Bayesian Network Reconstruction Using Systems Genetics Data: Comparison of MCMC Methods

Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.     

Body mass in comparative primatology

Data are presented on adult body mass for 230 of 249 primate species, based on a review of the literature and previously unpublished data. The issues involved in collecting data on adult body mass are discussed, including the definition of adults, the effects of habitat and pregnancy, the strategy for pooling data on single species from multiple studies, and use of an appropriate number of significant figures. An analysis of variability in body mass indicates that the coefficient of variation for body mass increases with increasing species mean mass. Evaluation of several previous body mass reviews reveals a number of shortcomings with data that have been used often in comparative studies.     

Bayesian species delimitation in West African forest geckos (Hemidactylus fasciatus)

Genealogical data are an important source of evidence for delimiting species, yet few statistical methods are available for calculating the probabilities associated with different species delimitations. Bayesian species delimitation uses reversible-jump Markov chain Monte Carlo (rjMCMC) in conjunction with a user-specified guide tree to estimate the posterior distribution for species delimitation models containing different numbers of species. We apply Bayesian species delimitation to investigate the speciation history of forest geckos (Hemidactylus fasciatus) from tropical West Africa using five nuclear loci (and mtDNA) for 51 specimens representing 10 populations. We find that species diversity in H. fasciatus is currently underestimated, and describe three new species to reflect the most conservative estimate for the number of species in this complex. We examine the impact of the guide tree, and the prior distributions on ancestral population sizes (θ) and root age (τ₀), on the posterior probabilities for species delimitation. Mis-specification of the guide tree or the prior distribution for θ can result in strong support for models containing more species. We describe a new statistic for summarizing the posterior distribution of species delimitation models, called speciation probabilities, which summarize the posterior support for each speciation event on the starting guide tree.     

Ancient DNA enables timing of the pleistocene origin and holocene expansion of two adélie penguin lineages in antarctica

The timing of divergent events in history is one of the central goals of contemporary evolutionary biology. Such studies are however dependent on accurate evolutionary rates. Recent developments in ancient DNA analysis enable the estimation of more accurate evolutionary rates and therefore more accurate timing of divergence events. Consequently, this leads to a better understanding of changes in populations through time. We use an evolutionary rate calculated from ancient DNA of Adélie penguins (Pygoscelis adeliae) to time divergent events in their history. We report the presence of two distinct and highly variable mitochondrial DNA lineages and track changes in these lineages through space and time. When the ancient DNA and the phylogenetic rates are used to estimate the time of origin of the lineages, two very different estimates resulted. In addition, these same rates provide very different estimates of the time of expansion of these lineages. We suggest that the rate calculated from ancient DNA is more consistent with the glacial history of Antarctica and requires fewer assumptions than does a narrative based on the phylogenetic rate. Finally, we suggest that our study indicates an important new role for ancient DNA studies in the timing of divergent events in history.     

Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.     

From bridewealth to dowry?

Significant amounts of wealth have been exchanged as part of marriage settlements throughout history. Although various models have been proposed for interpreting these practices, their development over time has not been investigated systematically. In this paper we use a Bayesian MCMC phylogenetic comparative approach to reconstruct the evolution of two forms of wealth transfers at marriage, dowry and bridewealth, for 51 Indo-European cultural groups. Results indicate that dowry is more likely to have been the ancestral practice, and that a minimum of four changes to bridewealth is necessary to explain the observed distribution of the two states across the cultural groups. A preliminary version of this paper was presented at the 16^th meeting of the Human Behavior and Evolution Society (HBES), Berlin 2004. Mark Pagel and Andrew Meade provided the software, the coded linguistic data, and valuable advice on their use. LF is funded by Fondazione Ing. Aldo Gini (Italy), the ESRC (UK), and the UCL Graduate School (UK). Five anonymous reviewers provided helpful comments. The authors are based at the Department of Anthropology of UCL and members of the AHRC Centre for the Evolutionary Analysis of Cultural Behaviour (CEACB). LF is studying towards a PhD, CH is a CEACB Senior Research Fellow, and RM is Professor of Evolutionary Anthropology.