Disequilibrium Coefficient: A Bayesian Perspective

Hardy-Weinberg Equilibrium (HWE) is an important genetic property that populations should have whenever they are not observing adverse situations as complete lack of panmixia, excess of mutations, excess of selection pressure, etc. HWE for decades has been evaluated; both frequentist and Bayesian methods are in use today. While historically the HWE formula was developed to examine the transmission of alleles in a population from one generation to the next, use of HWE concepts has expanded in human diseases studies to detect genotyping error and disease susceptibility (association); Ryckman and Williams (2008). Most analyses focus on trying to answer the question of whether a population is in HWE. They do not try to quantify how far from the equilibrium the population is. In this paper, we propose the use of a simple disequilibrium coefficient to a locus with two alleles. Based on the posterior density of this disequilibrium coefficient, we show how one can conduct a Bayesian analysis to verify how far from HWE a population is. There are other coefficients introduced in the literature and the advantage of the one introduced in this paper is the fact that, just like the standard correlation coefficients, its range is bounded and it is symmetric around zero (equilibrium) when comparing the positive and the negative values. To test the hypothesis of equilibrium, we use a simple Bayesian significance test, the Full Bayesian Significance Test (FBST); see Pereira, Stern andWechsler (2008) for a complete review. The disequilibrium coefficient proposed provides an easy and efficient way to make the analyses, especially if one uses Bayesian statistics. A routine in R programs (R Development Core Team, 2009) that implements the calculations is provided for the readers.

Phylogeography of the common vampire bat (Desmodus rotundus): Marked population structure, Neotropical Pleistocene vicariance and incongruence between nuclear and mtDNA markers

Background: The common vampire bat Desmodus rotundus is an excellent model organism for studying ecological vicariance in the Neotropics due to its broad geographic range and its preference for forested areas as roosting sites. With the objective of testing for Pleistocene ecological vicariance, we sequenced a mitocondrial DNA (mtDNA) marker and two nuclear markers (RAG2 and DRB) to try to understand how Pleistocene glaciations affected the distribution of intraspecific lineages in this bat. Results: Five reciprocally monophyletic clades were evident in the mitochondrial gene tree, and in most cases with high bootstrap support: Central America (CA), Amazon and Cerrado (AMC), Pantanal (PAN), Northern Atlantic Forest (NAF) and Southern Atlantic Forest (SAF). The Atlantic forest clades formed a monophyletic clade with high bootstrap support, creating an east/west division for this species in South America. On the one hand, all coalescent and non-coalescent estimates point to a Pleistocene time of divergence between the clades. On the other hand, the nuclear markers showed extensive sharing of haplotypes between distant localities, a result compatible with male-biased gene flow. In order to test if the disparity between the mitochondrial and nuclear markers was due to the difference in mutation rate and effective size, we performed a coalescent simulation to examine the feasibility that, given the time of separation between the observed lineages, even with a gene flow rate close to zero, there would not be reciprocal monophyly for a neutral nuclear marker. We used the observed values of theta and an estimated mutation rate for the nuclear marker gene to perform 1000 iterations of the simulation. The results of this simulation were inconclusive: the number of iterations with and without reciprocal monophyly of one or more clades are similar. Conclusions: We therefore conclude that the pattern exhibited by the common vampire bat, with marked geographical structure for a mitochondrial marker and no phylogeographic structure for nuclear markers is compatible with a historical scenario of complete isolation of refuge-like populations during the Pleistocene. The results on demographic history on this species is compatible with the Carnaval-Moritz model of Pleistocene vicariance, with demographic expansions in the southern Atlantic forest.

