Estimating Ancestral Population Sizes and Divergence Times

Genetics ◽  
2003 ◽  
Vol 163 (1) ◽  
pp. 395-404 ◽  
Author(s):  
Jeffrey D Wall
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Ancestral Population ◽  
Divergence Times ◽  
Intragenic Recombination ◽  
Intergenic Sequence ◽  
Effective Population ◽  
Recombination Rates ◽  
Population Sizes

Abstract This article presents a new method for jointly estimating species divergence times and ancestral population sizes. The method improves on previous ones by explicitly incorporating intragenic recombination, by utilizing orthologous sequence data from closely related species, and by using a maximum-likelihood framework. The latter allows for efficient use of the available information and provides a way of assessing how much confidence we should place in the estimates. I apply the method to recently collected intergenic sequence data from humans and the great apes. The results suggest that the human-chimpanzee ancestral population size was four to seven times larger than the current human effective population size and that the current human effective population size is slightly >10,000. These estimates are similar to previous ones, and they appear relatively insensitive to assumptions about the recombination rates or mutation rates across loci.

scholarly journals On the estimation of ancestral population sizes of modern humans

1997 ◽  
Vol 69 (2) ◽  
pp. 111-116 ◽  
Author(s):  
ZIHENG YANG
Keyword(s):  
Population Size ◽  
Sequence Data ◽  
Divergence Time ◽  
Ancestral Population ◽  
Rate Variation ◽  
Effective Population ◽  
Modern Humans ◽  
Extant Species ◽  
Population Sizes ◽  
Highly Correlated

The theory developed by Takahata and colleagues for estimating the effective population size of ancestral species using homologous sequences from closely related extant species was extended to take account of variation of evolutionary rates among loci. Nuclear sequence data related to the evolution of modern humans were reanalysed and computer simulations were performed to examine the effect of rate variation on estimation of ancestral population sizes. It is found that the among-locus rate variation does not have a significant effect on estimation of the current population size when sequences from multiple loci are sampled from the same species, but does have a significant effect on estimation of the ancestral population size using sequences from different species. The effects of ancestral population size, species divergence time and among-locus rate variation are found to be highly correlated, and to achieve reliable estimates of the ancestral population size, effects of the other two factors should be estimated independently.

40 Factors Influencing Accuracy of Genomic Selection with Sequence Information

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 20-21
Author(s):  
Ignacy Misztal ◽  
Ivan Pocrnic ◽  
Daniela Lourenco
Keyword(s):  
Population Size ◽  
Genomic Selection ◽  
Effective Population Size ◽  
Sequence Data ◽  
Single Step ◽  
Substitution Effects ◽  
Sequence Information ◽  
Effective Population ◽  
P Values ◽  
Population Sizes

Abstract Incorporating the sequence information only marginally increases the accuracy of genomic selection. The purpose of this study was to find out why by examining profiles of Quantitative Trait Nucleotides (QTN). Multiple populations were simulated with different effective population sizes and number of animals. 100 equidistant QTN with identical substitution effects were included in 50k SNP genotypes. Analyses were by single-step GBLUP, with solutions converted to SNP values and subsequently to p-values for each SNP. Manhattan plots for standardized SNP solutions were noisy and were elevated only for few QTNs. Manhattan plots for p-values were similar to those for SNP solutions, indicating little impact of population structure. The number of significant QTN was lower with lower effective population size and increased with larger data; at most about 20% of QTNs were detected. A QTN profile was created by averaging SNP solutions ±100 SNP around each QTN. The profile showed a normal-like response but with a distinct peak for the QTN. While the peak was higher with more data and higher effective population size, the normal-like response was smaller with higher effective population size. QTNs explained little variance because of shrinkage. The accuracy of genomic selection would be 100% if all QTNs are identified and their variances known, to prevent shrinking or inflation. This study allows to see limits of application of QTN from sequence data for genomic selection. If all causative SNP are included in the data, only a fraction of them can be identified even under a very simplistic architecture. As variance of QTN are assumed constant or are crude approximations (like in BayesR), the estimated QTN effects are inaccurate. Additional complications in QTN detection are close-spaced QTN and false QTNs due to imputation. Small effective population size allows the genomic selection by GBLUP but complicates the use of QTNs.

scholarly journals Mitochondrial DNA Sequence Diversity in Mammals: A Correlation between the Effective and Census Population Sizes

2020 ◽  
Vol 12 (12) ◽  
pp. 2441-2449
Author(s):  
Jennifer James ◽  
Adam Eyre-Walker
Keyword(s):  
Mitochondrial Dna ◽  
Metabolic Rate ◽  
Population Size ◽  
Effective Population Size ◽  
Dna Sequence ◽  
Range Size ◽  
Effective Population ◽  
Population Sizes ◽  
Census Population Size ◽  
The Relationship

