scholarly journals Substructured population growth in the Ashkenazi Jews inferred with Approximate Bayesian Computation

2018 ◽  
Author(s):  
Ariella L. Gladstein ◽  
Michael F. Hammer
Keyword(s):  
Population Growth ◽  
Central Europe ◽  
Population Differentiation ◽  
Approximate Bayesian Computation ◽  
Middle Eastern ◽  
Snp Array ◽  
Bayesian Computation ◽  
Ashkenazi Jews ◽  
Effective Population ◽  
Approximate Bayesian

The Ashkenazi Jews (AJ) are a population isolate that have resided in Central Europe since at least the 10th century and share ancestry with both European and Middle Eastern populations. Between the 11th and 16th centuries, AJ expanded eastward leading to two culturally distinct communities, one in central Europe and one in eastern Europe. Our aim was to determine if there are genetically distinct AJ subpopulations that reflect the cultural groups, and if so, what demographic events contributed to the population differentiation. We used Approximate Bayesian Computation (ABC) to choose among models of AJ history and infer demographic parameter values, including divergence times, effective population size, and gene flow. For the ABC analysis we used allele frequency spectrum and identical by descent based statistics to capture information on a wide timescale. We also mitigated the effects of ascertainment bias when performing ABC on SNP array data by jointly modeling and inferring the SNP discovery. We found that the most likely model was population differentiation between the Eastern and Western AJ ~400 years ago. The differentiation between the Eastern and Western AJ could be attributed to more extreme population growth in the Eastern AJ (0.25 per generation) than the Western AJ (0.069 per generation).

scholarly journals Substructured Population Growth in the Ashkenazi Jews Inferred with Approximate Bayesian Computation

2019 ◽  
Vol 36 (6) ◽  
pp. 1162-1171 ◽  
Author(s):  
Ariella L Gladstein ◽  
Michael F Hammer
Keyword(s):  
Population Growth ◽  
Population Differentiation ◽  
Approximate Bayesian Computation ◽  
Middle Eastern ◽  
Snp Array ◽  
Bayesian Computation ◽  
Ashkenazi Jews ◽  
Effective Population ◽  
Abc Analysis ◽  
Approximate Bayesian

Abstract The Ashkenazi Jews (AJ) are a population isolate sharing ancestry with both European and Middle Eastern populations that has likely resided in Central Europe since at least the tenth century. Between the 11th and 16th centuries, the AJ population expanded eastward leading to two culturally distinct communities in Western/Central and Eastern Europe. Our aim was to determine whether the western and eastern groups are genetically distinct, and if so, what demographic processes contributed to population differentiation. We used Approximate Bayesian Computation to choose among models of AJ history and to infer demographic parameter values, including divergence times, effective population sizes, and levels of gene flow. For the ABC analysis, we used allele frequency spectrum and identical by descent-based statistics to capture information on a wide timescale. We also mitigated the effects of ascertainment bias when performing ABC on SNP array data by jointly modeling and inferring SNP discovery. We found that the most likely model was population differentiation between Eastern and Western AJ ∼400 years ago. The differentiation between the Eastern and Western AJ could be attributed to more extreme population growth in the Eastern AJ (0.250 per generation) than the Western AJ (0.069 per generation).

scholarly journals Modeling SNP array ascertainment with Approximate Bayesian Computation for demographic inference

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Consuelo D. Quinto-Cortés ◽  
August E. Woerner ◽  
Joseph C. Watkins ◽  
Michael F. Hammer
Keyword(s):  
Approximate Bayesian Computation ◽  
Snp Array ◽  
Bayesian Computation ◽  
Demographic Inference ◽  
Approximate Bayesian

scholarly journals Demographic inference through approximate-Bayesian-computation skyline plots

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3530 ◽  
Author(s):  
Miguel Navascués ◽  
Raphaël Leblois ◽  
Concetta Burgarella
Keyword(s):  
Approximate Bayesian Computation ◽  
Graphical Representation ◽  
A Priori ◽  
Natural Populations ◽  
Composite Likelihood ◽  
Bayesian Computation ◽  
Mathematical Function ◽  
Effective Population ◽  
Population Sizes ◽  
Approximate Bayesian

The skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

scholarly journals Inferring population size history from large samples of genome wide molecular data - an approximate Bayesian computation approach

2016 ◽  
Author(s):  
Simon Boitard ◽  
Willy Rodriguez ◽  
Flora Jay ◽  
Stefano Mona ◽  
Frederic Austeritz
Keyword(s):  
Population Genetics ◽  
Population Size ◽  
Effective Population Size ◽  
Approximate Bayesian Computation ◽  
Recent Common Ancestor ◽  
Bayesian Computation ◽  
Effective Population ◽  
Wide Range ◽  
Complete Genomes ◽  
Approximate Bayesian

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.

scholarly journals Pleistocene expansion, anthropogenic pressure and ocean currents: Disentangling the past and ongoing evolutionary history of Patella aspera Röding, 1798 in the archipelago of Madeira

2021 ◽  
Author(s):  
Ricardo Sousa ◽  
Joana Vasconcelo ◽  
Iván Vera ◽  
Ana Rita ◽  
Stephen Hawkins ◽  
...  
Keyword(s):  
Sea Level ◽  
Population Differentiation ◽  
Approximate Bayesian Computation ◽  
Glacial Maximum ◽  
Ocean Currents ◽  
Reproductive Phenology ◽  
Bayesian Computation ◽  
Anthropogenic Pressure ◽  
Spatio Temporal ◽  
Approximate Bayesian

Rising sea-level following the Last Glacial Maximum lead to fragmentation of coastal limpet populations between islands of the Archipelago of Madeira. This fragmentation is reinforced by recent heavy exploitation reducing effective population size on Madeira Island. We use the limpet P. aspera to understand how the role of processes at different time scales (i.e. changes in the sea level and overexploitation) can influence the genetic composition of an extant species, relating these processes to reproductive phenology and seasonal shifts in ocean currents. Twelve microsatellite genetic markers were used. A power analysis was used to evaluate the power of the microsatellite markers to detect a signal of population differentiation. Long-term past migrations were assessed using a Bayesian Markov Montecarlo approach in the software MIGRATE-n to estimate mutation-scaled migration rates (M = m/μ; m, probability of a lineage immigrating per generation; μ, mutation rate). Two scenarios were evaluated using an Approximate Bayesian Computation (ABC) in the software DIYABC 2.1 (i) Scenario 1: considered a population scenario from a reduced Ne at time t3 to a higher Ne at time t2; and (ii) Scenario 2 considering a reduction of Ne from a time t3 to a time t2. Colonization of the archipelago by Portuguese settlers six centuries ago probably led to an important decrease in the genetic diversity of the species (Ne). Contemporary gene flow strongly support a pattern of high asymmetric connectivity explained by the reproductive phenology of the species and spatio-temporal seasonal changes in the ocean currents. Spatio-temporal reconstructions using Bayesian methods, including coalescent and Approximate Bayesian Computation (ABC) approaches, suggest changes in the migration patterns from highly symmetric to highly asymmetric connectivity with subtle population differentiation as consequence of post-glacial maximum sea level rise during the Holocene.

scholarly journals Demographic inference through approximate-Bayesian-computation skyline plots

2017 ◽  
Author(s):  
Miguel Navascués ◽  
Raphaël Leblois ◽  
Concetta Burgarella
Keyword(s):  
Approximate Bayesian Computation ◽  
Graphical Representation ◽  
Natural Populations ◽  
Composite Likelihood ◽  
Bayesian Computation ◽  
Mathematical Function ◽  
Effective Population ◽  
Population Sizes ◽  
Demographic Inference ◽  
Approximate Bayesian

AbstractThe skyline plot is a graphical representation of historical effective population sizes as a function of time. Past population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. Because of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. Currently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. Here, we provide a way to estimate historical effective population sizes using an Approximate Bayesian Computation (ABC) framework. We assess its performance using simulated and actual microsatellite datasets. Our method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. Because of the flexibility of ABC, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

Sign in / Sign up

Export Citation Format

Share Document