Bayesian inference of population size history from multiple loci

<p>Abstract</p> <p>Background</p> <p>Effective population size (<it>N</it>_<it>e</it>) is related to genetic variability and is a basic parameter in many models of population genetics. A number of methods for inferring current and past population sizes from genetic data have been developed since JFC Kingman introduced the n-coalescent in 1982. Here we present the Extended Bayesian Skyline Plot, a non-parametric Bayesian Markov chain Monte Carlo algorithm that extends a previous coalescent-based method in several ways, including the ability to analyze multiple loci.</p> <p>Results</p> <p>Through extensive simulations we show the accuracy and limitations of inferring population size as a function of the amount of data, including recovering information about evolutionary bottlenecks. We also analyzed two real data sets to demonstrate the behavior of the new method; a single gene Hepatitis C virus data set sampled from Egypt and a 10 locus <it>Drosophila ananassae </it>data set representing 16 different populations.</p> <p>Conclusion</p> <p>The results demonstrate the essential role of multiple loci in recovering population size dynamics. Multi-locus data from a small number of individuals can precisely recover past bottlenecks in population size which can not be characterized by analysis of a single locus. We also demonstrate that sequence data quality is important because even moderate levels of sequencing errors result in a considerable decrease in estimation accuracy for realistic levels of population genetic variability.</p

Bayesian Inference of Species Trees from Multilocus Data

Until recently, it has been common practice for a phylogenetic analysis to use a single gene sequence from a single individual organism as a proxy for an entire species. With technological advances, it is now becoming more common to collect data sets containing multiple gene loci and multiple individuals per species. These data sets often reveal the need to directly model intraspecies polymorphism and incomplete lineage sorting in phylogenetic estimation procedures

Calibrated Tree Priors for Relaxed Phylogenetics and Divergence Time Estimation

The use of fossil evidence to calibrate divergence time estimation has a long history. More recently Bayesian MCMC has become the dominant method of divergence time estimation and fossil evidence has been re-interpreted as the specification of prior distributions on the divergence times of calibration nodes. These so-called "soft calibrations" have become widely used but the statistical properties of calibrated tree priors in a Bayesian setting has not been carefully investigated. Here we clarify that calibration densities, such as those defined in BEAST 1.5, do not represent the marginal prior distribution of the calibration node. We illustrate this with a number of analytical results on small trees. We also describe an alternative construction for a calibrated Yule prior on trees that allows direct specification of the marginal prior distribution of the calibrated divergence time, with or without the restriction of monophyly. This method requires the computation of the Yule prior conditional on the height of the divergence being calibrated. Unfortunately, a practical solution for multiple calibrations remains elusive. Our results suggest that direct estimation of the prior induced by specifying multiple calibration densities should be a prerequisite of any divergence time dating analysis

How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories

Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, like the seminal work of Li and Durbin (Nature, 2011), attempt to solve this problem using sequence data but have no rigorous guarantees. Determining the amount of data needed to correctly reconstruct population histories is a major challenge. Using a variety of tools from information theory, the theory of extremal polynomials, and approximation theory, we prove new sharp information-theoretic lower bounds on the problem of reconstructing population structure -- the history of multiple subpopulations that merge, split and change sizes over time. Our lower bounds are exponential in the number of subpopulations, even when reconstructing recent histories. We demonstrate the sharpness of our lower bounds by providing algorithms for distinguishing and learning population histories with matching dependence on the number of subpopulations. Along the way and of independent interest, we essentially determine the optimal number of samples needed to learn an exponential mixture distribution information-theoretically, proving the upper bound by analyzing natural (and efficient) algorithms for this problem.Comment: 38 pages, Appeared in RECOMB 201