Abstract What determines the level of genetic diversity of a species remains one of the enduring problems of population genetics. Because neutral diversity depends upon the product of the effective population size and mutation rate, there is an expectation that diversity should be correlated to measures of census population size. This correlation is often observed for nuclear but not for mitochondrial DNA. Here, we revisit the question of whether mitochondrial DNA sequence diversity is correlated to census population size by compiling the largest data set to date, using 639 mammalian species. In a multiple regression, we find that nucleotide diversity is significantly correlated to both range size and mass-specific metabolic rate, but not a variety of other factors. We also find that a measure of the effective population size, the ratio of nonsynonymous to synonymous diversity, is also significantly negatively correlated to both range size and mass-specific metabolic rate. These results together suggest that species with larger ranges have larger effective population sizes. The slope of the relationship between diversity and range is such that doubling the range increases diversity by 12–20%, providing one of the first quantifications of the relationship between diversity and the census population size.

scholarly journals Coalescent models at small effective population sizes and population declines are positively misleading

2019 ◽  
Author(s):  
M. Elise Lauterbur
Keyword(s):  
Genetic Diversity ◽  
Sample Size ◽  
Population Size ◽  
Effective Population Size ◽  
Genetic Relationships ◽  
Population Decline ◽  
Population Declines ◽  
Effective Population ◽  
Analytical Approximations ◽  
Population Sizes

AbstractPopulation genetics employs two major models for conceptualizing genetic relationships among individuals – outcome-driven (coalescent) and process-driven (forward). These models are complementary, but the basic Kingman coalescent and its extensions make fundamental assumptions to allow analytical approximations: a constant effective population size much larger than the sample size. These make the probability of multiple coalescent events per generation negligible. Although these assumptions are often violated in species of conservation concern, conservation genetics often uses coalescent models of effective population sizes and trajectories in endangered species. Despite this, the effect of very small effective population sizes, and their interaction with bottlenecks and sample sizes, on such analyses of genetic diversity remains unexplored. Here, I use simulations to analyze the influence of small effective population size, population decline, and their relationship with sample size, on coalescent-based estimates of genetic diversity. Compared to forward process-based estimates, coalescent models significantly overestimate genetic diversity in oversampled populations with very small effective sizes. When sampled soon after a decline, coalescent models overestimate genetic diversity in small populations regardless of sample size. Such overestimates artificially inflate estimates of both bottleneck and population split times. For conservation applications with small effective population sizes, forward simulations that do not make population size assumptions are computationally tractable and should be considered instead of coalescent-based models. These findings underscore the importance of the theoretical basis of analytical techniques as applied to conservation questions.

scholarly journals Biodiversity Management Under Limiting Conditions: Estimating Effective Population Size Using the Molecular Mark and Recapture (MMR) Method

Sociobiology ◽  
2014 ◽  
Vol 59 (1) ◽  
pp. 165
Author(s):  
Kaori Murase ◽  
Masaharu Fukita
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
National Parks ◽  
Hot Spots ◽  
First Generation ◽  
Second Generation ◽  
Effective Population ◽  
Simple Method ◽  
Population Sizes ◽  
Virtual Species

Although many people have been paying attention to the decrease of biodiversity on earth in recent years, many local people, even staff of national parks, live under limiting conditions (such as a shortage of funds, specialists, literature, equipment for experiments and so on). To conserve biodiversity, it is important to be clear about which species decrease or increase. To find such information, it is quite important to know the dynamics of effective population size for each species. Although a large number of papers have been written about how to improve the precision of the estimated effective population size, little has been studied on how to estimate the dynamics of the effective population sizes for many species together under limiting situations, very similar to the management methods of national parks in countries which have biological hot spots. In this paper, we are not concerned with the improvement of the precision of the estimates. We do, however, propose a simple method for the estimation of the effective population size. We named it the “MMR method.” It is not difficult to understand and is easily applied to many species. To show the usefulness of the MMR method we made simple virtual species, which included the first generation and the second generation, on a computer, and then we conducted simulations to estimate the effective population size of the first generation. We calculated three statistics to estimate whether the MMR method is useful or not. The three statistics showed that the MMR method is useful.

scholarly journals Modeling the growth and decline of pathogen effective population size provides insight into epidemic dynamics and drivers of antimicrobial resistance

2017 ◽  
Author(s):  
Erik M. Volz ◽  
Xavier Didelot
Keyword(s):  
Growth Rate ◽  
Population Size ◽  
Effective Population Size ◽  
Growth Rates ◽  
Sequence Data ◽  
Demographic History ◽  
Parametric Models ◽  
Effective Population ◽  
Epidemic Dynamics ◽  
Non Parametric

