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Demographic inference for spatially heterogeneous populations using long shared haplotypes

We introduce a modified spatial Λ-Fleming-Viot process to model the ancestry of individuals in a population occupying a continuous spatial habitat divided into two areas by a sharp discontinuity of the dispersal rate and effective population density. We derive an analytical formula for the expected...

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Autores principales: Forien, Raphaël, Ringbauer, Harald, Coop, Graham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312651/
https://www.ncbi.nlm.nih.gov/pubmed/37398501
http://dx.doi.org/10.1101/2023.06.13.544589
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author Forien, Raphaël
Ringbauer, Harald
Coop, Graham
author_facet Forien, Raphaël
Ringbauer, Harald
Coop, Graham
author_sort Forien, Raphaël
collection PubMed
description We introduce a modified spatial Λ-Fleming-Viot process to model the ancestry of individuals in a population occupying a continuous spatial habitat divided into two areas by a sharp discontinuity of the dispersal rate and effective population density. We derive an analytical formula for the expected number of shared haplotype segments between two individuals depending on their sampling locations. This formula involves the transition density of a skew diffusion which appears as a scaling limit of the ancestral lineages of individuals in this model. We then show that this formula can be used to infer the dispersal parameters and the effective population density of both regions, using a composite likelihood approach, and we demonstrate the efficiency of this method on a range of simulated data sets.
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spelling pubmed-103126512023-07-01 Demographic inference for spatially heterogeneous populations using long shared haplotypes Forien, Raphaël Ringbauer, Harald Coop, Graham bioRxiv Article We introduce a modified spatial Λ-Fleming-Viot process to model the ancestry of individuals in a population occupying a continuous spatial habitat divided into two areas by a sharp discontinuity of the dispersal rate and effective population density. We derive an analytical formula for the expected number of shared haplotype segments between two individuals depending on their sampling locations. This formula involves the transition density of a skew diffusion which appears as a scaling limit of the ancestral lineages of individuals in this model. We then show that this formula can be used to infer the dispersal parameters and the effective population density of both regions, using a composite likelihood approach, and we demonstrate the efficiency of this method on a range of simulated data sets. Cold Spring Harbor Laboratory 2023-06-13 /pmc/articles/PMC10312651/ /pubmed/37398501 http://dx.doi.org/10.1101/2023.06.13.544589 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Forien, Raphaël
Ringbauer, Harald
Coop, Graham
Demographic inference for spatially heterogeneous populations using long shared haplotypes
title Demographic inference for spatially heterogeneous populations using long shared haplotypes
title_full Demographic inference for spatially heterogeneous populations using long shared haplotypes
title_fullStr Demographic inference for spatially heterogeneous populations using long shared haplotypes
title_full_unstemmed Demographic inference for spatially heterogeneous populations using long shared haplotypes
title_short Demographic inference for spatially heterogeneous populations using long shared haplotypes
title_sort demographic inference for spatially heterogeneous populations using long shared haplotypes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312651/
https://www.ncbi.nlm.nih.gov/pubmed/37398501
http://dx.doi.org/10.1101/2023.06.13.544589
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