A coalescence approach to gene conversion

In this paper we develop a coalescent model with intralocus gene conversion. Such models are of increasing importance in the analysis of intralocus variability and linkage disequilibrium. We derive the distribution of the waiting time until a gene conversion event occurs in a sample in terms of the distribution of the length of the transferred segment, zeta. We do not assume any specific form of the distribution of zeta. Further, given that a gene conversion event occurs we find the distribution of (sigma, tau), the end points of the transferred segment and derive results on correlations between local trees in positions chi(1) and chi(2). Among other results we show that the correlation between the branch lengths of two local trees in the coalescent with gene conversion (and no recombination) decreases toward a nonzero constant when the distance between chi(1) and chi(2) increases. Finally, we show that a model including both recombination and gene conversion might account for the lack of intralocus associations found in, e.g., Drosophila melanogaster.     

Recombination,selection, and the evolution of tandem gene arrays

Multigene families—immunity genes or sensory receptors, for instance—are often subject to diversifying selection. Allelic diversity may be favored not only through balancing or frequency-dependent selection at individual loci but also by associating different alleles in multicopy gene families. Using a combination of analytical calculations and simulations, we explored a population genetic model of epistatic selection and unequal recombination, where a trade-off exists between the benefit of allelic diversity and the cost of copy abundance. Starting from the neutral case, where we showed that gene copy number is Gamma distributed at equilibrium, we derived also the mean and shape of the limiting distribution under selection. Considering a more general model, which includes variable population size and population substructure, we explored by simulations mean fitness and some summary statistics of the copy number distribution. We determined the relative effects of selection, recombination, and demographic parameters in maintaining allelic diversity and shaping the mean fitness of a population. One way to control the variance of copy number is by lowering the rate of unequal recombination. Indeed, when encoding recombination by a rate modifier locus, we observe exactly this prediction. Finally, we analyzed the empirical copy number distribution of 3 genes in human and estimated recombination and selection parameters of our model.     

The coalescent with gene conversion

In this article we develop a coalescent model with intralocus gene conversion. The distribution of the tract length is geometric in concordance with results published in the literature. We derive a simulation scheme and deduce a number of analytical results for this coalescent with gene conversion. We compare patterns of variability in samples simulated according to the coalescent with recombination with similar patterns simulated according to the coalescent with gene conversion alone. Further, an expression for the expected number of topology shifts in a sample of present-day sequences caused by gene conversion events is derived.     

Habitat use,but not gene flow,is influenced by human activities in two ecotypes of Egyptian fruit bat (Rousettus aegyptiacus)

Understanding the ecological, behavioural and evolutionary response of organisms to changing environments is of primary importance in a human‐altered world. It is crucial to elucidate how human activities alter gene flow and what are the consequences for the genetic structure of a species. We studied two lineages of the Egyptian fruit bat (Rousettus aegyptiacus) throughout the contact zone between mesic and arid Ecozones in the Middle East to evaluate the species' response to the growing proportion of human‐altered habitats in the desert. We integrated population genetics, morphometrics and movement ecology to analyse population structure, morphological variation and habitat use from GPS‐ or radio‐tagged individuals from both desert and Mediterranean areas. We classified the spatial distribution and environmental stratification by describing physical–geographical conditions and land cover. We analysed this information to estimate patch occupancy and used an isolation‐by‐resistance approach to model gene flow patterns. Our results suggest that lineages from desert and Mediterranean habitats, despite their admixture, are isolated by environment and by adaptation supporting their classification as ecotypes. We found a positive effect of human‐altered habitats on patch occupancy and habitat use of fruit bats by increasing the availability of roosting and foraging areas. While this commensalism promotes the distribution of fruit bats throughout the Middle East, gene flow between colonies has not been altered by human activities. This discrepancy between habitat use and gene flow patterns may, therefore, be explained by the breeding system of the species and modifications of natal dispersal patterns.     

Statistical estimation of gene expression using multiple laser scans of microarrays

We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias. AVAILABILITY: FORTRAN 90 code for implementing the method can be obtained from the authors.     

