A new Bayesian approach for QTL mapping of family data.

In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs' effects. The model considers the detailed familiar dependence and it does not rely on random effects.

Bayesian Modeling for Epistasis Analysis Using Data-Driven Reversible Jump.

We propose a procedure for modeling a phenotype using QTLs which estimate the additive and dominance effects of genotypes and epistasis. The estimation of the model is implemented through a Bayesian approach which uses the data-driven reversible jump (DDRJ) for multiple QTL mapping and model selection. We compare the DDRJ's performance with the usual reversible jump (RJ), QTLBim, multiple interval mapping (MIM) and LASSO using real and simulated data sets.

Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data.

In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali-Mikhail-Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis-Hastings algorithm: Independent Metropolis-Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis-Hastings with a natural-candidate generating density (MH).

Improving Sample Distribution Homogeneity in Three-Dimensional Microfluidic Paper-Based Analytical Devices by Rational Device Design.

Paper-based devices are a portable, user-friendly, and affordable technology that is one of the best analytical tools for inexpensive diagnostic devices. Three-dimensional microfluidic paper-based analytical devices (3D-μPADs) are an evolution of single layer devices and they permit effective sample dispersion, individual layer treatment, and multiplex analytical assays. Here, we present the rational design of a wax-printed 3D-μPAD that enables more homogeneous permeation of fluids along the cellulose matrix than other existing designs in the literature.

A generalized mixture model applied to diabetes incidence data.

We present a generalization of the usual (independent) mixture model to accommodate a Markovian first-order mixing distribution. We propose the data-driven reversible jump, a Markov chain Monte Carlo (MCMC) procedure, for estimating the a posteriori probability for each model in a model selection procedure and estimating the corresponding parameters. Simulated datasets show excellent performance of the proposed method in the convergence, model selection, and precision of parameters estimates.

Development and statistical assessment of a paper-based immunoassay for detection of tumor markers.

Paper-based assays are an attractive low-cost option for clinical chemistry testing, due to characteristics such as short time of analysis, low consumption of samples and reagents, and high portability of assays. However, little attention has been given to the evaluation of the performance of these simple tests, which should include the use of a statistical approach to define the choice of best cut-off value for the test. The choice of the cut-off value impacts on the sensitivity and specificity of the bioassay.

Data-Driven Reversible Jump for QTL Mapping.

We propose a birth-death-merge data-driven reversible jump (DDRJ) for multiple-QTL mapping where the phenotypic trait is modeled as a linear function of the additive and dominance effects of the unknown QTL genotypes. We compare the performance of the proposed methodology, usual reversible jump (RJ) and multiple-interval mapping (MIM), using simulated and real data sets. Compared with RJ, DDRJ shows a better performance to estimate the number of QTLs and their locations on the genome mainly when the QTLs effect is moderate, basically as a result of better mixing for transdimensional moves.