The Experts below are selected from a list of 594474 Experts worldwide ranked by ideXlab platform
Mattias Jakobsson - One of the best experts on this subject based on the ideXlab platform.
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inferring past effective Population Size from distributions of coalescent times
Genetics, 2016Co-Authors: Lucie M Gattepaille, Mattias Jakobsson, Torsten GuntherAbstract:Inferring and understanding changes in effective Population Size over time is a major challenge for Population genetics. Here we investigate some theoretical properties of random-mating Populations with varying Size over time. In particular, we present an exact solution to compute the Population Size as a function of time, [Formula: see text], based on distributions of coalescent times of samples of any Size. This result reduces the problem of Population Size inference to a problem of estimating coalescent time distributions. To illustrate the analytic results, we design a heuristic method using a tree-inference algorithm and investigate simulated and empirical Population-genetic data. We investigate the effects of a range of conditions associated with empirical data, for instance number of loci, sample Size, mutation rate, and cryptic recombination. We show that our approach performs well with genomic data (≥ 10,000 loci) and that increasing the sample Size from 2 to 10 greatly improves the inference of [Formula: see text] whereas further increase in sample Size results in modest improvements, even under a scenario of exponential growth. We also investigate the impact of recombination and characterize the potential biases in inference of [Formula: see text] The approach can handle large sample Sizes and the computations are fast. We apply our method to human genomes from four Populations and reconstruct Population Size profiles that are coherent with previous finds, including the Out-of-Africa bottleneck. Additionally, we uncover a potential difference in Population Size between African and non-African Populations as early as 400 KYA. In summary, we provide an analytic relationship between distributions of coalescent times and [Formula: see text], which can be incorporated into powerful approaches for inferring past Population Sizes from Population-genomic data.
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inferring past effective Population Size from distributions of coalescent times
bioRxiv, 2015Co-Authors: Lucie M Gattepaille, Mattias JakobssonAbstract:Inferring and understanding changes in effective Population Size over time is a major challenge for Population genetics. Here we investigate some theoretical properties of random mating Populations with varying Size over time. In particular, we present an exact method to compute the Population Size as a function of time using the distributions of coalescent-times of samples of any Size. This result reduces the problem of Population Size inference to a problem of estimating coalescent-time distributions. Using tree inference algorithms and genetic data, we can investigate the effects of a range of conditions associated with real data, for instance finite number of loci, sample Size, mutation rate and presence of cryptic recombination. We show that our method requires at least a modest number of loci (10,000 or more) and that increasing the sample Size from 2 to 10 greatly improves the inference whereas further increase in sample Size only results in a modest improvement, even under as scenario of exponential growth. We also show that small amounts of recombination can lead to biased Population Size reconstruction when unaccounted for. The approach can handle large sample Sizes and the computations are fast. We apply our method on human genomes from 4 Populations and reconstruct Population Size profiles that are coherent with previous knowledge, including the Out-of-Africa bottleneck. Additionally, a potential difference in Population Size between African and non-African Populations as early as 400 thousand years ago is uncovered.
Julia A. Palacios - One of the best experts on this subject based on the ideXlab platform.
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bayesian nonparametric inference of Population Size changes from sequential genealogies
Genetics, 2015Co-Authors: Julia A. Palacios, John Wakeley, Sohini RamachandranAbstract:Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past Population Sizes from genomic data. Recent methods that model recombination require small sample Sizes, make constraining assumptions about Population Size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model that allows accurate inference of Population Sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including Population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum-likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method's credible intervals for Population Size as a function of time cover 90% of true values across multiple demographic scenarios, enabling formal hypothesis testing about Population Size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of Population Size history over the past 150,000 years.
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bayesian nonparametric inference of Population Size changes from sequential genealogies
bioRxiv, 2015Co-Authors: Julia A. Palacios, John Wakeley, Sohini RamachandranAbstract:Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past Population Sizes from genomic data. Accurate methods are available for data from a single locus or from independent loci. Recent methods that model recombination require small sample Sizes, make constraining assumptions about Population Size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model which allows accurate inference of Population Sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including Population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method's credible intervals for Population Size as a function of time cover 90 percent of true values across multiple demographic scenarios, enabling formal hypothesis testing about Population Size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of Population Size history over the past 150,000 years.