AbstractNon-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stationary stochastic processes which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of β-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://mrc-ide.github.io/skygrowth/.

scholarly journals A NEW METHOD FOR ESTIMATING THE EFFECTIVE POPULATION SIZE FROM ALLELE FREQUENCY CHANGES

Genetics ◽  
1983 ◽  
Vol 104 (3) ◽  
pp. 531-548
Author(s):  
Edward Pollak
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Allele Frequency ◽  
New Method ◽  
Standard Errors ◽  
Effective Population ◽  
Census Size ◽  
Population Sizes ◽  
Frequency Changes ◽  
Approximate Expressions

ABSTRACT A new procedure is proposed for estimating the effective population size, given that information is available on changes in frequencies of the alleles at one or more independently segregating loci and the population is observed at two or more separate times. Approximate expressions are obtained for the variances of the new statistic, as well as others, also based on allele frequency changes, that have been discussed in the literature. This analysis indicates that the new statistic will generally have a smaller variance than the others. Estimates of effective population sizes and of the standard errors of the estimates are computed for data on two fly populations that have been discussed in earlier papers. In both cases, there is evidence that the effective population size is very much smaller than the minimum census size of the population.

scholarly journals Inferring pandemic growth rates from sequence data

2012 ◽  
Vol 9 (73) ◽  
pp. 1797-1808 ◽  
Author(s):  
Eric de Silva ◽  
Neil M. Ferguson ◽  
Christophe Fraser
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Sequence Data ◽  
Simulated Data ◽  
Parametric Methods ◽  
Sampling Strategies ◽  
Size Estimation ◽  
Effective Population ◽  
Population Size Estimates ◽  
Non Parametric

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

scholarly journals Mitochondrial DNA Sequence Diversity in Mammals: a Correlation Between the Effective and Census Population Sizes

2020 ◽  
Author(s):  
Jennifer James ◽  
Adam Eyre-Walker
Keyword(s):  
Mitochondrial Dna ◽  
Metabolic Rate ◽  
Population Size ◽  
Effective Population Size ◽  
Dna Sequence ◽  
Sequence Diversity ◽  
Effective Population ◽  
Population Sizes ◽  
Census Population Size ◽  
The Relationship

AbstractWhat determines the level of genetic diversity of a species remains one of the enduring problems of population genetics. Since, neutral diversity depends upon the product of the effective population size and mutation rate there is an expectation that diversity should be correlated to measures of census population size. This correlation is often observed for nuclear but not for mitochondrial DNA. Here we revisit the question of whether mitochondrial DNA sequence diversity is correlated to census population size by compiling the largest dataset to date from 639 mammalian species. In a multiple regression we find that nucleotide diversity is significantly correlated to both range size and mass-specific metabolic rate, but not a variety of other factors. We also find that a measure of the effective population size, the ratio of non-synonymous to synonymous diversity, is also significantly negatively correlated to both range and mass-specific metabolic rate. These results together suggest that species with larger ranges have larger effective population sizes. The slope of the relationship between diversity and range is such that doubling the range increases diversity by 12 to 20%, providing one of the first quantifications of the relationship between effective and census population sizes.

scholarly journals Evaluating the use of ABBA-BABA statistics to locate introgressed loci

2013 ◽  
Author(s):  
Simon H. Martin ◽  
John W. Davey ◽  
Chris D. Jiggins
Keyword(s):  
Population Structure ◽  
Population Size ◽  
Effective Population Size ◽  
Ancestral Population ◽  
Whole Genome ◽  
Effective Population ◽  
Genome Data ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Several methods have been proposed to test for introgression across genomes. One method tests for a genome-wide excess of shared derived alleles between taxa using Patterson?s D statistic, but does not establish which loci show such an excess or whether the excess is due to introgression or ancestral population structure. Several recent studies have extended the use of D by applying the statistic to small genomic regions, rather than genome-wide. Here, we use simulations and whole genome data from Heliconius butterflies to investigate the behavior of D in small genomic regions. We find that D is unreliable in this situation as it gives inflated values when effective population size is low, causing D outliers to cluster in genomic regions of reduced diversity. As an alternative, we propose a related statistic f̂d, a modified version of a statistic originally developed to estimate the genome-wide fraction of admixture. f̂d is not subject to the same biases as D, and is better at identifying introgressed loci. Finally, we show that both D and f̂d outliers tend to cluster in regions of low absolute divergence (dXY), which can confound a recently proposed test for differentiating introgression from shared ancestral variation at individual loci.

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