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Robin S. Waples - One of the best experts on this subject based on the ideXlab platform.
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relative precision of the sibship and ld methods for estimating Effective Population Size with genomics scale datasets
bioRxiv, 2021Co-Authors: Robin S. WaplesAbstract:Computer simulations were used to compare relative precision of two widely-used single-sample methods for estimating Effective Population Size (Ne)--the sibship method and the linkage-disequilibrium (LD) method. Emphasis is on performance when thousands of gene loci are used, which now can easily be achieved even for non-model species. Results show that unless Ne is very small, if at least 500-2000 diallelic loci are used, precision of the LD method is higher than the maximum possible precision for the sibship method, which occurs when all sibling relationships have been correctly identified. Results also show that when precision is high for both methods, their estimates of Effective Population Size are high and positively correlated, which limits additional gains in precision that might be obtained by combining information from the two estimators
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simple life history traits explain key Effective Population Size ratios across diverse taxa
Proceedings of The Royal Society B: Biological Sciences, 2013Co-Authors: Robin S. Waples, Gordon Luikart, James R Faulkner, David A TallmonAbstract:Effective Population Size (Ne) controls both the rate of random genetic drift and the Effectiveness of selection and migration, but it is difficult to estimate in nature. In particular, for species...
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Estimation of Effective Population Size in continuously distributed Populations: there goes the neighborhood
Heredity, 2013Co-Authors: Maile C. Neel, Kevin S. Mckelvey, Nils Ryman, Michael W. Lloyd, R. A. Short Bull, Fred W. Allendorf, Michael K. Schwartz, Robin S. WaplesAbstract:Use of genetic methods to estimate Effective Population Size (Ne) is rapidly increasing, but all approaches make simplifying assumptions unlikely to be met in real Populations. In particular, all assume a single, unstructured Population, and none has been evaluated for use with continuously distributed species. We simulated continuous Populations with local mating structure, as envisioned by Wright's concept of neighborhood Size (NS), and evaluated performance of a single-sample estimator based on linkage disequilibrium (LD), which provides an estimate of the Effective number of parents that produced the sample (Nb). Results illustrate the interacting effects of two phenomena, drift and mixture, that contribute to LD. Samples from areas equal to or smaller than a breeding window produced estimates close to the NS. As the sampling window increased in Size to encompass multiple genetic neighborhoods, mixture LD from a two-locus Wahlund effect overwhelmed the reduction in drift LD from incorporating offspring from more parents. As a consequence, never approached the global Ne, even when the geographic scale of sampling was large. Results indicate that caution is needed in applying standard methods for estimating Effective Size to continuously distributed Populations.
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accounting for missing data in the estimation of contemporary genetic Effective Population Size ne
Molecular Ecology Resources, 2013Co-Authors: David Peel, Robin S. Waples, G M Macbeth, C Do, J R OvendenAbstract:Theoretical models are often applied to Population genetic data sets without fully considering the effect of missing data. Researchers can deal with missing data by removing individuals that have failed to yield genotypes and/or by removing loci that have failed to yield allelic determinations, but despite their best efforts, most data sets still contain some missing data. As a consequence, realized sample Size differs among loci, and this poses a problem for unbiased methods that must explicitly account for random sampling error. One commonly used solution for the calculation of contemporary Effective Population Size (Ne) is to calculate the Effective sample Size as an unweighted mean or harmonic mean across loci. This is not ideal because it fails to account for the fact that loci with different numbers of alleles have different information content. Here we consider this problem for genetic estimators of contemporary Effective Population Size (Ne). To evaluate bias and precision of several statistical approaches for dealing with missing data, we simulated Populations with known Ne and various degrees of missing data. Across all scenarios, one method of correcting for missing data (fixed-inverse variance-weighted harmonic mean) consistently performed the best for both single-sample and two-sample (temporal) methods of estimating Ne and outperformed some methods currently in widespread use. The approach adopted here may be a starting point to adjust other Population genetics methods that include per-locus sample Size components.
