Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference

An important goal of Zebu breeding programs is to improve reproductive performance. A major problem faced with the genetic improvement of reproductive traits is that recording the time for an animal to reach sexual maturity is costly. Another issue is that accurate estimates of breeding values are obtained only a long time after the young bulls have gone through selection. An alternative to overcome these problems is to use traits that are indicators of the reproductive efficiency of the herd and are easier to measure, such as age at first calving. Another problem is that heifers that have conceived once may fail to conceive in the next breeding season, which increases production costs. Thus, increasing heifer's rebreeding rates should improve the economic efficiency of the herd. Response to selection for these traits tends to be slow, since they have a low heritability and phenotypic information is provided only later in the life of the animal. Genome-wide association studies (GWAS) are useful to investigate the genetic mechanisms that underlie these traits by identifying the genes and metabolic pathways involved. Data from 1853 females belonging to the Agricultural Jacarezinho LTDA were used. Genotyping was performed using the BovineHD BeadChip (777 962 single nucleotide polymorphisms (SNPs)) according to the protocol of Illumina - Infinium Assay II ® Multi-Sample HiScan with the unit SQ ™ System. After quality control, 305 348 SNPs were used for GWAS. Forty-two and 19 SNPs had a Bayes factor greater than 150 for heifer rebreeding and age at first calving, respectively. All significant SNPs for age at first calving were significant for heifer rebreeding. These 42 SNPs were next or within 35 genes that were distributed over 18 chromosomes and comprised 27 protein-encoding genes, six pseudogenes and two miscellaneous noncoding RNAs. The use of Bayes factor to determine the significance of SNPs allowed us to identify two sets of 42 and 19 significant SNPs for heifer rebreeding and age at first calving, respectively, which explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information to help elucidate which genes affect these traits.

The FGM bivariate lifetime copula model: a bayesian approach

In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.

Diversidade genética em populações de Candidatus Liberibacter asiaticus determinada por marcadores SSR

Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV

Estrutura genômica de uma população de suínos base Landrace

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The Kumaraswamy Gumbel distribution

The Gumbel distribution is perhaps the most widely applied statistical distribution for problems in engineering. We propose a generalization-referred to as the Kumaraswamy Gumbel distribution-and provide a comprehensive treatment of its structural properties. We obtain the analytical shapes of the density and hazard rate functions. We calculate explicit expressions for the moments and generating function. The variation of the skewness and kurtosis measures is examined and the asymptotic distribution of the extreme values is investigated. Explicit expressions are also derived for the moments of order statistics. The methods of maximum likelihood and parametric bootstrap and a Bayesian procedure are proposed for estimating the model parameters. We obtain the expected information matrix. An application of the new model to a real dataset illustrates the potentiality of the proposed model. Two bivariate generalizations of the model are proposed.

A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

Clusters of leprosy transmission and of late diagnosis in a highly endemic area in Brazil: focus on different spatial analysis approaches

Objective The Brazilian National Hansens Disease Control Program recently identified clusters with high disease transmission. Herein, we present different spatial analytical approaches to define highly vulnerable areas in one of these clusters. Method The study area included 373 municipalities in the four Brazilian states Maranha o, Para ', Tocantins and Piaui '. Spatial analysis was based on municipalities as the observation unit, considering the following disease indicators: (i) rate of new cases / 100 000 population, (ii) rate of cases < 15 years / 100 000 population, (iii) new cases with grade-2 disability / 100 000 population and (iv) proportion of new cases with grade-2 disabilities. We performed descriptive spatial analysis, local empirical Bayesian analysis and spatial scan statistic. Results A total of 254 (68.0%) municipalities were classified as hyperendemic (mean annual detection rates > 40 cases / 100 000 inhabitants). There was a concentration of municipalities with higher detection rates in Para ' and in the center of Maranha o. Spatial scan statistic identified 23 likely clusters of new leprosy case detection rates, most of them localized in these two states. These clusters included only 32% of the total population, but 55.4% of new leprosy cases. We also identified 16 significant clusters for the detection rate < 15 years and 11 likely clusters of new cases with grade-2. Several clusters of new cases with grade-2 / population overlap with those of new cases detection and detection of children < 15 years of age. The proportion of new cases with grade-2 did not reveal any significant clusters. Conclusions Several municipality clusters for high leprosy transmission and late diagnosis were identified in an endemic area using different statistical approaches. Spatial scan statistic is adequate to validate and confirm high-risk leprosy areas for transmission and late diagnosis, identified using descriptive spatial analysis and using local empirical Bayesian method. National and State leprosy control programs urgently need to intensify control actions in these highly vulnerable municipalities.