Identifying the rooted species tree from the distribution of unrooted gene trees under the coalescent

Gene trees are evolutionary trees representing the ancestry of genes sampled from multiple populations. Species trees represent populations of individuals—each with many genes—splitting into new populations or species. The coalescent process, which models ancestry of gene copies within populations, is often used to model the probability distribution of gene trees given a fixed species tree. This multispecies coalescent model provides a framework for phylogeneticists to infer species trees from gene trees using maximum likelihood or Bayesian approaches. Because the coalescent models a branching process over time, all trees are typically assumed to be rooted in this setting. Often, however, gene trees inferred by traditional phylogenetic methods are unrooted. We investigate probabilities of unrooted gene trees under the multispecies coalescent model. We show that when there are four species with one gene sampled per species, the distribution of unrooted gene tree topologies identifies the unrooted species tree topology and some, but not all, information in the species tree edges (branch lengths). The location of the root on the species tree is not identifiable in this situation. However, for 5 or more species with one gene sampled per species, we show that the distribution of unrooted gene tree topologies identifies the rooted species tree topology and all its internal branch lengths. The length of any pendant branch leading to a leaf of the species tree is also identifiable for any species from which more than one gene is sampled.     

Expression of an immediate early gene, IEX-1, in human tissues

IEX-1 is an immediate early gene that is induced by ionizing radiation, ultraviolet radiation, and a variety of growth factors. It plays an important role in the regulation of cellular growth. Earlier, we performed studies on the distribution of IEX-1 messenger RNA in different tissues and on the subcellular localization of IEX-1 protein. No reports, however, have appeared concerning the distribution of IEX-1 protein in a variety of human tissues. We raised a polyclonal antibody against a synthetic IEX-1 peptide (amino acids 51-75) and used the antibody to study the distribution of the protein in human tissues. We demonstrate that IEX-1 is strongly expressed in epithelia of the skin, trachea, gastrointestinal, and genitourinary systems, as well as in the pancreas and breast. Endothelial cells within the vasculature of most tissue/organs also strongly express IEX-1. Liver, lung, lymph nodes, and placenta stain weakly. No IEX-1 epitopes were detected in the thymus, testes, ovary, myocardium, skeletal muscle, or spleen. We conclude that IEX-1 is widely expressed in epithelial and endocrine tissues, as well as in vascular endothelium.     

Rates and patterns of gene duplication and loss in the human genome

Gene duplication has certainly played a major role in structuring vertebrate genomes but the extent and nature of the duplication events involved remains controversial. A recent study identified two major episodes of gene duplication: one episode of putative genome duplication ca. 500 Myr ago and a more recent gene-family expansion attributed to segmental or tandem duplications. We confirm this pattern using methods not reliant on molecular clocks for individual gene families. However, analysis of a simple model of the birth-death process suggests that the apparent recent episode of duplication is an artefact of the birth-death process. We show that a constant-rate birth-death model is appropriate for gene duplication data, allowing us to estimate the rate of gene duplication and loss in the vertebrate genome over the last 200 Myr (0.00115 and 0.00740 Myr(-1) lineage(-1), respectively). Finally, we show that increasing rates of gene loss reduce the impact of a genome-wide duplication event on the distribution of gene duplications through time.     

The wrong way to think about gene conversion?

Summary The sex circle model for crossing over and gene conversion proposed by Stahl has a number of properties which are in conflict with a considerable body of recombination data in fungi. The model is unable to explain the observed frequencies of gene conversion and postmeiotic segregation for particular mutants in several species. It does not provide an explanation for fine structure map expansion, nor does it account satisfactorily for the polarised distribution of outside markers amongst allelic recombinants.     

A hybrid gene team model and its application to genome analysis

It is well-known that functionally related genes occur in a physically clustered form, especially operons in bacteria. By leveraging on this fact, there has recently been an interesting problem formulation known as gene team model, which searches for a set of genes that co-occur in a pair of closely related genomes. However, many gene teams, even experimentally verified operons, frequently scatter within other genomes. Thus, the gene team model should be refined to reflect this observation. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraints. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. We also compared the result from our hybrid model to those from the traditional gene team model. We also show that predicted gene teams can be used for various genome analysis: operon prediction, phylogenetic analysis of organisms, contextual sequence analysis and genome annotation. Our program is fast enough to provide a service on the web at http://platcom.informatics.indiana.edu/platcom/. Users can select any combination of 97 genomes to predict gene teams.     

