scholarly journals Comparative Evaluation of a New Effective Population Size Estimator Based on Approximate Bayesian Computation

Genetics ◽  
2004 ◽  
Vol 167 (2) ◽  
pp. 977-988 ◽  
Author(s):  
David A. Tallmon ◽  
Gordon Luikart ◽  
Mark A. Beaumont
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Comparative Evaluation ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Effective Population ◽  
Approximate Bayesian

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 Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation

2020 ◽  
Author(s):  
Théophile Sanchez ◽  
Jean Cury ◽  
Guillaume Charpiat ◽  
Flora Jay
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Population Size ◽  
Effective Population Size ◽  
Approximate Bayesian Computation ◽  
Summary Statistics ◽  
Effective Population ◽  
The Past ◽  
Approximate Bayesian

AbstractFor the past decades, simulation-based likelihood-free inference methods have enabled researchers to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics). Here we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history. First, we provide guidelines to construct artificial neural networks that comply with the intrinsic properties of SNP data such as invariance to permutation of haplotypes, long scale interactions between SNPs and variable genomic length. Thanks to a Bayesian hyperparameter optimization procedure, we evaluate the performance of multiple networks and compare them to well established methods like Approximate Bayesian Computation (ABC). Even without the expert knowledge of summary statistics, our approach compares fairly well to an ABC based on handcrafted features. Furthermore we show that combining deep learning and ABC can improve performance while taking advantage of both frameworks. Finally, we apply our approach to reconstruct the effective population size history of cattle breed populations.

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 Inferring Population Size History from Large Samples of Genome-Wide Molecular Data - An Approximate Bayesian Computation Approach

PLoS Genetics ◽  
2016 ◽  
Vol 12 (3) ◽  
pp. e1005877 ◽  
Author(s):  
Simon Boitard ◽  
Willy Rodríguez ◽  
Flora Jay ◽  
Stefano Mona ◽  
Frédéric Austerlitz
Keyword(s):  
Population Size ◽  
Approximate Bayesian Computation ◽  
Molecular Data ◽  
Bayesian Computation ◽  
Large Samples ◽  
Genome Wide ◽  
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 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 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.

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