@misc{9754,
abstract = {Short-read sequencing technologies have in principle made it feasible to draw detailed inferences about the recent history of any organism. In practice, however, this remains challenging due to the difficulty of genome assembly in most organisms and the lack of statistical methods powerful enough to discriminate among recent, non-equilibrium histories. We address both the assembly and inference challenges. We develop a bioinformatic pipeline for generating outgroup-rooted alignments of orthologous sequence blocks from de novo low-coverage short-read data for a small number of genomes, and show how such sequence blocks can be used to fit explicit models of population divergence and admixture in a likelihood framework. To illustrate our approach, we reconstruct the Pleistocene history of an oak-feeding insect (the oak gallwasp Biorhiza pallida) which, in common with many other taxa, was restricted during Pleistocene ice ages to a longitudinal series of southern refugia spanning theWestern Palaearctic. Our analysis of sequence blocks sampled from a single genome from each of three major glacial refugia reveals support for an unexpected history dominated by recent admixture. Despite the fact that 80% of the genome is affected by admixture during the last glacial cycle, we are able to infer the deeper divergence history of these populations. These inferences are robust to variation in block length, mutation model, and the sampling location of individual genomes within refugia. This combination of de novo assembly and numerical likelihood calculation provides a powerful framework for estimating recent population history that can be applied to any organism without the need for prior genetic resources.},
author = {Hearn, Jack and Stone, Graham and Barton, Nicholas H and Lohse, Konrad and Bunnefeld, Lynsey},
publisher = {Dryad},
title = {{Data from: Likelihood-based inference of population history from low coverage de novo genome assemblies}},
doi = {10.5061/dryad.r3r60},
year = {2013},
}
@article{2944,
abstract = {We propose a two-step procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the trade-off between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABC-type model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.},
author = {Aeschbacher, Simon and Futschik, Andreas and Beaumont, Mark},
journal = {Molecular Ecology},
number = {4},
pages = {987 -- 1002},
publisher = {Wiley-Blackwell},
title = {{Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates. }},
doi = {10.1111/mec.12165},
volume = {22},
year = {2013},
}
@article{2917,
abstract = {The search for extra-terrestrial intelligence (SETI) has been performed principally as a one-way survey, listening of radio frequencies across the Milky Way and other galaxies. However, scientists have engaged in an active messaging only rarely. This suggests the simple rationale that if other civilizations exist and take a similar approach to ours, namely listening but not broadcasting, the result is a silent universe. A simple game theoretical model, the prisoner's dilemma, explains this situation: each player (civilization) can passively search (defect), or actively search and broadcast (cooperate). In order to maximize the payoff (or, equivalently, minimize the risks) the best strategy is not to broadcast. In fact, the active search has been opposed on the basis that it might be dangerous to expose ourselves. However, most of these ideas have not been based on objective arguments, and ignore accounting of the possible gains and losses. Thus, the question stands: should we perform an active search? I develop a game-theoretical framework where civilizations can be of different types, and explicitly apply it to a situation where societies are either interested in establishing a two-way communication or belligerent and in urge to exploit ours. The framework gives a quantitative solution (a mixed-strategy), which is how frequent we should perform the active SETI. This frequency is roughly proportional to the inverse of the risk, and can be extremely small. However, given the immense amount of stars being scanned, it supports active SETI. The model is compared with simulations, and the possible actions are evaluated through the San Marino scale, measuring the risks of messaging.},
author = {Vladar, Harold},
journal = {International Journal of Astrobiology},
number = {1},
pages = {53 -- 62},
publisher = {Cambridge University Press},
title = {{The game of active search for extra terrestrial intelligence Breaking the Great Silence }},
doi = {10.1017/S1473550412000407},
volume = {12},
year = {2012},
}
@article{2962,
abstract = {The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine learning literature. We consider different types of boosting and compare them to partial least squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the re-introduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θanc = 4 Ne u), and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L2-loss performs best. Applying that method to the ibex data, we estimate θanc ≈ 1.288, and find that most of the variation across loci of the ancestral mutation rate u is between 7.7×10−4 and 3.5×10−3 per locus per generation. The proportion of males with access to matings is estimated to ω ≈ 0.21, which is in good agreement with recent independent estimates.},
author = {Aeschbacher, Simon and Beaumont, Mark and Futschik, Andreas},
journal = {Genetics},
number = {3},
pages = {1027 -- 1047},
publisher = {Genetics Society of America},
title = {{A novel approach for choosing summary statistics in approximate Bayesian computation}},
doi = {10.1534/genetics.112.143164},
volume = {192},
year = {2012},
}
@article{2968,
abstract = {Little is known about the stability of trophic relationships in complex natural communities over evolutionary timescales. Here, we use sequence data from 18 nuclear loci to reconstruct and compare the intraspecific histories of major Pleistocene refugial populations in the Middle East, the Balkans and Iberia in a guild of four Chalcid parasitoids (Cecidostiba fungosa, Cecidostiba semifascia, Hobbya stenonota and Mesopolobus amaenus) all attacking Cynipid oak galls. We develop a likelihood method to numerically estimate models of divergence between three populations from multilocus data. We investigate the power of this framework on simulated data, and-using triplet alignments of intronic loci-quantify the support for all possible divergence relationships between refugial populations in the four parasitoids. Although an East to West order of population divergence has highest support in all but one species, we cannot rule out alternative population tree topologies. Comparing the estimated times of population splits between species, we find that one species, M. amaenus, has a significantly older history than the rest of the guild and must have arrived in central Europe at least one glacial cycle prior to other guild members. This suggests that although all four species may share a common origin in the East, they expanded westwards into Europe at different times. © 2012 Blackwell Publishing Ltd.},
author = {Lohse, Konrad and Barton, Nicholas H and Melika, George and Stone, Graham},
journal = {Molecular Ecology},
number = {18},
pages = {4605 -- 4617},
publisher = {Wiley-Blackwell},
title = {{A likelihood based comparison of population histories in a parasitoid guild}},
doi = {10.1111/j.1365-294X.2012.05700.x},
volume = {21},
year = {2012},
}