Contemporary Ne estimation using temporally spaced data with linked loci
File(s)
Author(s)
Hui, Tin-Yu J
Brenas, Jon Hael
Burt, Austin
Type
Journal Article
Abstract
The contemporary effective population size Ne is important in many disciplines including population genetics, conservation science and pest management. One of the most
popular methods of estimating this quantity uses temporal changes in allele frequency
due to genetic drift. A significant assumption of the existing methods is the independence among loci while constructing confidence intervals (CI), which restricts the types
of species or genetic data applicable to the methods. Although genetic linkage does
not bias point Ne estimates, applying these methods to linked loci can yield unreliable
CI that are far too narrow. We extend the current methods to enable the use of many
linked loci to produce precise contemporary Ne estimates, while preserving the targeted CI width and coverage. This is achieved by deriving the covariance of changes in
allele frequency at linked loci in the face of recombination and sampling errors, such
that the extra sampling variance due to between-locus correlation is properly handled. Extensive simulations are used to verify the new method. We apply the method
to two temporally spaced genomic data sets of Anopheles mosquitoes collected from
a cluster of villages in Burkina Faso between 2012 and 2014. With over 33,000 linked
loci considered, the Ne estimate for Anopheles coluzzii is 9,242 (95% CI 5,702–24,282),
and for Anopheles gambiae it is 4,826 (95% CI 3,602–7,353).
popular methods of estimating this quantity uses temporal changes in allele frequency
due to genetic drift. A significant assumption of the existing methods is the independence among loci while constructing confidence intervals (CI), which restricts the types
of species or genetic data applicable to the methods. Although genetic linkage does
not bias point Ne estimates, applying these methods to linked loci can yield unreliable
CI that are far too narrow. We extend the current methods to enable the use of many
linked loci to produce precise contemporary Ne estimates, while preserving the targeted CI width and coverage. This is achieved by deriving the covariance of changes in
allele frequency at linked loci in the face of recombination and sampling errors, such
that the extra sampling variance due to between-locus correlation is properly handled. Extensive simulations are used to verify the new method. We apply the method
to two temporally spaced genomic data sets of Anopheles mosquitoes collected from
a cluster of villages in Burkina Faso between 2012 and 2014. With over 33,000 linked
loci considered, the Ne estimate for Anopheles coluzzii is 9,242 (95% CI 5,702–24,282),
and for Anopheles gambiae it is 4,826 (95% CI 3,602–7,353).
Date Issued
2021-10
Date Acceptance
2021-04-27
Citation
Molecular Ecology Resources, 2021, 21 (7), pp.2221-2230
ISSN
1471-8278
Publisher
Wiley
Start Page
2221
End Page
2230
Journal / Book Title
Molecular Ecology Resources
Volume
21
Issue
7
Copyright Statement
© 2021 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000664263800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
ANOPHELES-GAMBIAE
Biochemistry & Molecular Biology
Ecology
effective population size
EFFECTIVE POPULATION-SIZE
Environmental Sciences & Ecology
Evolutionary Biology
GENETIC DRIFT
Life Sciences & Biomedicine
linkage disequilibrium
MALARIA MOSQUITO
MAXIMUM-LIKELIHOOD
POLYMORPHISM
recombination
SAMPLES
Science & Technology
SELECTION
temporal samples
Publication Status
Published
Date Publish Online
2021-05-05