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accounting for missing data in the estimation of contemporary genetic Effective Population Size n e
Molecular Ecology Resources, 2013Co-Authors: David Peel, Robin S. Waples, G M Macbeth, J R OvendenAbstract:Theoretical models are often applied to Population genetic data sets without fully considering the effect of missing data. Researchers can deal with missing data by removing individuals that have failed to yield genotypes and/or by removing loci that have failed to yield allelic determinations, but despite their best efforts, most data sets still contain some missing data. As a consequence, realized sample Size differs among loci, and this poses a problem for unbiased methods that must explicitly account for random sampling error. One commonly used solution for the calculation of contemporary Effective Population Size (Ne) is to calculate the Effective sample Size as an unweighted mean or harmonic mean across loci. This is not ideal because it fails to account for the fact that loci with different numbers of alleles have different information content. Here we consider this problem for genetic estimators of contemporary Effective Population Size (Ne). To evaluate bias and precision of several statistical approaches for dealing with missing data, we simulated Populations with known Ne and various degrees of missing data. Across all scenarios, one method of correcting for missing data (fixed-inverse variance-weighted harmonic mean) consistently performed the best for both single-sample and two-sample (temporal) methods of estimating Ne and outperformed some methods currently in widespread use. The approach adopted here may be a starting point to adjust other Population genetics methods that include per-locus sample Size components.
J R Ovenden - One of the best experts on this subject based on the ideXlab platform.
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accounting for missing data in the estimation of contemporary genetic Effective Population Size ne
Molecular Ecology Resources, 2013Co-Authors: David Peel, Robin S. Waples, G M Macbeth, C Do, J R OvendenAbstract:Theoretical models are often applied to Population genetic data sets without fully considering the effect of missing data. Researchers can deal with missing data by removing individuals that have failed to yield genotypes and/or by removing loci that have failed to yield allelic determinations, but despite their best efforts, most data sets still contain some missing data. As a consequence, realized sample Size differs among loci, and this poses a problem for unbiased methods that must explicitly account for random sampling error. One commonly used solution for the calculation of contemporary Effective Population Size (Ne) is to calculate the Effective sample Size as an unweighted mean or harmonic mean across loci. This is not ideal because it fails to account for the fact that loci with different numbers of alleles have different information content. Here we consider this problem for genetic estimators of contemporary Effective Population Size (Ne). To evaluate bias and precision of several statistical approaches for dealing with missing data, we simulated Populations with known Ne and various degrees of missing data. Across all scenarios, one method of correcting for missing data (fixed-inverse variance-weighted harmonic mean) consistently performed the best for both single-sample and two-sample (temporal) methods of estimating Ne and outperformed some methods currently in widespread use. The approach adopted here may be a starting point to adjust other Population genetics methods that include per-locus sample Size components.
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accounting for missing data in the estimation of contemporary genetic Effective Population Size n e
Molecular Ecology Resources, 2013Co-Authors: David Peel, Robin S. Waples, G M Macbeth, J R OvendenAbstract:Theoretical models are often applied to Population genetic data sets without fully considering the effect of missing data. Researchers can deal with missing data by removing individuals that have failed to yield genotypes and/or by removing loci that have failed to yield allelic determinations, but despite their best efforts, most data sets still contain some missing data. As a consequence, realized sample Size differs among loci, and this poses a problem for unbiased methods that must explicitly account for random sampling error. One commonly used solution for the calculation of contemporary Effective Population Size (Ne) is to calculate the Effective sample Size as an unweighted mean or harmonic mean across loci. This is not ideal because it fails to account for the fact that loci with different numbers of alleles have different information content. Here we consider this problem for genetic estimators of contemporary Effective Population Size (Ne). To evaluate bias and precision of several statistical approaches for dealing with missing data, we simulated Populations with known Ne and various degrees of missing data. Across all scenarios, one method of correcting for missing data (fixed-inverse variance-weighted harmonic mean) consistently performed the best for both single-sample and two-sample (temporal) methods of estimating Ne and outperformed some methods currently in widespread use. The approach adopted here may be a starting point to adjust other Population genetics methods that include per-locus sample Size components.
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.
Rasmus Nielsen - One of the best experts on this subject based on the ideXlab platform.
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joint estimation of pedigrees and Effective Population Size using markov chain monte carlo
Genetics, 2019Co-Authors: Rasmus NielsenAbstract:Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term Effective Population Size [Formula: see text], which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and [Formula: see text] from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and [Formula: see text] with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.
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joint estimation of pedigrees and Effective Population Size using markov chain monte carlo
bioRxiv, 2018Co-Authors: Rasmus NielsenAbstract:ABSTRACT Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term Effective Population Size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals with the use of a composite likelihood, which significantly increases computational efficiency. We show on simulated data that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.