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gaussian process based bayesian nonparametric inference of Population Size trajectories from gene genealogies
Biometrics, 2013Co-Authors: Julia A. Palacios, Vladimir N. MininAbstract:Summary Changes in Population Size influence genetic diversity of the Population and, as a result, leave a signature of these changes in individual genomes in the Population. We are interested in the inverse problem of reconstructing past Population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the Population of interest. These genealogies serve as a glue between the Population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about Population Size dynamics. Viewing these coalescent times as a point process, estimating Population Size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of Population Size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating Population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method.
Mirjam Sepesy Maucec - One of the best experts on this subject based on the ideXlab platform.
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Population Size reduction for the differential evolution algorithm
Applied Intelligence, 2008Co-Authors: Janez Brest, Mirjam Sepesy MaucecAbstract:This paper studies the efficiency of a recently defined Population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed Population Size but a method for gradually reducing Population Size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies.
Sohini Ramachandran - One of the best experts on this subject based on the ideXlab platform.
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bayesian nonparametric inference of Population Size changes from sequential genealogies
Genetics, 2015Co-Authors: Julia A. Palacios, John Wakeley, Sohini RamachandranAbstract:Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past Population Sizes from genomic data. Recent methods that model recombination require small sample Sizes, make constraining assumptions about Population Size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model that allows accurate inference of Population Sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including Population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum-likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method's credible intervals for Population Size as a function of time cover 90% of true values across multiple demographic scenarios, enabling formal hypothesis testing about Population Size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of Population Size history over the past 150,000 years.
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bayesian nonparametric inference of Population Size changes from sequential genealogies
bioRxiv, 2015Co-Authors: Julia A. Palacios, John Wakeley, Sohini RamachandranAbstract:Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past Population Sizes from genomic data. Accurate methods are available for data from a single locus or from independent loci. Recent methods that model recombination require small sample Sizes, make constraining assumptions about Population Size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model which allows accurate inference of Population Sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including Population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method's credible intervals for Population Size as a function of time cover 90 percent of true values across multiple demographic scenarios, enabling formal hypothesis testing about Population Size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of Population Size history over the past 150,000 years.
Bernard Vaissière - One of the best experts on this subject based on the ideXlab platform.
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Rapid measurement of the adult worker Population Size in honey bees
Ecological Indicators, 2021Co-Authors: Stan Chabert, Fabrice Requier, Joël Chadoeuf, Laurent Guilbaud, Nicolas Morison, Bernard VaissièreAbstract:Changes in agricultural practices have lead to pollination deficits in entomophilous crops, leading to a growing interest in supplementing farmlands with managed colonies of honey bee, Apis mellifera. However, the metrics of a colony as a pollination unit is controversial due to the wide range of adult Population Sizes encountered in a colony, especially in relation with the time of year and beekeeping management. Correctly measuring the number of adult honey bees per hive is critical for farmers to adjust the number of colonies they need to meet crop pollination demand. We tested a simple non-invasive method to estimate the adult worker Population Size of colonies based on common beekeeping handlings. This method consisted in counting the number of inter-frames covered with adult bees (called IFB thereafter) from above the hive body. Based on the monitoring of 181 colonies, we investigated the nature of the relation between IFB and the adult bee Population Size and its contextdependence to the meterological conditions and hive type. We then evaluated the possible improvement of the method with additional IFB counted in the supers and from below the hive body. Finally, we analysed the robustness of the method by comparing estimates obtained from colonies observed by experimented and naive observers. We revealed a clear-cut logarithmic relation between the IFB and the adult Population Size, covering the effects of meteorological conditions and hive type. The counting of IFB from above the hive body were particularly sensitive to meteorological conditions, unlike those counted from below the hive body. Moreover, the counting of additional IFB from the supers slightly improved the estimates of adult Population Size. Interestingly, no difference of estimate was detected between experimented and naive observers, suggesting applied simplicity of the method. The IFB counting method thus provides a simple, non-invasive and robust indicator of the adult Population Size of a managed honey bee colony. The counting of IFB from below the hive body should be recommend due to the sensitivity to meteorological conditions of the counting of IFB from above the hive body. Beyond crop pollination, we also highlighted application perspectives of this method as an indicator of survival probability. This method can therefore be viewed as a standard for routine field monitoring to help farmers to estimate rigorously the number of colonies they need to meet the crop pollination demand and (ii) to help beekeepers assessing the mortality risk of their colonies.