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Home / Equine Research Bank / Genetics / Bayesian Inference of Natural Selection from Allele Frequenc...
Genetics2016; 203(1); 493-511; doi: 10.1534/genetics.116.187278

Bayesian Inference of Natural Selection from Allele Frequency Time Series.

Abstract: The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We developed a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in nonequilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.
Copyright © 2016 by the Genetics Society of America.
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Publication Date: 2016-03-23 PubMed ID: 27010022PubMed Central: PMC4858794DOI: 10.1534/genetics.116.187278Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • U.S. Gov't
  • Non-P.H.S.
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  • N.I.H.
  • Extramural

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The researchers have developed a Bayesian method that allows the use of allele frequency time series to infer the parameters of natural selection. This is enabled through the progress in ancient DNA technology. The research shows that this method effectively estimates selection coefficients and allele age. The researchers also concluded that disregarding relevant demographic history could significantly affect the results of inference.

Introduction

This research focuses on the use of ancient DNA technology to directly obtain the allele frequencies in ancestral populations. By doing so, the researchers aim to develop a new method in which allele frequency time series can be used to infer the parameters of natural selection. The proposed method is expected to offer more powerful results than traditional inferences made using patterns of linked neutral variation obtained from modern individuals.

Development of the Bayesian Method

  • The research team developed a Bayesian method that is able to use allele frequency time series data to infer the parameters of general diploid selection, in addition to allele age, in nonequilibrium populations.
  • A special feature of their approach is a novel path augmentation effort. This uses Markov chain Monte Carlo to integrate over the possible allele frequency trajectories that align with the observed data.

Performance of the Bayesian Method

  • Assessments through simulations reveal that this approach shows good potential in estimating selection coefficients and allele age.
  • The researchers have provided this method to the public via a C++ software package.

Importance of Considering Relevant Demographic History

  • A key insight from applying this tool to data on horse coat color is the realization that neglecting pertinent demographic history can considerably distort the results of inference.

Cite This Article

APA
Schraiber JG, Evans SN, Slatkin M. (2016). Bayesian Inference of Natural Selection from Allele Frequency Time Series. Genetics, 203(1), 493-511. https://doi.org/10.1534/genetics.116.187278

Publication

Genetics
ISSN: 1943-2631
NlmUniqueID: 0374636
Country: United States
Language: English
Volume: 203
Issue: 1
Pages: 493-511

Researcher Affiliations

Schraiber, Joshua G
  • Department of Genome Sciences, University of Washington, Seattle, Washington 98195 schraib@uw.edu.
Evans, Steven N
  • Department of Statistics, University of California, Berkeley, California Department of Mathematics, University of California, Berkeley, California.
Slatkin, Montgomery
  • Department of Integrative Biology, University of California, Berkeley, California 94720.

MeSH Terms

  • Animals
  • Bayes Theorem
  • Diploidy
  • Gene Frequency
  • Horses / genetics
  • Models, Genetic
  • Selection, Genetic
  • Skin Pigmentation / genetics
  • Software

Grant Funding

  • R01 GM040282 / NIGMS NIH HHS
  • R01 GM109454 / NIGMS NIH HHS

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Citations

This article has been cited 60 times.
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