Gordon Luikart - One of the best experts on this subject based on the ideXlab platform.
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simple life history traits explain key Effective Population Size ratios across diverse taxa
Proceedings of The Royal Society B: Biological Sciences, 2013Co-Authors: Robin S. Waples, Gordon Luikart, James R Faulkner, David A TallmonAbstract:Effective Population Size (Ne) controls both the rate of random genetic drift and the Effectiveness of selection and migration, but it is difficult to estimate in nature. In particular, for species...
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onesamp a program to estimate Effective Population Size using approximate bayesian computation
Molecular Ecology Resources, 2008Co-Authors: David A Tallmon, Gordon Luikart, Ally Koyuk, Mark A BeaumontAbstract:The estimation of Effective Population Size from one sample of genotypes has been problematic because most estimators have been proven imprecise or biased. We developed a web-based program, onesamp that uses approximate Bayesian computation to estimate Effective Population Size from a sample of microsatellite genotypes. onesamp requires an input file of sampled individuals' microsatellite genotypes along with information about several sampling and biological parameters. onesamp provides an estimate of Effective Population Size, along with 95% credible limits. We illustrate the use of onesamp with an example data set from a re-introduced Population of ibex Capra ibex.
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computer programs onesamp a program to estimate Effective Population Size using approximate bayesian computation
Molecular Ecology Resources, 2008Co-Authors: David A Tallmon, Gordon Luikart, Ally Koyuk, Mark A BeaumontAbstract:The estimation of Effective Population Size from one sample of genotypes has been problematic because most estimators have been proven imprecise or biased. We developed a web-based program, onesamp that uses approximate Bayesian computation to estimate Effective Population Size from a sample of microsatellite genotypes. onesamp requires an input file of sampled individuals’ microsatellite genotypes along with information about several sampling and biological parameters. onesamp provides an estimate of Effective Population Size, along with 95% credible limits. We illustrate the use of onesamp with an example data set from a re-introduced Population of ibex Capra ibex.
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estimating Effective Population Size from linkage disequilibrium severe bias in small samples
Conservation Genetics, 2006Co-Authors: Phillip R England, Gordon Luikart, David A Tallmon, Jeanmarie Cornuet, Pierre BerthierAbstract:Effective Population Size (Ne) is a central concept in evolutionary biology and conservation genetics. It predicts rates of loss of neutral genetic variation, fixation of deleterious and favourable alleles, and the increase of inbreeding experienced by a Population. A method exists for the estimation of Ne from the observed linkage disequilibrium between unlinked loci in a Population sample. While an increasing number of studies have applied this method in natural and managed Populations, its reliability has not yet been evaluated. We developed a computer program to calculate this estimator of Ne using the most widely used linkage disequilibrium algorithm and used simulations to show that this estimator is strongly biased when the sample Size is small (<100) and below the true Ne. This is probably due to the linkage disequilibrium generated by the sampling process itself and the inadequate correction for this phenomenon in the method. Results suggest that Ne estimates derived using this method should be regarded with caution in many cases. To improve the method’s reliability and usefulness we propose a way to determine whether a given sample Size exceeds the Population Ne and can therefore be used for the computation of an unbiased estimate.
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review of dna based census and Effective Population Size estimators
Animal Conservation, 1998Co-Authors: Michael K. Schwartz, David A Tallmon, Gordon LuikartAbstract:The detection of reductions in Effective Population Size (Ne) or census Size (Nc) is essential for conservation. Recent developments allow wildlife researchers to obtain genetic material via non-invasive sampling techniques that may provide the large sample Sizes necessary for precise estimates of Ne and Nc. Population genetic theory provides several methods to estimate Ne from allele frequency data: including temporal change in allele frequencies, gametic disequilibrium and heterozygote excess methods. Modification of capture–mark–recapture methods for use with multi-locus genotype data provides new means for estimating Nc. The combination of new DNA sampling techniques, polymerase chain reaction-based DNA markers and analytical methods may provide unprecedented power to detect reductions in Ne and Nc of endangered Populations. However, these genetic methods are largely untested in the field. We review some relatively unexplored, but promising ways that multi-locus genetic data can be used to provide important genetic and demographic information and suggest avenues for further research in this area.
