Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data
Summary: Background: Assessing relatedness of pathogen sequences in clinical samples is a core goal in molecular epidemiology. Tools for Bayesian analysis of phylogeny, such as the BEAST software package, have been typically used in the analysis of sequence/time data in public health. However, they...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | EBioMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396422001736 |
| _version_ | 1819062402902130688 |
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| author | Ana Raquel Penedos Aurora Fernández-García Mihaela Lazar Kajal Ralh David Williams Kevin E. Brown |
| author_facet | Ana Raquel Penedos Aurora Fernández-García Mihaela Lazar Kajal Ralh David Williams Kevin E. Brown |
| author_sort | Ana Raquel Penedos |
| collection | DOAJ |
| description | Summary: Background: Assessing relatedness of pathogen sequences in clinical samples is a core goal in molecular epidemiology. Tools for Bayesian analysis of phylogeny, such as the BEAST software package, have been typically used in the analysis of sequence/time data in public health. However, they are computationally-, time-, and knowledge-intensive, demanding resources that many laboratories do not have available or cannot allocate frequently. Methods: To evaluate a faster and simpler alternative method to support the routine interpretation of sequence data for epidemiology, we obtained sequences for two regions in the measles virus genome, N-450 and MF-NCR, from patient samples of genotypes B3, D4 and D8 taken between 2011 and 2017 in the UK and Romania. A mathematical model incorporating time, possible shared ancestry and the Poisson distribution describing the number of expected substitutions at a given time point was developed to exclude epidemiological relatedness between pairs of sequences. The model was validated against the commonly used Bayesian phylogenetic method using an independent dataset collected in 2017–19. Findings: We demonstrate that our model, using time and sequence information to predict whether two samples may be related within a given time frame, minimises the risk of erroneous exclusion of relatedness. An easy-to-use implementation in the form of a guide and spreadsheet is provided for convenient application. Interpretation: The proposed model only requires a previously calculated substitution rate for the locus and pathogen of interest. It allows for an informed but quick decision on the likelihood of relatedness between two samples within a time frame, without the need for phylogenetic reconstruction, thus facilitating rapid epidemiological interpretation of sequence data. Funding: This work was funded by the United Kingdom Health Security Agency (UKHSA). The World Health Organization European Regional Office funded Aurora Fernández-García and Mihaela Lazar training visits to UKHSA. |
| first_indexed | 2024-12-21T14:58:13Z |
| format | Article |
| id | doaj.art-7e26c8cbc6f94acc815e2513da73eecd |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-12-21T14:58:13Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-7e26c8cbc6f94acc815e2513da73eecd2022-12-21T18:59:41ZengElsevierEBioMedicine2352-39642022-05-0179103989Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology dataAna Raquel Penedos0Aurora Fernández-García1Mihaela Lazar2Kajal Ralh3David Williams4Kevin E. Brown5Virus Reference Department, United Kingdom Health Security Agency, London NW9 5EQ, United Kingdom; Corresponding author.National Reference Laboratory for Measles and Rubella, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Madrid, Majadahonda, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, SpainCantacuzino, National Military-Medical Institute for Research and Development, Bucharest, RomaniaVirus Reference Department, United Kingdom Health Security Agency, London NW9 5EQ, United KingdomVirus Reference Department, United Kingdom Health Security Agency, London NW9 5EQ, United KingdomVirus Reference Department, United Kingdom Health Security Agency, London NW9 5EQ, United Kingdom; Immunisation and Countermeasures, United Kingdom Health Security Agency, London NW9 5EQ, United KingdomSummary: Background: Assessing relatedness of pathogen sequences in clinical samples is a core goal in molecular epidemiology. Tools for Bayesian analysis of phylogeny, such as the BEAST software package, have been typically used in the analysis of sequence/time data in public health. However, they are computationally-, time-, and knowledge-intensive, demanding resources that many laboratories do not have available or cannot allocate frequently. Methods: To evaluate a faster and simpler alternative method to support the routine interpretation of sequence data for epidemiology, we obtained sequences for two regions in the measles virus genome, N-450 and MF-NCR, from patient samples of genotypes B3, D4 and D8 taken between 2011 and 2017 in the UK and Romania. A mathematical model incorporating time, possible shared ancestry and the Poisson distribution describing the number of expected substitutions at a given time point was developed to exclude epidemiological relatedness between pairs of sequences. The model was validated against the commonly used Bayesian phylogenetic method using an independent dataset collected in 2017–19. Findings: We demonstrate that our model, using time and sequence information to predict whether two samples may be related within a given time frame, minimises the risk of erroneous exclusion of relatedness. An easy-to-use implementation in the form of a guide and spreadsheet is provided for convenient application. Interpretation: The proposed model only requires a previously calculated substitution rate for the locus and pathogen of interest. It allows for an informed but quick decision on the likelihood of relatedness between two samples within a time frame, without the need for phylogenetic reconstruction, thus facilitating rapid epidemiological interpretation of sequence data. Funding: This work was funded by the United Kingdom Health Security Agency (UKHSA). The World Health Organization European Regional Office funded Aurora Fernández-García and Mihaela Lazar training visits to UKHSA.http://www.sciencedirect.com/science/article/pii/S2352396422001736MeaslesOutbreakEliminationEpidemiologyMolecular epidemiologyClinical virology |
| spellingShingle | Ana Raquel Penedos Aurora Fernández-García Mihaela Lazar Kajal Ralh David Williams Kevin E. Brown Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data EBioMedicine Measles Outbreak Elimination Epidemiology Molecular epidemiology Clinical virology |
| title | Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data |
| title_full | Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data |
| title_fullStr | Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data |
| title_full_unstemmed | Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data |
| title_short | Mind your Ps: A probabilistic model to aid the interpretation of molecular epidemiology data |
| title_sort | mind your ps a probabilistic model to aid the interpretation of molecular epidemiology data |
| topic | Measles Outbreak Elimination Epidemiology Molecular epidemiology Clinical virology |
| url | http://www.sciencedirect.com/science/article/pii/S2352396422001736 |
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