Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package.

The BGLR-R package implements various types of single-trait shrinkage/variable selection Bayesian regressions. The package was first released in 2014, since then it has become a software very often used in genomic studies. We recently develop functionality for multitrait models. The implementation allows users to include an arbitrary number of random-effects terms. For each set of predictors, users can choose diffuse, Gaussian, and Gaussian-spike-slab multivariate priors. Unlike other software packages for multitrait genomic regressions, BGLR offers many specifications for (co)variance parameters (unstructured, diagonal, factor analytic, and recursive). Samples from the posterior distribution of the models implemented in the multitrait function are generated using a Gibbs sampler, which is implemented by combining code written in the R and C programming languages. In this article, we provide an overview of the models and methods implemented BGLR's multitrait function, present examples that illustrate the use of the package, and benchmark the performance of the software.

Core collection of common bean formed from traditional Brazilian germplasm / Coleção nuclear de feijão comum formada por germoplasma tradicional de coletas brasileiras

The aim of this study was to select traditional accessions, compose a core collection of common bean, and assess the representativeness of the collection in relation to the base collection accommodated in the BAG of Embrapa using analysis strategies for multivariate models. We used data characterizing 2903 accessions from collections representing all geographic areas of Brazil regarding three morphologic descriptors (seed color, growth habit type, and seed size) and four ecogeographic descriptors (geographical areas, states, altitudes, and soil classes). A set of 400 accessions were selected using multivariate models applied to the data transformed in multibinary values. The accessions sampled had maximum similarity (100%) to the traditional collection, phenotypic diversity, and representative heterogeneity in relation to the traditional collection. In the core collection, the accessions represented 9.5% of the traditional accessions and were equivalent to 3% of the accessions of the base collection. Thus, it is possible to form a core collection that is representative of the base collection regarding genetic diversity and the conservation of rare alleles.

O objetivo deste trabalho foi selecionar acessos tradicionais, compor uma coleção nuclear de feijoeiro comum e avaliar sua representatividade em relação à coleção base de coletas hospedadas no BAG da Embrapa. Utilizando dados de caracterização de 2903 acessos de coletas representando todas as regiões geográficas do Brasil quanto a três descritores morfológicos (cor de semente, tipos de crescimento e tamanho de semente) e quatro descritores ecogeográficos (regiões geográficas, unidades federativas, altitudes e classes de solos), foram selecionados 400 acessos utilizando modelos multivariados aplicados aos dados transformados em valores multibinários. Os acessos amostrados apresentaram similaridade máxima (100%) com a coleção tradicional, diversidade fenotípica e heterogeneidade representativa em relação à coleção tradicional. Na coleção nuclear, os acessos representaram 9,5% dos acessos tradicionais e equivalem a 3% dos acessos da coleção base. Com isso conclui-se que é possível formar uma coleção nuclear representativa da coleção base, no que diz respeito à diversidade genética e a conservação de alelos raros.

A Case⁻Control Study of Socio-Economic and Nutritional Characteristics as Determinants of Dental Caries in Different Age Groups, Considered as Public Health Problem: Data from NHANES 2013⁻2014.

One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary" among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10⁻19; group 2: 20⁻59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013⁻2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = -0.080 and AUC = 0.933; age group 3 obtained a mean NRI = -0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = -0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subjects.

[Clinical research XXII. From clinical judgment to Cox proportional hazards model]. / Investigación Clínica XXII. Del juicio clínico al modelo de riesgos proporcionales de Cox.

Survival analyses are commonly used to determine the time of an event (for example, death). However, they can be used also for other clinical outcomes on the condition that these are dichotomous, for example healing time. These analyses only consider the relationship of one variable. However, Cox proportional hazards model is a multivariate analysis of the survival analysis, in which other potentially confounding covariates of the effect of the main maneuver studied, such as age, gender or disease stage, are taken into account. This analysis can include both quantitative and qualitative variables in the model. The measure of association used is called hazard ratio (HR) or relative risk ratio, which is not the same as the relative risk or odds ratio (OR). The difference is that the HR refers to the possibility that one of the groups develops the event before it is compared with the other group. The proportional hazards multivariate model of Cox is the most widely used in medicine when the phenomenon is studied in two dimensions: time and event.

Los análisis de supervivencia son usados comúnmente para establecer el tiempo de ocurrencia de un evento (por ejemplo, muerte). Sin embargo, pueden ser utilizados para otros desenlaces clínicos siempre y cuando estos sean dicotómicos, como tiempo de curación, tiempo de recaída, tiempo para que una enfermedad inicie, etc. Los análisis de Kaplan-Meier (K-M) solo consideran la relación de una variable a traves del tiempo, mientras que los riesgos proporcionales de Cox son el modelo multivariado de este método, el cual toma en cuenta otras covariables posiblemente confusoras del efecto de la maniobra principal estudiada, como la edad, el sexo o el estadio de la enfermedad. Este análisis puede incluir en su modelo variables dependientes cuantitativas y cualitativas. La medida de asociación que se usa se llama hazard ratio (HR) o razón de riesgos, la cual no es lo mismo que el riesgo relativo o la razón de momios (RM). La diferencia es que el HR se refiere a la posibilidad de que uno de los grupos llegue antes a un evento al compararlo con otro. El modelo de riesgos proporcionales de Cox es el modelo multivariado más usado en la medicina cuando se estudia el fenómeno en dos dimensiones: tiempo y evento.