Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.
Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random samp...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-11-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010745 |
| _version_ | 1797788319934840832 |
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| author | Connor Chato Yi Feng Yuhua Ruan Hui Xing Joshua Herbeck Marcia Kalish Art F Y Poon |
| author_facet | Connor Chato Yi Feng Yuhua Ruan Hui Xing Joshua Herbeck Marcia Kalish Art F Y Poon |
| author_sort | Connor Chato |
| collection | DOAJ |
| description | Clusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies. |
| first_indexed | 2024-03-13T01:33:54Z |
| format | Article |
| id | doaj.art-67112e635a2a48d4927fd8683a38d966 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-13T01:33:54Z |
| publishDate | 2022-11-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-67112e635a2a48d4927fd8683a38d9662023-07-04T05:31:28ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-11-011811e101074510.1371/journal.pcbi.1010745Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.Connor ChatoYi FengYuhua RuanHui XingJoshua HerbeckMarcia KalishArt F Y PoonClusters of genetically similar infections suggest rapid transmission and may indicate priorities for public health action or reveal underlying epidemiological processes. However, clusters often require user-defined thresholds and are sensitive to non-epidemiological factors, such as non-random sampling. Consequently the ideal threshold for public health applications varies substantially across settings. Here, we show a method which selects optimal thresholds for phylogenetic (subset tree) clustering based on population. We evaluated this method on HIV-1 pol datasets (n = 14, 221 sequences) from four sites in USA (Tennessee, Washington), Canada (Northern Alberta) and China (Beijing). Clusters were defined by tips descending from an ancestral node (with a minimum bootstrap support of 95%) through a series of branches, each with a length below a given threshold. Next, we used pplacer to graft new cases to the fixed tree by maximum likelihood. We evaluated the effect of varying branch-length thresholds on cluster growth as a count outcome by fitting two Poisson regression models: a null model that predicts growth from cluster size, and an alternative model that includes mean collection date as an additional covariate. The alternative model was favoured by AIC across most thresholds, with optimal (greatest difference in AIC) thresholds ranging 0.007-0.013 across sites. The range of optimal thresholds was more variable when re-sampling 80% of the data by location (IQR 0.008 - 0.016, n = 100 replicates). Our results use prospective phylogenetic cluster growth and suggest that there is more variation in effective thresholds for public health than those typically used in clustering studies.https://doi.org/10.1371/journal.pcbi.1010745 |
| spellingShingle | Connor Chato Yi Feng Yuhua Ruan Hui Xing Joshua Herbeck Marcia Kalish Art F Y Poon Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. PLoS Computational Biology |
| title | Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. |
| title_full | Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. |
| title_fullStr | Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. |
| title_full_unstemmed | Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. |
| title_short | Optimized phylogenetic clustering of HIV-1 sequence data for public health applications. |
| title_sort | optimized phylogenetic clustering of hiv 1 sequence data for public health applications |
| url | https://doi.org/10.1371/journal.pcbi.1010745 |
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