Comparison of Brucella abortus population structure based on genotyping methods with different levels of resolution.

Numerous genotyping techniques based on different principles and with different costs and levels of resolution are currently available for understanding the transmission dynamics of brucellosis worldwide. We aimed to compare the population structure of the genomes of 53 Brazilian Brucella abortus isolates using eight different genotyping methods: multiple-locus variable-number tandem-repeat analysis (MLVA8, MLVA11, MLVA16), multilocus sequence typing (MLST9, MLST21), core genome MLST (cgMLST) and two techniques based on single nucleotide polymorphism (SNP) detection (parSNP and NASP) from whole genomes. The strains were isolated from six different Brazilian states between 1977 and 2008 and had previously been analyzed using MLVA8, MLVA11, and MLVA16. Their whole genomes were sequenced, assembled, and subjected to MLST9 MLST21, cgMLST, and SNP analyses. All the genotypes were compared by hierarchical grouping method based on the average distances between the correlation matrices of each technique. MLST9 and MLST21 had the lowest level of resolution, both revealing only four genotypes. MLVA8, MLVA11, and MLVA16 had progressively increasing levels of resolution as more loci were analyzed, identifying 6, 16, and 44 genotypes, respectively. cgMLST showed the highest level of resolution, identifying 45 genotypes, followed by the SNP-based methods, both of which had 44 genotypes. In the assessed population, MLVA was more discriminatory than MLST and was easier and cheaper to perform. SNP techniques and cgMLST provided the highest levels of resolution and the results from the two methods were in close agreement. In conclusion, the choice of genotyping technique can strongly affect one's ability to make meaningful epidemiological conclusions but is dependent on available resources: while the VNTR based techniques are more indicated to high prevalence scenarios, the WGS methods are the ones with the best discriminative power and therefore recommended for outbreaks investigation.

Application of Bayesian networks to the prediction of the AMEn: a new methodology in broiler nutrition.

Designing balanced rations for broilers depends on precise knowledge of nitrogen-corrected apparent metabolizable energy (AMEn) and the chemical composition of the feedstuffs. The equations that include the measurements of the chemical composition of the feedstuff can be used in the prediction of AMEn. In the literature, there are studies that obtained prediction equations through multiple regression, meta-analysis, and neural networks. However, other statistical methodologies with promising potential can be used to obtain better predictions of energy values. The objective of the present study was to propose and evaluate the use of Bayesian networks (BN) to the prediction of the AMEn values of energy and protein feedstuffs of vegetable origin used in the formulation of broiler rations. In addition, verify that the predictions of energy values using this methodology are the most accurate and, consequently, are recommended to Animal Science professionals area for the preparation of balanced feeds. BN are models that consist of graphical and probabilistic representations of conditional and joint distributions of the random variables. BN uses machine learning algorithms, being a methodology of artificial intelligence. The bnlearn package in R software was used to predict AMEn from the following covariates: crude protein, crude fiber, ethereal extract, mineral matter, as well as food category, i.e., energy (corn, corn by-products, and others) or protein (soybean, soy by-products, and others) and the type of animal (chick or cockerel). The data come from 568 feeding experiments carried out in Brazil. Additional data from metabolic experiments were obtained from the Federal University of Lavras (UFLA) - Lavras, Minas Gerais, Brazil. The model with the highest accuracy (mean squared error = 66529.8 and multiple coefficients of determination = 0.87) was fitted with the max-min hill climbing algorithm (MMHC) using 80% and 20% of the data for training and test sets, respectively. The accuracy of the models was evaluated based on their values of mean squared error, mean absolute deviation, and mean absolute percentage error. The equations proposed by a new methodology in avian nutrition can be used by the broiler industry in the determination of rations.

Design of microarray experiments for genetical genomics studies.

Microarray experiments have been used recently in genetical genomics studies, as an additional tool to understand the genetic mechanisms governing variation in complex traits, such as for estimating heritabilities of mRNA transcript abundances, for mapping expression quantitative trait loci, and for inferring regulatory networks controlling gene expression. Several articles on the design of microarray experiments discuss situations in which treatment effects are assumed fixed and without any structure. In the case of two-color microarray platforms, several authors have studied reference and circular designs. Here, we discuss the optimal design of microarray experiments whose goals refer to specific genetic questions. Some examples are used to illustrate the choice of a design for comparing fixed, structured treatments, such as genotypic groups. Experiments targeting single genes or chromosomic regions (such as with transgene research) or multiple epistatic loci (such as within a selective phenotyping context) are discussed. In addition, microarray experiments in which treatments refer to families or to subjects (within family structures or complex pedigrees) are presented. In these cases treatments are more appropriately considered to be random effects, with specific covariance structures, in which the genetic goals relate to the estimation of genetic variances and the heritability of transcriptional abundances.