The probability distribution of ranked gene trees on a species tree

The properties of random gene tree topologies have recently been studied under a coalescent model that treats a species tree as a fixed parameter. Here we develop the analogous theory for random ranked gene tree topologies, in which both the topology and the sequence of coalescences for a random gene tree are considered. We derive the probability distribution of ranked gene tree topologies conditional on a fixed species tree. We then show that similar to the unranked case, ranked gene trees that do not match either the ranking or the topology of the species tree can have greater probability than the matching ranked gene tree.     

Optimal gene partition into operons correlates with gene functional order

Gene arrangement into operons varies between bacterial species. Genes in a given system can be on one operon in some organisms and on several operons in other organisms. Existing theories explain why genes that work together should be on the same operon, since this allows for advantageous lateral gene transfer and accurate stoichiometry. But what causes the frequent separation into multiple operons of co-regulated genes that act together in a pathway? Here we suggest that separation is due to benefits made possible by differential regulation of each operon. We present a simple mathematical model for the optimal distribution of genes into operons based on a balance of the cost of operons and the benefit of regulation that provides 'just-when-needed' temporal order. The analysis predicts that genes are arranged such that genes on the same operon do not skip functional steps in the pathway. This prediction is supported by genomic data from 137 bacterial genomes. Our work suggests that gene arrangement is not only the result of random historical drift, genome re-arrangement and gene transfer, but has elements that are solutions of an evolutionary optimization problem. Thus gene functional order may be inferred by analyzing the operon structure across different genomes.     

Bayesian infinite mixture model based clustering of gene expression profiles

MOTIVATION: The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. RESULTS: We developed a clustering procedure based on the Bayesian infinite mixture model and applied it to clustering gene expression profiles. Clusters of genes with similar expression patterns are identified from the posterior distribution of clusterings defined implicitly by the stochastic data-generation model. The posterior distribution of clusterings is estimated by a Gibbs sampler. We summarized the posterior distribution of clusterings by calculating posterior pairwise probabilities of co-expression and used the complete linkage principle to create clusters. This approach has several advantages over usual clustering procedures. The analysis allows for incorporation of a reasonable probabilistic model for generating data. The method does not require specifying the number of clusters and resulting optimal clustering is obtained by averaging over models with all possible numbers of clusters. Expression profiles that are not similar to any other profile are automatically detected, the method incorporates experimental replicates, and it can be extended to accommodate missing data. This approach represents a qualitative shift in the model-based cluster analysis of expression data because it allows for incorporation of uncertainties involved in the model selection in the final assessment of confidence in similarities of expression profiles. We also demonstrated the importance of incorporating the information on experimental variability into the clustering model. AVAILABILITY: The MS Windows(TM) based program implementing the Gibbs sampler and supplemental material is available at http://homepages.uc.edu/~medvedm/BioinformaticsSupplement.htm CONTACT: medvedm@email.uc.edu     

A method for two-dimensional registration and construction of the two-dimensional atlas of gene expression patterns in situ

We apply the fast redundant dyadic wavelet transform to the spatial registration of two-dimensional gene expression patterns of 736 Drosophila melanogaster embryos. This method is superior to the Fourier transform or windowed Fourier transform because of its ability to reduce noise and is of high resolution. In registration of the dataset we use two cost functions based on computing the Euclidean or Mahalanobis distance. The algorithm shows a high level of accuracy. For early temporal classes the cost function based on Mahalanobis distance gives better results. We have reported a method for construction of an integrated dataset elsewhere. In this paper the method is extended to the two-dimensional case. The procedure for data assembly provides for the preservation of some aspects of the nuclear structure of a two-dimensional gene expression pattern. It is based on creating an averaged model that reproduces the spatial distribution of nuclei over the embryo image. The average concentrations of each protein in each averaged nucleus are computed from the series of embryos of the same age.