Comparative analysis of secondary structure of insect mitochondrial small subunit ribosomal RNA using maximum weighted matching

Comparative analysis is the preferred method of inferring RNA secondary structure, but its use requires considerable expertise and manual effort. As the importance of secondary structure for accurate sequence alignment and phylogenetic analysis becomes increasingly realised, the need for secondary structure models for diverse taxonomic groups becomes more pressing. The number of available structures bears little relation to the relative diversity or importance of the different taxonomic groups. Insects, for example, comprise the largest group of animals and yet are very poorly represented in secondary structure databases. This paper explores the utility of maximum weighted matching (MWM) to help automate the process of comparative analysis by inferring secondary structure for insect mitochondrial small subunit (12S) rRNA sequences. By combining information on correlated changes in substitutions and helix dot plots, MWM can rapidly generate plausible models of secondary structure. These models can be further refined using standard comparative techniques. This paper presents a secondary structure model for insect 12S rRNA based on an alignment of 225 insect sequences and an alignment for 16 exemplar insect sequences. This alignment is used as a template for a web server that automatically generates secondary structures for insect sequences.     

Phylogeography of speciation: allopatric divergence and secondary contact between outcrossing and selfing Clarkia

The origins of hybrid zones between parapatric taxa have been of particular interest for understanding the evolution of reproductive isolation and the geographic context of species divergence. One challenge has been to distinguish between allopatric divergence (followed by secondary contact) versus primary intergradation (parapatric speciation) as alternative divergence histories. Here, we use complementary phylogeographic and population genetic analyses to investigate the recent divergence of two subspecies of Clarkia xantiana and the formation of a hybrid zone within the narrow region of sympatry. We tested alternative phylogeographic models of divergence using approximate Bayesian computation (ABC) and found strong support for a secondary contact model and little support for a model allowing for gene flow throughout the divergence process (i.e. primary intergradation). Two independent methods for inferring the ancestral geography of each subspecies, one based on probabilistic character state reconstructions and the other on palaeo-distribution modelling, also support a model of divergence in allopatry and range expansion leading to secondary contact. The membership of individuals to genetic clusters suggests geographic substructure within each taxon where allopatric and sympatric samples are primarily found in separate clusters. We also observed coincidence and concordance of genetic clines across three types of molecular markers, which suggests that there is a strong barrier to gene flow. Taken together, our results provide evidence for allopatric divergence followed by range expansion leading to secondary contact. The location of refugial populations and the directionality of range expansion are consistent with expectations based on climate change since the last glacial maximum. Our approach also illustrates the utility of combining phylogeographic hypothesis testing with species distribution modelling and fine-scale population genetic analyses for inferring the geography of the divergence process.     

The joint allele frequency spectrum of multiple populations: a coalescent theory approach

The allele frequency spectrum is a series of statistics that describe genetic polymorphism, and is commonly used for inferring population genetic parameters and detecting natural selection. Population genetic theory on the allele frequency spectrum for a single population has been well studied using both coalescent theory and diffusion equations. Recently, the theory was extended to the joint allele frequency spectrum (JAFS) for three populations using diffusion equations and was shown to be very useful in inferring human demographic history. In this paper, I show that the JAFS can be analytically derived with coalescent theory for a basic model of two isolated populations and then extended to multiple populations and various complex scenarios, such as those involving population growth and bottleneck, migration, and positive selection. Simulation study is used to demonstrate the accuracy and applicability of the theoretical model. The coalescent theory-based approach for the JAFS can characterize the demographic history with comprehensive statistical models as the diffusion approach does, and in addition gains several novel advantages: the computational complexity of calculating the JAFS with coalescent theory is reduced, and thus it is feasible to analytically obtain the JAFS for multiple populations; the hitchhiking effect can be efficiently modeled in coalescent theory, enabling the development of methodologies for detecting selection via multi-population polymorphism data. As an alternative to the diffusion approximation approach, the coalescent theory for the JAFS also provides a foundation for population genetic inference with the advent of large-scale genomic polymorphism data.     