Intellectual Property and Public Health – A White Paper

On October 26, 2012, the University of Akron School of Law’s Center for Intellectual Property and Technology hosted its Sixth Annual IP Scholars Forum. In attendance were thirteen legal scholars with expertise and an interest in IP and public health who met to discuss problems and potential solutions at the intersection of these fields. This report summarizes this discussion by describing the problems raised, areas of agreement and disagreement between the participants, suggestions and solutions made by participants and the subsequent evaluations of these suggestions and solutions. Led by the moderator, participants at the Forum focused generally on three broad questions. First, are there alternatives to either the patent system or specific patent doctrines that can provide or help provide sufficient incentives for health-related innovation? Second, is health information being used proprietarily and if so, is this type of protection appropriate? Third, does IP conflict with other non-IP values that are important in health and how does or can IP law help resolve these conflicts? This report addresses each of these questions in turn

Bayesian phylogenetics with BEAUti and the BEAST 1.7

Computational evolutionary biology, statistical phylogenetics and coalescent-based population genetics are becoming increasingly central to the analysis and understanding of molecular sequence data. We present the Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package version 1.7, which implements a family of Markov chain Monte Carlo (MCMC) algorithms for Bayesian phylogenetic inference, divergence time dating, coalescent analysis, phylogeography and related molecular evolutionary analyses. This package includes an enhanced graphical user interface program called Bayesian Evolutionary Analysis Utility (BEAUti) that enables access to advanced models for molecular sequence and phenotypic trait evolution that were previously available to developers only. The package also provides new tools for visualizing and summarizing multispecies coalescent and phylogeographic analyses. BEAUti and BEAST 1.7 are open source under the GNU lesser general public license and available at http://beast-mcmc.googlecode.com and http://beast.bio.ed.ac.u

Intellectual Property and Public Health - A White Paper

On October 26, 2012, The University of Akron School of Law\u27s Center for Intellectual Property and Technology hosted its Sixth Annual IP Scholars Forum. In attendance were thirteen legal scholars with expertise and an interest in IP and public health who met to discuss problems and potential solutions at the intersection of these fields. This report summarizes this discussion by describing the problems raised, areas of agreement and disagreement between the participants, suggestions and solutions made by participants, and the subsequent evaluations of these suggestions and solutions. Led by the moderator, participants at the Forum focused generally on three broad questions. First, are there alternatives to the patent system or specific patent doctrines that can provide or help provide sufficient incentives for health-related innovation? Second, is health information being used proprietarily, and if so, is this use appropriate? Third, does IP conflict with other non-IP values that are important in health, and how does or how can IP law help resolve these conflicts? This report addresses each of these questions in turn

Phylogenetic Codivergence Supports Coevolution of Mimetic Heliconius Butterflies

The unpalatable and warning-patterned butterflies _Heliconius erato_ and _Heliconius melpomene_ provide the best studied example of mutualistic M&#xfc;llerian mimicry, thought &#x2013; but rarely demonstrated &#x2013; to promote coevolution. Some of the strongest available evidence for coevolution comes from phylogenetic codivergence, the parallel divergence of ecologically associated lineages. Early evolutionary reconstructions suggested codivergence between mimetic populations of _H. erato_ and _H. melpomene_, and this was initially hailed as the most striking known case of coevolution. However, subsequent molecular phylogenetic analyses found discrepancies in phylogenetic branching patterns and timing (topological and temporal incongruence) that argued against codivergence. We present the first explicit cophylogenetic test of codivergence between mimetic populations of _H. erato_ and _H. melpomene_, and re-examine the timing of these radiations. We find statistically significant topological congruence between multilocus coalescent population phylogenies of _H. erato_ and _H. melpomene_, supporting repeated codivergence of mimetic populations. Divergence time estimates, based on a Bayesian coalescent model, suggest that the evolutionary radiations of _H. erato_ and _H. melpomene_ occurred over the same time period, and are compatible with a series of temporally congruent codivergence events. This evidence supports a history of reciprocal coevolution between M&#xfc;llerian co-mimics characterised by phylogenetic codivergence and parallel phenotypic change