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Distinguishing gene flow between malaria parasite populations

View ORCID ProfileTyler S. Brown, View ORCID ProfileAimee R. Taylor, View ORCID ProfileOlufunmilayo Arogbokun, Caroline O. Buckee, View ORCID ProfileHsiao-Han Chang
doi: https://doi.org/10.1101/2021.01.08.425858
Tyler S. Brown
1Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health
2Infectious Diseases Division, Massachusetts General Hospital
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  • For correspondence: tsbrown@mgh.harvard.edu
Aimee R. Taylor
1Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health
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Olufunmilayo Arogbokun
3Infectious Disease Epidemiology and Ecology Lab, University of North Carolina School of Medicine
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Caroline O. Buckee
1Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health
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Hsiao-Han Chang
1Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health
4Institute of Bioinformatics and Structural Biology, National Tsing Hua University
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Abstract

Measuring gene flow between malaria parasite populations in different geographic locations can provide strategic information for malaria control interventions. Multiple important questions pertaining to the design of such studies remain unanswered, limiting efforts to operationalize genomic surveillance tools for routine public health use. This report evaluates numerically the ability to distinguish different levels of gene flow between malaria populations, using different amounts of real and simulated data, where data are simulated using parameters that approximate different epidemiological conditions. Specifically, using Plasmodium falciparum whole genome sequence data and sequence data simulated for a metapopulation with different migration rates and effective population sizes, we compare two estimators of gene flow, explore the number of genetic markers and number of individuals required to reliably rank highly connected locations, and describe how these thresholds change given different effective population sizes and migration rates. Our results have implications for the design and implementation of malaria genomic surveillance efforts.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 08, 2021.
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Distinguishing gene flow between malaria parasite populations
Tyler S. Brown, Aimee R. Taylor, Olufunmilayo Arogbokun, Caroline O. Buckee, Hsiao-Han Chang
bioRxiv 2021.01.08.425858; doi: https://doi.org/10.1101/2021.01.08.425858
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Distinguishing gene flow between malaria parasite populations
Tyler S. Brown, Aimee R. Taylor, Olufunmilayo Arogbokun, Caroline O. Buckee, Hsiao-Han Chang
bioRxiv 2021.01.08.425858; doi: https://doi.org/10.1101/2021.01.08.425858

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