Phylogeography and historical demography of the neotropical stingless bee Melipona quadrifasciata (Hymenoptera, Apidae): incongruence between morphology and mitochondrial DNA

The stingless bees are among the most abundant and ecologically important social invertebrates in tropical communities. The Neotropical stingless bee Melipona quadrifasciata has two subspecies: M. quadrifasciata quadrifasciata and M. quadrifasciata anthidioides. The main difference between subspecies are the yellow metassomal stripes, which are continuous in M. q. quadrifasciata and discontinuous in M. q. anthidioides. Recently, two populations were described with continuous stripes and inhabiting clearly disjunct areas in relation to M. q. quadrifasciata. We sequenced 852 bp of the mtDNA COI gene from 145 colonies from 56 localities, and for the first time performed a detailed phylogeographic study of a neotropical stingless bee. Phylogenetic analyses revealed the existence of two clades exhibiting a south to north distribution: southern populations comprise the subspecies M. q. quadrifasciata, and northern populations are composed of M. q. anthidioides and two disjunct populations with continuous stripes. The divergence time of these two phylogroups was estimated between 0.233 and 0.840 million years ago in the Pleistocene, a period of climatic changes and geomorphological alterations in the Neotropical region. No evidence of genetic structure in relation to the tergal stripes was found, indicating that the morphological trait regarding the pattern of stripes on tergites is not an accurate diagnostic for the subspecies of M. quadrifasciata.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.

Molecular and morphological characterization of Amathia distans Busk and Amathia brasiliensis Busk (Bryozoa: Ctenostomata) from the tropical and subtropical Western Atlantic

Morphological and molecular analyses have proven to be complementary tools of taxonomic information for the redescription of the ctenostome bryozoans Amathia brasiliensis Busk, 1886 and Amathia distans Busk, 1886. The two species, originally described from material collected by the `Challenger` expedition but synonymized by later authors, now have their status fixed by means of the selection of lectotypes, morphological observations and analyses of DNA sequences described here. The morphological characters allowing the identification of living and/or preserved specimens are (1) A. brasiliensis: whitish-pale pigment spots in the frontal surface of stolons and zooids, and a wide stolon with biserial zooid clusters growing in clockwise and anti-clockwise spirals along it, the spirality direction being maintained from maternal to daughter stolons; and (2) A. distans: bright yellow pigment spots in stolonal and zooidal surfaces including lophophores, and a slender stolon, thickly cuticularized, with biserial zooid clusters growing in clockwise and anti-clockwise spirals along it and the spirality direction not maintained from maternal to daughter stolons. Pairwise comparisons of DNA sequences of the mitochondrial genes cytochrome c oxidase subunit I and large ribosomal RNA subunit revealed deep genetic divergence between A. brasiliensis and A. distans. Finally, analyses of those sequences within a Bayesian phylogenetic context recovered their genealogical species status.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

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.

Phylogeography of the cactophilic species Drosophila gouveai: demographic events and divergence timing in dry vegetation enclaves in eastern Brazil

Aim The aim of this study was to assess the causal mechanisms underlying populational subdivision in Drosophila gouveai, a cactophilic species associated with xeric vegetation enclaves in eastern Brazil. A secondary aim was to investigate the genetic effects of Pleistocene climatic fluctuations on these environments. Location Dry vegetation enclaves within the limits of the Cerrado domain in eastern Brazil. Methods We determined the mitochondrial DNA haplotypes of 55 individuals (representing 12 populations) based on sequence data of a 483-bp fragment from the cytochrome c oxidase subunit II (COII) gene. Phylogenetic and coalescent analyses were used to test for the occurrence of demographic events and to infer the time of divergence amongst genetically independent groups. Results Our analyses revealed the existence of two divergent subclades (G1 and G2) plus an introgressed clade restricted to the southernmost range of D. gouveai. Subclades G1 and G2 displayed genetic footprints of range expansion and segregated geographical distributions in south-eastern and some central highland regions, east and west of the Parana River valley. Molecular dating indicated that the main demographic and diversification events occurred in the late to middle Pleistocene. Main conclusions The phylogeographical and genetic patterns observed for D. gouveai in this study are consistent with changes in the distribution of dry vegetation in eastern Brazil. All of the estimates obtained by molecular dating indicate that range expansion and isolation pre-dated the Last Glacial Maximum, occurring during the late to middle Pleistocene, and were probably triggered by climatic changes during the Pleistocene. The current patchy geographical distribution and population subdivision in D. gouveai is apparently closely linked to these past events.