei dati raccolti nella realizzazione degli esperimenti costituisce uno dei momenti pi\uf9 importanti del metodo scientifico. In questa fase il ricercatore estrae le informazioni raccolte nei dati sperimentali, le decodifica e le confronta con la sua ipotesi di ricerca. La statistica \ue8 la disciplina del campo delle scienze matematiche che fornisce l'armamentario ai ricercatori per l'analisi e l'interpretazione dei dati sperimentali. Nei secoli, questa disciplina si \ue8 sviluppata dal punto di vista teorico grazie allo sforzo di studiosi geniali. Il grande sviluppo dell'informatica degli ultimi decenni ha messo a disposizione dei ricercatori PC e software in grado di condurre analisi, anche complesse, in tempi rapidi e con relativa facilit\ue0 di esecuzione. Questa semplicit\ue0 di realizzazione non pu\uf2 per\uf2 prescindere dalla conoscenza teorica profonda delle tecniche utilizzate. Un ricercatore deve essere in grado di progettare in maniera efficace un esperimento ed interpretare correttamente i risultati delle analisi. Le moderne tecnologie consentono grande velocit\ue0 e facilit\ue0 di esecuzione, ma non possono essere disgiunte dalla consapevolezza di ci\uf2 che si sta facendo. Un approccio equilibrato verso questa disciplina deve pertanto coniugare il rigore formale dell\u2019approccio matematico con uno sforzo di semplificazione in grado di legare i concetti teorici alla realt\ue0 su cui si sta operando. In questo senso, il testo \u201cElementi di statistica di base per le scienze zootecniche\u201d presenta una serie di tecniche statistiche di generale utilit\ue0 per i ricercatori del campo delle scienze zootecniche e delle scienze biologiche in generale. Con uno stile che combina una breve ma puntuale trattazione teorica con applicazioni ad esempi sperimentali del campo zootecnico vengono presentate le tecniche di statistica descrittiva, i fondamenti dell\u2019inferenza statistica, l\u2019analisi della varianza, alcune tecniche non parametriche, i principali disegni sperimentali e l\u2019analisi della regressione. Gli esempi vengono sviluppati con il software gratuito R, uno dei programmi di pi\uf9 largo utilizzo nella comunit\ue0 scientifica internazionale e di cui il testo fornisce alcune nozioni di base per il suo utilizzo, e sviluppati sia nei singoli capitoli che raggruppati in una appendice finale

Use of Canonical discriminant analysis to study signatures of selection in Cattle

Background: Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of "signatures of selection" is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach. Methods: A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an FST-based approach. Results: Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the FST-based approach. Conclusions: The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the FST index