作物品种间杂种优势遗传分析的新方法

本文提出了分析双列杂交试验资料的两个遗传模型。第一个模型包括加性、显性和母体效应;第二个模型只包括简单的加性和显性效应。还介绍了分析杂种优势、估算遗传方差分量以及预测遗传效应值的相应统计分析方法。用所介绍的遗传模型和分析方法以及常用的Griffing配合力分析方法,分析了棉花6个品种双列杂交的产量性状,并进一步比较了不同方法的分析结果。采用本文所介绍的遗传模型和分析方法,可以克服用Griffing的配合力模型及其方法分析杂种优势和配合力遗传表现所存在的局限性。     

Bayesian analysis of population structure based on linked molecular information

The Bayesian model-based approach to inferring hidden genetic population structures using multilocus molecular markers has become a popular tool within certain branches of biology. In particular, it has been shown that heterogeneous data arising from genetically dissimilar latent groups of individuals can be effectively modelled using an unsupervised classification formulation. However, most currently employed models ignore potential linkage within the employed molecular information, and can therefore lead to biased inferences under certain circumstances. Utilizing the general theory of graphical models, we develop a framework that accounts for dependences both within linked molecular marker loci and DNA sequence data. Due to a high level of sequence conservation among eukaryotic species, the latter aspect is particularly relevant for analyzing rapidly evolving microbial species. The advantages of incorporating the dependence due to linkage in the classification models are illustrated by analyses of both simulated data and real samples of Bacillus cereus.     

GENIE: estimating demographic history from molecular phylogenies

GENIE implements a statistical framework for inferring the demographic history of a population from phylogenies that have been reconstructed from sampled DNA sequences. The methods are based on population genetic models known collectively as coalescent theory. AVAILABILITY: GENIE is available from http://evolve.zoo.ox.ac.uk. All popular operating systems are supported.     

Method for inferring and extracting reliable genetic interactions from time-series profile of gene expression

Recent advances in technologies such as DNA microarrays have provided an abundance of gene expression data on the genomic scale. One of the most important projects in the post-genome-era is the systemic identification of gene expression networks. However, inferring internal gene expression structure from experimentally observed time-series data are an inverse problem. We have therefore developed a system for inferring network candidates based on experimental observations. Moreover, we have proposed an analytical method for extracting common core binomial genetic interactions from various network candidates. Common core binomial genetic interactions are reliable interactions with a higher possibility of existence, and are important for understanding the dynamic behavior of gene expression networks. Here, we discuss an efficient method for inferring genetic interactions that combines a Step-by-step strategy (Y. Maki, Y. Takahashi, Y. Arikawa, S. Watanabe, K. Aoshima, Y. Eguchi, T. Ueda, S. Aburatani, S. Kuhara, M. Okamoto, An integrated comprehensive workbench for inferring genetic networks: Voyagene, Journal of Bioinformatics and Computational Biology 2(3) (2004) 533.) with an analysis method for extracting common core binomial genetic interactions.     

An empirical Bayes approach to inferring large-scale gene association networks

MOTIVATION: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. METHODS: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. RESULTS: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes.     

Inference of S-system models of genetic networks by solving one-dimensional function optimization problems

Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments.     

Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm

MOTIVATION: To resolve the high-dimensionality of the genetic network inference problem in the S-system model, a problem decomposition strategy has been proposed. While this strategy certainly shows promise, it cannot provide a model readily applicable to the computational simulation of the genetic network when the given time-series data contain measurement noise. This is a significant limitation of the problem decomposition, given that our analysis and understanding of the genetic network depend on the computational simulation. RESULTS: We propose a new method for inferring S-system models of large-scale genetic networks. The proposed method is based on the problem decomposition strategy and a cooperative coevolutionary algorithm. As the subproblems divided by the problem decomposition strategy are solved simultaneously using the cooperative coevolutionary algorithm, the proposed method can be used to infer any S-system model ready for computational simulation. To verify the effectiveness of the proposed method, we apply it to two artificial genetic network inference problems. Finally, the proposed method is used to analyze the actual DNA microarray data.     

Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.     

