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The Limits to Estimating Population-Genetic Parameters with Temporal Data
The ability to obtain genome-wide sequences of very large numbers of individuals from natural populations raises questions about optimal sampling designs and the limits to extracting information on key population-genetic parameters from temporal-survey data. Methods are introduced for evaluating whe...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197491/ https://www.ncbi.nlm.nih.gov/pubmed/32181820 http://dx.doi.org/10.1093/gbe/evaa056 |
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author | Lynch, Michael Ho, Wei-Chin |
author_facet | Lynch, Michael Ho, Wei-Chin |
author_sort | Lynch, Michael |
collection | PubMed |
description | The ability to obtain genome-wide sequences of very large numbers of individuals from natural populations raises questions about optimal sampling designs and the limits to extracting information on key population-genetic parameters from temporal-survey data. Methods are introduced for evaluating whether observed temporal fluctuations in allele frequencies are consistent with the hypothesis of random genetic drift, and expressions for the expected sampling variances for the relevant statistics are given in terms of sample sizes and numbers. Estimation methods and aspects of statistical reliability are also presented for the mean and temporal variance of selection coefficients. For nucleotide sites that pass the test of neutrality, the current effective population size can be estimated by a method of moments, and expressions for its sampling variance provide insight into the degree to which such methodology can yield meaningful results under alternative sampling schemes. Finally, some caveats are raised regarding the use of the temporal covariance of allele-frequency change to infer selection. Taken together, these results provide a statistical view of the limits to population-genetic inference in even the simplest case of a closed population. |
format | Online Article Text |
id | pubmed-7197491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71974912020-05-07 The Limits to Estimating Population-Genetic Parameters with Temporal Data Lynch, Michael Ho, Wei-Chin Genome Biol Evol Research Article The ability to obtain genome-wide sequences of very large numbers of individuals from natural populations raises questions about optimal sampling designs and the limits to extracting information on key population-genetic parameters from temporal-survey data. Methods are introduced for evaluating whether observed temporal fluctuations in allele frequencies are consistent with the hypothesis of random genetic drift, and expressions for the expected sampling variances for the relevant statistics are given in terms of sample sizes and numbers. Estimation methods and aspects of statistical reliability are also presented for the mean and temporal variance of selection coefficients. For nucleotide sites that pass the test of neutrality, the current effective population size can be estimated by a method of moments, and expressions for its sampling variance provide insight into the degree to which such methodology can yield meaningful results under alternative sampling schemes. Finally, some caveats are raised regarding the use of the temporal covariance of allele-frequency change to infer selection. Taken together, these results provide a statistical view of the limits to population-genetic inference in even the simplest case of a closed population. Oxford University Press 2020-03-29 /pmc/articles/PMC7197491/ /pubmed/32181820 http://dx.doi.org/10.1093/gbe/evaa056 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lynch, Michael Ho, Wei-Chin The Limits to Estimating Population-Genetic Parameters with Temporal Data |
title | The Limits to Estimating Population-Genetic Parameters with Temporal Data |
title_full | The Limits to Estimating Population-Genetic Parameters with Temporal Data |
title_fullStr | The Limits to Estimating Population-Genetic Parameters with Temporal Data |
title_full_unstemmed | The Limits to Estimating Population-Genetic Parameters with Temporal Data |
title_short | The Limits to Estimating Population-Genetic Parameters with Temporal Data |
title_sort | limits to estimating population-genetic parameters with temporal data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197491/ https://www.ncbi.nlm.nih.gov/pubmed/32181820 http://dx.doi.org/10.1093/gbe/evaa056 |
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