A Bayesian Approach to Inferring Rates of Selfing and Locus-Specific Mutation

We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about reproduction under pure hermaphroditism, gynodioecy, and a model developed to describe the self-fertilizing killifish Kryptolebias marmoratus. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens sampling formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet process prior model. Our sampler is designed to accommodate additional information, including observations pertaining to the sex ratio, the intensity of inbreeding depression, and other aspects of reproduction. It can provide joint posterior distributions for the population-wide proportion of uniparental individuals, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual. Further, estimation of all basic parameters of a given model permits estimation of functions of those parameters, including the proportion of the gene pool contributed by each sex and relative effective numbers.     

变长度Motif识别中的Gibbs抽样算法(英文)

Motif识别是计算生物学中的重要问题.处理缺失数据的方法被大家广泛应用于生物序列中的Motif识别,例如EM算法,Gibbs抽样等等.现在识别Motif的方法都是首先假定Motif的长度是给的,但是,事实上Motif的长度是未知的,在这篇文章中,我们用Gibbs抽样算法在寻找Motif的位置的同时确定Motif的长度.     

Probabilistic Analysis Indicates Discordant Gene Trees in Chloroplast Evolution

Abstract Analyses of whole-genome data often reveal that some genes have evolutionary histories that diverge from the majority phylogeny estimated for the entire genome. We present a probabilistic model that deals with heterogeneity among gene trees, implement it via the Gibbs sampler, and apply it to the plastid genome. Plastids and their genomes are transmitted as a single block without recombination, hence homogeneity among gene trees within this genome is expected. Nevertheless, previous work has revealed clear heterogeneity among plastid genes (e.g., Delwiche and Palmer 1996). Other studies, using whole plastid genomes of various algae and land plants, found little additional heterogeneity (Martin et al. 1998; Adachi et al. 2000). We augment the earlier studies by using a data set of 14 taxa: 6 land plants, 2 green algae, a diatom, 2 red algae and a cryptophyte, the cyanelle of the glaucocystophyte Cyanophora, and the blue–green alga Synechocystis as an outgroup. Contrary to the earlier analyses, we cannot find even a single, dominant consensus tree. Therefore, we formulate a probabilistic model that divides the genes into two sets: those that follow the consensus tree and those that have independent gene trees. No particular tree is supported by more than three-fourths of the genes. But the set of genes that follows a certain tree is fairly independent of data processing and the method of analysis. With one possible exception, we find no evidence for collinear or functionally related genes to follow similar trees. The phylogenetic pattern also seems independent of bias in amino acid composition. Among possible explanations for the observed phenomenon, the hypothesis that different genes have different covarion structures is difficult to assess. But gene duplication may be possible through the inverted or direct repeat regions, while horizontal gene transfer seems less likely. In contrast to green algae and land plants, inverted repeat regions in red algae and in Cyanophora show abundant differences among the copies. Thus, genes may get duplicated when they are recruited into the inverted repeat region and one of the two copies may be lost after leaving the inverted repeat region.     

Bayesian population dynamics of interacting species: great gerbils and fleas in Kazakhstan

We propose a discrete-time Bayesian hierarchical model for the population dynamics of the great gerbil-flea ecological system. The model accounts for the sampling variability arising from data originally collected for other purposes. The prior for the unknown population densities incorporates specific biological hypotheses regarding the interacting dynamics of the two species, as well as their life cycles, where density-dependent effects are included. Posterior estimates are obtained via Markov chain Monte Carlo. The variance of the observed density estimates is a quadratic function of the unknown density. Our study indicates the presence of a density-dependent growth rate for the gerbil population. For the flea population there is clear evidence of density-dependent over-summer net growth, which is dependent on the flea-to-gerbil ratio at the beginning of the reproductive summer. Over-winter net growth is favored by high density. We estimate that on average 35% of the gerbil population survives the winter. Our study shows that hierarchical Bayesian models can be useful in extracting ecobiological information from observational data.     

非损伤性取样法在保护遗传学研究中的应用

通过对非损伤怀取样法的种类以及毛发的非损伤性取样分析方法、口腔脱落细胞的非损伤性取样分析方法、馆藏标本的非损伤性取样分析方法、粪便的非损伤性取样分析方法和其它样品的非损伤性取样分析方法的介绍,就非损伤取样法在保护遗传学中的应用及其优点、局限性和应用前景作了介绍、分析和讨论。     

A Bayesian approach for the analysis of panel-count data with dependent termination

We consider modeling and Bayesian analysis for panel-count data when the termination time for each subject may depend on its history of the recurrent events. We propose a fully specified semiparametric model for the joint distribution of the recurrent events and the termination time. For this model, we provide a natural motivation, derive several novel properties, and develop a Bayesian analysis based on a Markov chain Monte Carlo algorithm. Comparisons are made to other existing models and methods for panel-count data. We demonstrate the usefulness of our new models and methodologies through the reanalysis of a data set from a clinical trial.