Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Pricing farm-level agricultural insurance: a Bayesian approach

This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Cyclic Guanosine Monophosphate Signaling Cascade Mediates Pigment Aggregation in Freshwater Shrimp Chromatophores

The cell signaling cascades that mediate pigment movements in crustacean chromatophores are not yet well established, although Ca(2+) and cyclic nucleotide second messengers are involved. Here, we examine the participation of cyclic guanosine monophosphate (cGMP) in pigment aggregation triggered by red pigment concentrating hormone (RPCH) in the red ovarian chromatophores of freshwater shrimp. In Ca(2+)-containing (5.5 mmol l(-1)) saline, 10 mu mol l(-1) dibutyryl cGMP alone produced complete pigment aggregation with the same time course (approximate to 20 min) and peak velocity (approximate to 17 mu m/min) as 10(-8) mol l(-1) RPCH; however, in Ca(2+)-free saline (9 X 10(-11) mol l(-1) Ca(2+)), db-cGMP was without effect. The soluble guanylyl cyclase (GC-S) activators sodium nitroprusside (SNP, 0.5 mu mol l(-1)) and 3-morpholinosydnonimine (SIN-1, 100 mu mol l(-1)) induced moderate aggregation by themselves (approximate to 35%-40%) but did not affect RPCH-triggered aggregation. The GC-S inhibitors zinc protoporphyrin IX (ZnPP-XI, 30 mu mol l(-1)) and 6-anilino-5,8-quinolinedione (LY83583, 10 mu mol l(-1)) partially inhibited RPCH-triggered aggregation by approximate to 35%. Escherichia coli heat-stable enterotoxin (STa, 1 mu mol l(-1)), a membrane-receptor guanylyl cyclase stimulator, did not induce or affect RPCH-triggered aggregation. We propose that the binding of RPCH to an unknown membrane-receptor type activates a Ca(2+)-dependent signaling cascade coupled via cytosolic guanylyl cyclase and cGMP to protein kinase G-phosphorylated proteins that regulate aggregation-associated, cytoskeletal molecular motor activity. This is a further example of a cGMP signaling cascade mediating the effect of a crustacean X-organ neurosecretory peptide.

Bayesian analysis of the error correction model

This paper presents a method for estimating the posterior probability density of the cointegrating rank of a multivariate error correction model. A second contribution is the careful elicitation of the prior for the cointegrating vectors derived from a prior on the cointegrating space. This prior obtains naturally from treating the cointegrating space as the parameter of interest in inference and overcomes problems previously encountered in Bayesian cointegration analysis. Using this new prior and Laplace approximation, an estimator for the posterior probability of the rank is given. The approach performs well compared with information criteria in Monte Carlo experiments. (C) 2003 Elsevier B.V. All rights reserved.

Prion protein ablation increases cellular aggregation and embolization contributing to mechanisms of metastasis

Cellular Prion Protein (PrP(C)) is a cell surface protein highly expressed in the nervous system, and to a lesser extent in other tissues. PrP(C) binds to the extracellular matrix laminin and vitronectin, to mediate cell adhesion and differentiation. Herein, we investigate how PrP(C) expression modulates the aggressiveness of transformed cells. Mesenchymal embryonic cells (MEC) from wildtype (Prnp(+/+)) and PrP(C)-null (Prnp(0/0)) mice were immortalized and transformed by co-expression of ras and myc. These cells presented similar growth rates and tumor formation in vivo. When injected in the tail vein, PrnP(0/0)raS/myc cells exhibited increased lung colonization compared with Prnp(+/+)ras/myc cells. Additionally, Prnp(0/0)ras/myc cells form more aggregates with blood components than Prnp(+/+)ras/myc cells, facilitating the arrest of Prnp(0/0)ras/myc cells in the lung vasculature. Integrin alpha(v)beta(3) is more expressed and activated in MEC and in transformed Prnp(0/0) cells than in the respective Prnp(+/+) cells. The blocking of integrin alpha(v)beta(3) by RGD peptide reduces lung colonization in transformed Prnp(0/0) cells to similar levels of those presented by transformed Prnp(+/+) cells. Our data indicate that PrP(C) negatively modulates the expression and activation of integrin alpha(v)beta(3) resulting in a more aggressive phenotype. These results indicate that PrP(C) may have main implications in modulating metastasis formation. (C) 2009 UICC

