Phylogeography of endemic toads and post-Pliocene persistence of the Brazilian Atlantic Forest

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Phylogeography of Batrachospermum macrosporum (Batrachospermales, Rhodophyta) from North and South America

Phylogeographic trends in Batrachospermum macrosporum Mont. were investigated using the mitochondrial intergenic spacer between the cytochrome oxidase subunit 2 and 3 genes (cox2-3). A total of 11 stream segments were sampled with seven in the coastal plain of North America and four in tropical areas of South America. Fifteen thalli were sampled from seven streams, 14 thalli from two streams, and eight thalli from two streams. There were 16 haplotypes detected using 149 individuals. of the eight haplotypes from locations in North America, all were 334 base pairs (bp) in length, and of those from South America, five were 344 bp, and three were 348 bp. Two individual networks were produced: one for the haplotypes from North America and another for those from South America, and these could not be joined due to the large number of base pair differences. This split between haplotypes from North and South America was confirmed with sequence data of the rbcL gene. There was very little genetic variation among the haplotypes from the North American locations, leading us to hypothesize that these are fairly recent colonization events along the coastal plain. In contrast, there was high variation among haplotypes from South America, and it would appear that the Amazon serves as a center of diversity. We detected considerable variation in haplotypes among streams, but frequently, a single haplotype in an individual stream segment, which is consistent with data from previous studies of other batrachospermalean taxa, may suggest a single colonization event per stream.

Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

A Bayesian approach for assessing the stability of genotypes

Several statistical models can be used for assessing genotype X environment interaction (GEI) and studying genotypic stability. The objectives of this research were to show how (i) to use Bayesian methodology for computing Shukla's phenotypic stability variance and (ii) to incorporate prior information on the parameters for better estimation. Potato [Solanum tuberosum subsp. andigenum (Juz. & Bukasov) Hawkes], wheat (Triticum aestivum L.), and maize (Zea mays L.) multi environment trials (MET) were used for illustrating the application of the Bayes paradigm. The potato trial included 15 genotypes, but prior information for just three genotypes was used. The wheat trial used prior information on all 10 genotypes included in the trial, whereas for the maize trial, noninformative priors for the nine genotypes was used. Concerning the posterior distribution of the genotypic means, the maize MET with 20 sites gave less disperse posterior distributions of the genotypic means than did the posterior distribution of the genotypic means of the other METs, which included fewer environments. The Bayesian approach allows use of other statistical strategies such as the normal truncated distribution (used in this study). When analyzing grain yield, a lower bound of zero and an upper bound set by the researcher's experience can be used. The Bayesian paradigm offers plant breeders the possibility of computing the probability of a genotype being the best performer. The results of this study show that although some genotypes may have a very low probability of being the best in all sites, they have a relatively good chance of being among the five highest yielding genotypes.

Phylogeography of the Solanaceae-infecting Basidiomycota fungus Rhizoctonia solani AG-3 based on sequence analysis of two nuclear DNA loci

Background: the soil fungus Rhizoctonia solani anastomosis group 3 (AG-3) is an important pathogen of cultivated plants in the family Solanaceae. Isolates of R. solani AG-3 are taxonomically related based on the composition of cellular fatty acids, phylogenetic analysis of nuclear ribosomal DNA (rDNA) and beta-tubulin gene sequences, and somatic hyphal interactions. Despite the close genetic relationship among isolates of R. solani AG-3, field populations from potato and tobacco exhibit comparative differences in their disease biology, dispersal ecology, host specialization, genetic diversity and population structure. However, little information is available on how field populations of R. solani AG-3 on potato and tobacco are shaped by population genetic processes. In this study, two field populations of R. solani AG-3 from potato in North Carolina (NC) and the Northern USA; and two field populations from tobacco in NC and Southern Brazil were examined using sequence analysis of two cloned regions of nuclear DNA (pP42F and pP89).Results: Populations of R. solani AG-3 from potato were genetically diverse with a high frequency of heterozygosity, while limited or no genetic diversity was observed within the highly homozygous tobacco populations from NC and Brazil. Except for one isolate (TBR24), all NC and Brazilian isolates from tobacco shared the same alleles. No alleles were shared between potato and tobacco populations of R. solani AG-3, indicating no gene flow between them. To infer historical events that influenced current geographical patterns observed for populations of R. solani AG-3 from potato, we performed an analysis of molecular variance (AMOVA) and a nested clade analysis (NCA). Population differentiation was detected for locus pP89 (Phi(ST) = 0.257, significant at P < 0.05) but not for locus pP42F (Phi(ST) = 0.034, not significant). Results based on NCA of the pP89 locus suggest that historical restricted gene flow is a plausible explanation for the geographical association of clades. Coalescent-based simulations of genealogical relationships between populations of R. solani AG-3 from potato and tobacco were used to estimate the amount and directionality of historical migration patterns in time, and the ages of mutations of populations. Low rates of historical movement of genes were observed between the potato and tobacco populations of R. solani AG-3.Conclusion: the two sisters populations of the basidiomycete fungus R. solani AG-3 from potato and tobacco represent two genetically distinct and historically divergent lineages that have probably evolved within the range of their particular related Solanaceae hosts as sympatric species.

Bayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMC

Linear mixed effects models are frequently used to analyse longitudinal data, due to their flexibility in modelling the covariance structure between and within observations. Further, it is easy to deal with unbalanced data, either with respect to the number of observations per subject or per time period, and with varying time intervals between observations. In most applications of mixed models to biological sciences, a normal distribution is assumed both for the random effects and for the residuals. This, however, makes inferences vulnerable to the presence of outliers. Here, linear mixed models employing thick-tailed distributions for robust inferences in longitudinal data analysis are described. Specific distributions discussed include the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted, and the Gibbs sampler and the Metropolis-Hastings algorithms are used to carry out the posterior analyses. An example with data on orthodontic distance growth in children is discussed to illustrate the methodology. Analyses based on either the Student-t distribution or on the usual Gaussian assumption are contrasted. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process for modelling distributions of the random effects and of residuals in linear mixed models, and the MCMC implementation allows the computations to be performed in a flexible manner.

Bayesian approximations in randomized response model

Practical Bayesian inference depends upon detailed examination of posterior distribution. When the prior and likelihood are conjugate, this is easily carried out; however, in general, one must resort to numerical approximation. In this paper, our aim is to solve, using MAPLE, the Bayesian paradigm, for a very special data collecting procedure, known as the randomized-response technique. This allows researchers to obtain sensitive information while guaranteeing privacy to respondents. This approach intends to reduce false responses on sensitive questions. Exact methods and approximations will be compared from the accuracy point of view as well as for the computational effort.

Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bayesian L-optimal exact design of experiments for biological kinetic models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic parameters by Bayesian inference for dual purpose Jaffarabadi buffaloes

Knowledge of genetic parameters is essential for improved reproductive management and increased yield. Quantitative analysis of genetic parameters is lacking for many breeds of buffaloes. This article provides the first estimate of genetic parameters for dual purpose (meat and milk) Brazilian Jaffarabadi buffaloes, using Bayesian inference. Data on milk yield (MY), lactation length (LL), weight at 205 days (W205) and 365 (W365) days of age, and average daily gain (ADG) from 205 to 365 days of age were collected in two herds. Bivariate analyses (using the program MTGSAM) were performed with the Gibbs sampler to obtain estimates of variance and covariance. Average lactation milk yield and lactation length were 1 620.2 +/- 450.9 kg and 257.6 +/- 46.8 days, respectively, and the mean values for weight traits (kg) were 181.6 +/- 63.3 (W205), 298.04 +/- 116.1 (W365), and 0.73 +/- 0.35 (ADG). Heritability estimates (modes) were 0.16 for MY, 0.10 for LL, 0.43 for W205, 0.48 for W365 and 0.32 for ADG. There was a high genetic correlation (0.96) between milk yield and lactation length and very high genetic correlations (0.99) between the three growth traits. Our data suggest that both milk production and growth traits have clear potential for yield improvement through direct selection in this dual purpose breed. The selection for weight at an early age would be successful and selection for MY can be performed in the first lactation.

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Bayesian estimation of generalized exponential distribution under noninformative priors

The generalized exponential distribution, proposed by Gupta and Kundu (1999), is a good alternative to standard lifetime distributions as exponential, Weibull or gamma. Several authors have considered the problem of Bayesian estimation of the parameters of generalized exponential distribution, assuming independent gamma priors and other informative priors. In this paper, we consider a Bayesian analysis of the generalized exponential distribution by assuming the conventional non-informative prior distributions, as Jeffreys and reference prior, to estimate the parameters. These priors are compared with independent gamma priors for both parameters. The comparison is carried out by examining the frequentist coverage probabilities of Bayesian credible intervals. We shown that maximal data information prior implies in an improper posterior distribution for the parameters of a generalized exponential distribution. It is also shown that the choice of a parameter of interest is very important for the reference prior. The different choices lead to different reference priors in this case. Numerical inference is illustrated for the parameters by considering data set of different sizes and using MCMC (Markov Chain Monte Carlo) methods.

Genetic correlations between categorical morphological traits in Nelore cattle by applying Bayesian analysis under a threshold animal model

P>In this study, Bayesian analysis under a threshold animal model was used to estimate genetic correlations between morphological traits (body structure, finishing precocity and muscling) in Nelore cattle evaluated at weaning and yearling. Visual scores obtained from 7651 Nelore cattle at weaning and from 4155 animals at yearling, belonging to the Brazilian Nelore Program, were used. Genetic parameters for the morphological traits were estimated by two-trait Bayesian analysis under a threshold animal model. The genetic correlations between the morphological traits evaluated at two ages of the animal (weaning and yearling) were positive and high for body structure (0.91), finishing precocity (0.96) and muscling (0.94). These results indicate that the traits are mainly determined by the same set of genes of additive action and that direct selection at weaning will also result in genetic progress for the same traits at yearling. Thus, selection of the best genotypes during only one phase of life of the animal is suggested. However, genetic differences between morphological traits were better detected during the growth phase to yearling. Direct selection for body structure, finishing precocity and muscling at only one age, preferentially at yearling, is recommended as genetic differences between traits can be detected at this age.