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.

Quantifying intraclass correlations for count and time-to-event data.

The intraclass correlation is commonly used with clustered data. It is often estimated based on fitting a model to hierarchical data and it leads, in turn, to several concepts such as reliability, heritability, inter-rater agreement, etc. For data where linear models can be used, such measures can be defined as ratios of variance components. Matters are more difficult for non-Gaussian outcomes. The focus here is on count and time-to-event outcomes where so-called combined models are used, extending generalized linear mixed models, to describe the data. These models combine normal and gamma random effects to allow for both correlation due to data hierarchies as well as for overdispersion. Furthermore, because the models admit closed-form expressions for the means, variances, higher moments, and even the joint marginal distribution, it is demonstrated that closed forms of intraclass correlations exist. The proposed methodology is illustrated using data from agricultural and livestock studies.