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes

A Bayesian nonparametric model for Taguchi's on-line quality monitoring procedure for attributes is introduced. The proposed model may accommodate the original single shift setting to the more realistic situation of gradual quality deterioration and allows the incorporation of an expert's opinion on the production process. Based on the number of inspections to be carried out until a defective item is found, the Bayesian operation for the distribution function that represents the increasing sequence of defective fractions during a cycle considering a mixture of Dirichlet processes as prior distribution is performed. Bayes estimates for relevant quantities are also obtained. (C) 2012 Elsevier B.V. All rights reserved.

Bayesian analysis of weak gravitational lensing and Sunyaev-Zel'dovich data for six galaxy clusters

We present an analysis of observations made with the Arcminute Microkelvin Imager (AMI) and the CanadaFranceHawaii Telescope (CFHT) of six galaxy clusters in a redshift range of 0.160.41. The cluster gas is modelled using the SunyaevZeldovich (SZ) data provided by AMI, while the total mass is modelled using the lensing data from the CFHT. In this paper, we (i) find very good agreement between SZ measurements (assuming large-scale virialization and a gas-fraction prior) and lensing measurements of the total cluster masses out to r200; (ii) perform the first multiple-component weak-lensing analysis of A115; (iii) confirm the unusual separation between the gas and mass components in A1914 and (iv) jointly analyse the SZ and lensing data for the relaxed cluster A611, confirming our use of a simulation-derived masstemperature relation for parametrizing measurements of the SZ effect.

Spatio-temporal patterns of tuberculosis incidence in Ribeirao Preto, State of Sao Paulo, southeast Brazil, and their relationship with social vulnerability: a Bayesian analysis

Introduction: The purpose of this ecological study was to evaluate the urban spatial and temporal distribution of tuberculosis (TB) in Ribeirao Preto, State of Sao Paulo, southeast Brazil, between 2006 and 2009 and to evaluate its relationship with factors of social vulnerability such as income and education level. Methods: We evaluated data from TBWeb, an electronic notification system for TB cases. Measures of social vulnerability were obtained from the SEADE Foundation, and information about the number of inhabitants, education and income of the households were obtained from Brazilian Institute of Geography and Statistics. Statistical analyses were conducted by a Bayesian regression model assuming a Poisson distribution for the observed new cases of TB in each area. A conditional autoregressive structure was used for the spatial covariance structure. Results: The Bayesian model confirmed the spatial heterogeneity of TB distribution in Ribeirao Preto, identifying areas with elevated risk and the effects of social vulnerability on the disease. We demonstrated that the rate of TB was correlated with the measures of income, education and social vulnerability. However, we observed areas with low vulnerability and high education and income, but with high estimated TB rates. Conclusions: The study identified areas with different risks for TB, given that the public health system deals with the characteristics of each region individually and prioritizes those that present a higher propensity to risk of TB. Complex relationships may exist between TB incidence and a wide range of environmental and intrinsic factors, which need to be studied in future research.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results". The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.

A bayesian network meta-analysis on comparisons of enamel matrix derivatives, guided tissue regeneration and their combination therapies

Aims: Guided tissue regeneration (GTR) and enamel matrix derivatives (EMD) are two popular regenerative treatments for periodontal infrabony lesions. Both have been used in conjunction with other regenerative materials. We conducted a Bayesian network meta-analysis of randomized controlled trials on treatment effects of GTR, EMD and their combination therapies. Material and Methods: A systematic literature search was conducted using the Medline, EMBASE, LILACS and CENTRAL databases up to and including June 2011. Treatment outcomes were changes in probing pocket depth (PPD), clinical attachment level (CAL) and infrabony defect depth. Different types of bone grafts were treated as one group and so were barrier membranes. Results: A total of 53 studies were included in this review, and we found small differences between regenerative therapies which were non-significant statistically and clinically. GTR and GTR-related combination therapies achieved greater PPD reduction than EMD and EMD-related combination therapies. Combination therapies achieved slightly greater CAL gain than the use of EMD or GTR alone. GTR with BG achieved greatest defect fill. Conclusion: Combination therapies performed better than single therapies, but the additional benefits were small. Bayesian network meta-analysis is a promising technique to compare multiple treatments. Further analysis of methodological characteristics will be required prior to clinical recommendations.

Statistical Information: A Bayesian Perspective

We explore the meaning of information about quantities of interest. Our approach is divided in two scenarios: the analysis of observations and the planning of an experiment. First, we review the Sufficiency, Conditionality and Likelihood principles and how they relate to trivial experiments. Next, we review Blackwell Sufficiency and show that sampling without replacement is Blackwell Sufficient for sampling with replacement. Finally, we unify the two scenarios presenting an extension of the relationship between Blackwell Equivalence and the Likelihood Principle.