A first step towards inferring levels of long-distance dispersal during past expansions

Improving the realism of spatially explicit demographic models is important for better inferring the history of past populations and for understanding the genetic bases of adaptation and speciation. One particular type of demographic event to take into account is long-distance dispersal (LDD). The goals of this study are to explore the impact of various levels of LDD on genetic diversity and to show to what extent LDD levels can be correctly inferred from multilocus data sets using an approximate Bayesian computation approach. We therefore incorporated LDD into a 2D stepping stone forward simulation framework coupled to a coalescent backward simulation step to generate genetic diversity at 100 microsatellite markers under various demographic conditions relevant to recent human evolution. Our results confirm that LDD considerably increases genetic diversity within demes and decreases levels of diversity between demes. By controlling the spatial occurrence of LDD, it appears that LDD events occurring during a phase of range expansion into new territories are more important in maintaining genetic diversity than those occurring in the wake of the expansion or when colonization is over. We also show that it is possible to infer whether LDD has occurred during a range expansion, but our results suggest that one can only approximately estimate the extent of LDD based on genetic summary statistics.     

From phenotype to genotype: a Bayesian solution

The study of biological systems commonly depends on inferring the state of a 'hidden' variable, such as an underlying genotype, from that of an 'observed' variable, such as an expressed phenotype. However, this cannot be achieved using traditional quantitative methods when more than one genetic mechanism exists for a single observable phenotype. Using a novel latent class Bayesian model, it is possible to infer the prevalence of different genetic elements in a population given a sample of phenotypes. As an exemplar, data comprising phenotypic resistance to six antimicrobials obtained from passive surveillance of Salmonella Typhimurium DT104 are analysed to infer the prevalence of individual resistance genes, as well as the prevalence of a genomic island known as SGI1 and its variants. Three competing models are fitted to the data and distinguished between using posterior predictive p-values to assess their ability to predict the observed number of unique phenotypes. The results suggest that several SGI1 variants circulate in a few fixed forms through the population from which our data were derived. The methods presented could be applied to other types of phenotypic data, and represent a useful and generic mechanism of inferring the genetic population structure of organisms.     

The influence of nonrandom extra‐pair paternity on heritability estimates derived from wild pedigrees

Quantitative genetic analysis is often fundamental for understanding evolutionary processes in wild populations. Avian populations provide a model system due to the relative ease of inferring relatedness among individuals through observation. However, extra‐pair paternity (EPP) creates erroneous links within the social pedigree. Previous work has suggested this causes minor underestimation of heritability if paternal misassignment is random and hence not influenced by the trait being studied. Nevertheless, much literature suggests numerous traits are associated with EPP and the accuracy of heritability estimates for such traits remains unexplored. We show analytically how nonrandom pedigree errors can influence heritability estimates. Then, combining empirical data from a large great tit (Parus major) pedigree with simulations, we assess how heritability estimates derived from social pedigrees change depending on the mode of the relationship between EPP and the focal trait. We show that the magnitude of the underestimation is typically small (<15%). Hence, our analyses suggest that quantitative genetic inference from pedigrees derived from observations of social relationships is relatively robust; our approach also provides a widely applicable method for assessing the consequences of nonrandom EPP.     

草鱼种群SSR分析中样本量及标记数量对遗传多度的影响

利用45对微卫星分子标记（SSR）,以草鱼(Ctenopharyngodon idellus)自然群体为实验材料,探讨野生群体遗传多样性研究中所需的最适样本量与标记量。实验设置6个样本量梯度,9个标记量梯度。对等位基因数（Na）、有效等位基因数（Ne）、观察杂合度（Ho）、期望杂合度（He）等遗传多样性指标的变化趋势进行统计分析。结果表明,样本量、微卫星标记的数量和多态性水平对群体遗传多样性均有较大的影响,其中等位基因数与样本量大小呈显著正相关,而杂合度随标记量的增多而剧烈波动。当取样量大于40,标记量大于25时,各遗传参数值趋于稳定。因此,在应用微卫星标记对水产动物自然群体的遗传学研究中,要根据所研究种类的特点,尽可能采样40尾以上,采用25个以上标记,避免由人为选择的偏差对群体遗传多样性水平的正确评估所造成的影响。同时根据上述研究结果,对陕西草鱼自然群体进行了遗传多样性的评估,结果显示该群体平均等位基因数（MNA）、平均有效等位基因数、平均观测杂合度、平均期望杂合度分别为7.26、4.21、0.73、0.68,认为该群体具有较高的遗传多样性。     

Perspectives on genetic animal models of serotonin toxicity

Serotonin syndrome, or serotonin toxicity, is a serious disorder attributable to exaggerated serotonergic function in the brain, most commonly after antidepressant overdose or after combining several psychotropic medications. Similar condition (serotonin syndrome-like behavior) can be evoked in animals experimentally, following administration of serotonergic drugs. In addition to pharmacological stimulation, some genetic and other factors may contribute to serotonin toxicity, prompting the need for new experimental genetic models relevant to this disorder. Here we discuss current problems and perspectives regarding genetic animal models of serotonin-related syndromes, and outline the potential utility of these models in experimental neurochemistry and clinical research.     