Molecular characterization of the Hepatitis B virus genotypes in Colombia: A Bayesian inference on the genotype F

Hepatitis B is a worldwide health problem affecting about 2 billion people and more than 350 million are chronic carriers of the virus. Nine HBV genotypes (A to I) have been described. The geographical distribution of HBV genotypes is not completely understood due to the limited number of samples from some parts of the world. One such example is Colombia, in which few studies have described the HBV genotypes. In this study, we characterized HBV genotypes in 143 HBsAg-positive volunteer blood donors from Colombia. A fragment of 1306 bp partially comprising HBsAg and the DNA polymerase coding regions (S/POL) was amplified and sequenced. Bayesian phylogenetic analyses were conducted using the Markov Chain Monte Carlo (MCMC) approach to obtain the maximum clade credibility (MCC) tree using BEAST v.1.5.3. Of all samples, 68 were positive and 52 were successfully sequenced. Genotype F was the most prevalent in this population (77%) - subgenotypes F3 (75%) and Fib (2%). Genotype G (7.7%) and subgenotype A2 (15.3%) were also found. Genotype G sequence analysis suggests distinct introductions of this genotype in the country. Furthermore, we estimated the time of the most recent common ancestor (TMRCA) for each HBV/F subgenotype and also for Colombian F3 sequences using two different datasets: (i) 77 sequences comprising 1306 bp of S/POL region and (ii) 283 sequences comprising 681 bp of S/POL region. We also used two other previously estimated evolutionary rates: (i) 2.60 x 10(-4) s/s/y and (ii) 1.5 x 10(-5) s/s/y. Here we report the HBV genotypes circulating in Colombia and estimated the TMRCA for the four different subgenotypes of genotype F. (C) 2010 Elsevier B.V. All rights reserved.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

Anisotropic Reversible Aggregation of Latex Nanoparticles Suspended in a Lyotropic Nematic Liquid Crystal: Effect of Gradients of Biaxial Order

We studied the anisotropic aggregation of spherical latex particles dispersed in a lyotropic liquid crystal presenting three nematic phases; calamitic, biaxial, and discotic. We observed that in the nematic calamitic phase aggregates of latex particles are formed, which become larger and anisotropic in the vicinity of the transition to the discotic phase, due to a coalescence process. Such aggregates are weakly anisotropic and up to 50 mu m long and tend to align parallel to the director field. At the transition to the discotic phase, the aggregates dissociated and re-formed when the system was brought back to the calamitic phase. This shows that the aggregation is due to attractive and repulsive forces generated by the particular structure of the nematic phase. The surface-induced positional order was investigated by surface force apparatus experiments with the lyotropic system confined between mica surfaces, revealing the existence of a presmectic wetting layer around the surfaces and oscillating forces of increasing amplitude as the confinement thickness was decreased. We discuss the possible mechanisms responsible for the reversible aggregation of latex particles, and we propose that capillary condensation of the N(C) phase, induced by the confinement between the particles, could reduce or remove the gradient of order parameter, driving the transition of aggregates from solidlike to liquidlike and gaslike.

A tick salivary protein targets cathepsin G and chymase and inhibits host inflammation and platelet aggregation

Platelet aggregation and acute inflammation are key processes in vertebrate defense to a skin injury. Recent studies uncovered the mediation of 2 serine proteases, cathepsin G and chymase, in both mechanisms. Working with a mouse model of acute inflammation, we revealed that an exogenous salivary protein of Ixodes ricinus, the vector of Lyme disease pathogens in Europe, extensively inhibits edema formation and influx of neutrophils in the inflamed tissue. We named this tick salivary gland secreted effector as I ricinus serpin-2 (IRS-2), and we show that it primarily inhibits cathepsin G and chymase, while in higher molar excess, it affects thrombin activity as well. The inhibitory specificity was explained using the crystal structure, determined at a resolution of 1.8 angstrom. Moreover, we disclosed the ability of IRS-2 to inhibit cathepsin G-induced and thrombin-induced platelet aggregation. For the first time, an ectoparasite protein is shown to exhibit such pharmacological effects and target specificity. The stringent specificity and biological activities of IRS-2 combined with the knowledge of its structure can be the basis for the development of future pharmaceutical applications. (Blood. 2011;117(2):736-744)