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Inferring the source of transmission with phylogenetic data.

ABSTRACT: Identifying the source of transmission using pathogen genetic data is complicated by numerous biological, immunological, and behavioral factors. A large source of error arises when there is incomplete or sparse sampling of cases. Unsampled cases may act as either a common source of infection or as an intermediary in a transmission chain for hosts infected with genetically similar pathogens. It is difficult to quantify the probability of common source or intermediate transmission events, which has made it difficult to develop statistical tests to either confirm or deny putative transmission pairs with genetic data. We present a method to incorporate additional information about an infectious disease epidemic, such as incidence and prevalence of infection over time, to inform estimates of the probability that one sampled host is the direct source of infection of another host in a pathogen gene genealogy. These methods enable forensic applications, such as source-case attribution, for infectious disease epidemics with incomplete sampling, which is usually the case for high-morbidity community-acquired pathogens like HIV, Influenza and Dengue virus. These methods also enable epidemiological applications such as the identification of factors that increase the risk of transmission. We demonstrate these methods in the context of the HIV epidemic in Detroit, Michigan, and we evaluate the suitability of current sequence databases for forensic and epidemiological investigations. We find that currently available sequences collected for drug resistance testing of HIV are unlikely to be useful in most forensic investigations, but are useful for identifying transmission risk factors.

SUBMITTER: Volz EM

PROVIDER: S-EPMC3868546 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Inferring the source of transmission with phylogenetic data.

Volz Erik M EM Frost Simon D W SD

PLoS computational biology 20131219 12

Identifying the source of transmission using pathogen genetic data is complicated by numerous biological, immunological, and behavioral factors. A large source of error arises when there is incomplete or sparse sampling of cases. Unsampled cases may act as either a common source of infection or as an intermediary in a transmission chain for hosts infected with genetically similar pathogens. It is difficult to quantify the probability of common source or intermediate transmission events, which ha ...[more]

PMID: 24367249

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Project description:BackgroundRecent developments in sequencing technologies make it possible to obtain genome sequences from a large number of isolates in a very short time. Bayesian phylogenetic approaches can take advantage of these data by simultaneously inferring the phylogenetic tree, evolutionary timescale, and demographic parameters (such as population growth rates), while naturally integrating uncertainty in all parameters. Despite their desirable properties, Bayesian approaches can be computationally intensive, hindering their use for outbreak investigations involving genome data for a large numbers of pathogen isolates. An alternative to using full Bayesian inference is to use a hybrid approach, where the phylogenetic tree and evolutionary timescale are estimated first using maximum likelihood. Under this hybrid approach, demographic parameters are inferred from estimated trees instead of the sequence data, using maximum likelihood, Bayesian inference, or approximate Bayesian computation. This can vastly reduce the computational burden, but has the disadvantage of ignoring the uncertainty in the phylogenetic tree and evolutionary timescale.ResultsWe compared the performance of a fully Bayesian and a hybrid method by analysing six whole-genome SNP data sets from a range of bacteria and simulations. The estimates from the two methods were very similar, suggesting that the hybrid method is a valid alternative for very large datasets. However, we also found that congruence between these methods is contingent on the presence of strong temporal structure in the data (i.e. clocklike behaviour), which is typically verified using a date-randomisation test in a Bayesian framework. To reduce the computational burden of this Bayesian test we implemented a date-randomisation test using a rapid maximum likelihood method, which has similar performance to its Bayesian counterpart.ConclusionsHybrid approaches can produce reliable inferences of evolutionary timescales and phylodynamic parameters in a fraction of the time required for fully Bayesian analyses. As such, they are a valuable alternative in outbreak studies involving a large number of isolates.

Project description:Age-mixing patterns are of key importance for understanding the dynamics of human immunodeficiency virus (HIV)-epidemics and target public health interventions. We use the densely sampled Swiss HIV Cohort Study (SHCS) resistance database to study the age difference at infection in HIV transmission pairs using phylogenetic methods. In addition, we investigate whether the mean age difference of pairs in the phylogenetic tree is influenced by sampling as well as by additional distance thresholds for including pairs. HIV-1 pol-sequences of 11,922 SHCS patients and approximately 240,000 Los Alamos background sequences were used to build a phylogenetic tree. Using this tree, 100 per cent down to 1 per cent of the tips were sampled repeatedly to generate pruned trees (N = 500 for each sample proportion), of which pairs of SHCS patients were extracted. The mean of the absolute age differences of the pairs, measured as the absolute difference of the birth years, was analyzed with respect to this sample proportion and a distance criterion for inclusion of the pairs. In addition, the transmission groups men having sex with men (MSM), intravenous drug users (IDU), and heterosexuals (HET) were analyzed separately. Considering the tree with all 11,922 SHCS patients, 2,991 pairs could be extracted, with 954 (31.9 per cent) MSM-pairs, 635 (21.2 per cent) HET-pairs, 414 (13.8 per cent) IDU-pairs, and 352 (11.8 per cent) HET/IDU-pairs. For all transmission groups, the age difference at infection was significantly (P < 0.001) smaller for pairs in the tree compared with randomly assigned pairs, meaning that patients of similar age are more likely to be pairs. The mean age difference in the phylogenetic analysis, using a fixed distance of 0.05, was 9.2, 9.0, 7.3 and 5.6 years for MSM-, HET-, HET/IDU-, and IDU-pairs, respectively. Decreasing the cophenetic distance threshold from 0.05 to 0.01 significantly decreased the mean age difference. Similarly, repeated sampling of 100 per cent down to 1 per cent of the tips revealed an increased age difference at lower sample proportions. HIV-transmission is age-assortative, but the age difference of transmission pairs detected by phylogenetic analyses depends on both sampling proportion and distance criterion. The mean age difference decreases when using more conservative distance thresholds, implying an underestimation of age-assortativity when using liberal distance criteria. Similarly, overestimation of the mean age difference occurs for pairs from sparsely sampled trees, as it is often the case in sub-Saharan Africa.

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Inferring the source of transmission with phylogenetic data.

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Inferring the source of transmission with phylogenetic data.

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