Landscape influences on dispersal behaviour: a theoretical model and empirical test using the fire salamander,Salamandra infraimmaculata

When populations reside within a heterogeneous landscape, isolation by distance may not be a good predictor of genetic divergence if dispersal behaviour and therefore gene flow depend on landscape features. Commonly used approaches linking landscape features to gene flow include the least cost path (LCP), random walk (RW), and isolation by resistance (IBR) models. However, none of these models is likely to be the most appropriate for all species and in all environments. We compared the performance of LCP, RW and IBR models of dispersal with the aid of simulations conducted on artificially generated landscapes. We also applied each model to empirical data on the landscape genetics of the endangered fire salamander, Salamandra infraimmaculata, in northern Israel, where conservation planning requires an understanding of the dispersal corridors. Our simulations demonstrate that wide dispersal corridors of the low-cost environment facilitate dispersal in the IBR model, but inhibit dispersal in the RW model. In our empirical study, IBR explained the genetic divergence better than the LCP and RW models (partial Mantel correlation 0.413 for IBR, compared to 0.212 for LCP, and 0.340 for RW). Overall dispersal cost in salamanders was also well predicted by landscape feature slope steepness (76 %), and elevation (24 %). We conclude that fire salamander dispersal is well characterised by IBR predictions. Together with our simulation findings, these results indicate that wide dispersal corridors facilitate, rather than hinder, salamander dispersal. Comparison of genetic data to dispersal model outputs can be a useful technique in inferring dispersal behaviour from population genetic data.     

phylin: an r package for phylogeographic interpolation

phylin is a package for the r programming environment which offers different methods to spatially interpolate genetic information from phylogeographic data. These interpolations can be used to predict the spatial occurrence of different lineages within a phylogeny using a modified method of kriging, which allows the usage of a genetic distance matrix to derive a model of spatial dependence. phylin improves the available methods to generate interpolated surfaces from a phylogenetic trees by assessing the autocorrelation structure of the genetic information, interpolating the genetic data based on a statistical model, estimating the uncertainty of the predictions and identifying lineage occurrence and contact zones probability without projection of pairwise genetic distances into mid‐points between sample locations. The package also includes methods to plot interpolation surfaces and provide summary tables from the generated data and models. We provide an example of the usefulness of this tool by inferring the spatial occurrence of distinct historical evolutionary lineages of the Lataste's viper (Vipera latastei Boscá, 1878) in the Iberian Peninsula and identifying potential contact areas. The maps of phylogenetic patterns obtained with these methods provide a spatial context to test hypotheses related to processes underlying the geographic distribution of genetic diversity and to inform conservation planning.     

Bayesian inference for identifying interaction rules in moving animal groups

The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of "universal" classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed.     

Inferring evolutionary processes from phylogenies

Evolutionary processes shape the regular trends of evolution and are responsible for the diversity and distribution of contemporary species. They include correlated evolutionary change and trajectories of trait evolution, convergent and parallel evolution, differential rates of evolution, speciation and extinction, the order and direction of change in characters, and the nature of the evolutionary process itself—does change accumulate gradually, episodically, or in punctuational bursts. Phylogenies, in combination with information on species, contain the imprint of these historical evolutionary processes. By applying comparative methods based upon statistical models of evolution to well resolved phylogenies, it is possible to infer the historical evolutionary processes that must have existed in the past, given the patterns of diversity seen in the present. I describe a set of maximum likelihood statistical methods for inferring such processes. The methods estimate parameters of statistical models for inferring correlated evolutionary change in continuously varying characters, for detecting correlated evolution in discrete characters, for estimating rates of evolution, and for investigating the nature of the evolutionary process itself. They also anticipate the wealth of information becoming available to biological scientists from genetic studies that pin down relationships among organisms with unprecedented accuracy.