Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent
With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and e...
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| Published in | PLoS genetics Vol. 13; no. 10; p. e1007065 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
27.10.2017
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1553-7404 1553-7390 1553-7404 |
| DOI | 10.1371/journal.pgen.1007065 |
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| Abstract | With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs. |
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| AbstractList | With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs. The spatiotemporal dispersal of organisms can inform efforts to conserve endangered species, to contain the spread of drug resistance, and to eliminate disease. As genomic data become increasingly more affordable and accessible via public depositories, the demand for methods capable of extracting fine-scale population structure from genomic data grows. However, to the best of our knowledge, there are no guidelines regarding the type of data and methods required to resolve local spatial trends over sexually recombining haploid organisms, such as the malaria parasite. The approach we present here compares relatedness based on identity by descent, which accounts for recombination while distinguishing genetic identity due to inheritance from genetic identity due to chance, to a classic population genetic measure of divergence, using data from sexually recombining malaria parasites. Using identity by descent, we uncover a significant decrease in highly related malaria parasites collected from proximal clinics on the Thai-Myanmar border, a region where human mobility is high. Our results demonstrate the power of analyses based on identity by descent to detect recent and local trends. Similar analyses could be used to inform the molecular epidemiology of other sexually recombining organisms. With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (F.sub.ST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and F.sub.ST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs. With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs. With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs.With the rapidly increasing abundance and accessibility of genomic data, there is a growing interest in using population genetic approaches to characterize fine-scale dispersal of organisms, providing insight into biological processes across a broad range of fields including ecology, evolution and epidemiology. For sexually recombining haploid organisms such as the human malaria parasite P. falciparum, however, there have been no systematic assessments of the type of data and methods required to resolve fine scale connectivity. This analytical gap hinders the use of genomics for understanding local transmission patterns, a crucial goal for policy makers charged with eliminating this important human pathogen. Here we use data collected from four clinics with a catchment area spanning approximately 120 km of the Thai-Myanmar border to compare the ability of divergence (FST) and relatedness based on identity by descent (IBD) to resolve spatial connectivity between malaria parasites collected from proximal clinics. We found no relationship between inter-clinic distance and FST, likely due to sampling of highly related parasites within clinics, but a significant decline in IBD-based relatedness with increasing inter-clinic distance. This association was contingent upon the data set type and size. We estimated that approximately 147 single-infection whole genome sequenced parasite samples or 222 single-infection parasite samples genotyped at 93 single nucleotide polymorphisms (SNPs) were sufficient to recover a robust spatial trend estimate at this scale. In summary, surveillance efforts cannot rely on classical measures of genetic divergence to measure P. falciparum transmission on a local scale. Given adequate sampling, IBD-based relatedness provides a useful alternative, and robust trends can be obtained from parasite samples genotyped at approximately 100 SNPs. |
| Audience | Academic |
| Author | Taylor, Aimee R. Neafsey, Daniel E. Pyae Phyo, Aung Buckee, Caroline O. Cerqueira, Gustavo C. Anderson, Timothy J. C. Schaffner, Stephen F. Sriprawat, Kanlaya Nkhoma, Standwell C. Nosten, François |
| AuthorAffiliation | 2 Infectious Disease and Microbiome Program, Broad Institute, Cambridge, Massachusetts, United States of America 6 Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America 4 Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand 1 Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America 5 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine Research building, University of Oxford, Old Road campus, Oxford, United Kingdom Imperial College London, UNITED KINGDOM 3 Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, United States of America |
| AuthorAffiliation_xml | – name: 5 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine Research building, University of Oxford, Old Road campus, Oxford, United Kingdom – name: 2 Infectious Disease and Microbiome Program, Broad Institute, Cambridge, Massachusetts, United States of America – name: 3 Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, United States of America – name: 6 Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America – name: Imperial College London, UNITED KINGDOM – name: 4 Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand – name: 1 Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America |
| Author_xml | – sequence: 1 givenname: Aimee R. orcidid: 0000-0002-2337-8992 surname: Taylor fullname: Taylor, Aimee R. – sequence: 2 givenname: Stephen F. orcidid: 0000-0001-6699-3568 surname: Schaffner fullname: Schaffner, Stephen F. – sequence: 3 givenname: Gustavo C. orcidid: 0000-0002-7692-3886 surname: Cerqueira fullname: Cerqueira, Gustavo C. – sequence: 4 givenname: Standwell C. orcidid: 0000-0002-7137-7935 surname: Nkhoma fullname: Nkhoma, Standwell C. – sequence: 5 givenname: Timothy J. C. surname: Anderson fullname: Anderson, Timothy J. C. – sequence: 6 givenname: Kanlaya surname: Sriprawat fullname: Sriprawat, Kanlaya – sequence: 7 givenname: Aung orcidid: 0000-0002-0383-9624 surname: Pyae Phyo fullname: Pyae Phyo, Aung – sequence: 8 givenname: François orcidid: 0000-0002-7951-0745 surname: Nosten fullname: Nosten, François – sequence: 9 givenname: Daniel E. surname: Neafsey fullname: Neafsey, Daniel E. – sequence: 10 givenname: Caroline O. surname: Buckee fullname: Buckee, Caroline O. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29077712$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2017 Public Library of Science 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: malaria parasite populations using identity by descent. PLoS Genet13(10): e1007065. https://doi.org/10.1371/journal.pgen.1007065 2017 Taylor et al 2017 Taylor et al 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: malaria parasite populations using identity by descent. PLoS Genet13(10): e1007065. https://doi.org/10.1371/journal.pgen.1007065 |
| Copyright_xml | – notice: COPYRIGHT 2017 Public Library of Science – notice: 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: malaria parasite populations using identity by descent. PLoS Genet13(10): e1007065. https://doi.org/10.1371/journal.pgen.1007065 – notice: 2017 Taylor et al 2017 Taylor et al – notice: 2017 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: malaria parasite populations using identity by descent. PLoS Genet13(10): e1007065. https://doi.org/10.1371/journal.pgen.1007065 |
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| Notes | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist. DEN and COB are joint senior authors on this work. |
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| SubjectTerms | Apis mellifera Biological evolution Biology and Life Sciences Biomedical research Catchment areas Clinics Connectivity Descent Dispersal Distribution Divergence DNA, Protozoan - genetics Epidemiology Funding Genetic aspects Genetic divergence Genetics Genome, Protozoan - genetics Genomes Haplotypes - genetics Health aspects Humans Infectious diseases Investigations Malaria Malaria, Falciparum - parasitology Medicine Medicine and Health Sciences Pandemics Parasites Paternity People and Places Physical Sciences Plasmodium falciparum Plasmodium falciparum - genetics Polymorphism, Single Nucleotide - genetics Population Population genetics Prosopis alba Public health Sampling Single-nucleotide polymorphism Social aspects Software Supervision Thailand Trends |
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| Title | Quantifying connectivity between local Plasmodium falciparum malaria parasite populations using identity by descent |
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