Tanja Stadler | Search bioRxiv

bioRxiv2017

Accurate detection of HIV transmission clusters from phylogenetic trees using a multi-state birth-death model

HIV transmission networks are highly clustered, and accurate identification of these clusters is essential for effective targeting of public health interventions. This clustering affects the transmission dynamics of the HIV epidemic, which affects the pathogen phylogenies reconstructed from patient samples. We present a new method for identifying transmission clusters by detecting the changes in transmission rate provoked by the introduction of the epidemic into a new cluster. The method employs a multi-state birth-death (MSBD) model where each state represents a cluster. Transmission rates in each cluster decrease exponentially over time, simulating susceptible depletion in the cluster. This model is fitted to the pathogen phylogeny using a Maximum Likelihood approach. Using simulated datasets we show that the MSBD method is able to reliably infer both the cluster repartition and the transmission parameters from a pathogen phylogeny. In contrast to existing cutpoint-based methods for cluster identification, which are dependent on a parameter set by the user, the MSBD method is consistently reliable. It also performs better on phylogenies containing nested clusters. We present an application of our method to the inference of transmission clusters using sequences obtained from the Swiss HIV Cohort Study. The MSBD method is available as an R package.

bioinformatics↗

bioRxiv2016

The heritability of pathogen traits - definitions and estimators

Pathogen traits, such as the virulence of an infection, can vary significantly between patients. A major challenge is to measure the extent to which genetic differences between infecting strains explain the observed variation of the trait. This is quantified by the traits broad-sense heritability, H2. A recent discrepancy between estimates of the heritability of HIV-virulence has opened a debate on the estimators accuracy. Here, we show that the discrepancy originates from model limitations and important lifecycle differences between sexually reproducing organisms and transmittable pathogens. In particular, current quantitative genetics methods, such as donor-recipient regression (DR) of surveyed serodiscordant couples and the phylogenetic mixed model (PMM), are prone to underestimate H2, because they fail to model the gradual loss of phenotypic resemblance between transmission-related patients in the presence of within-host evolution. We explore two approaches correcting these errors: ANOVA on closest phylogenetic pairs (ANOVA-CPP) and the phylogenetic Ornstein-Uhlenbeck mixed model (POUMM). Empirical analyses reveal that at least 25% of the variation in HIV-virulence is explained by the virus genome both for European and African data. These results confirm the presence of significant factors for HIV virulence in the viral genotype and reject previous hypotheses of negligible viral influence. Beyond HIV, ANOVA-CPP is ideal for slowly evolving protozoa, bacteria and DNA-viruses, while POUMM suits rapidly mutating RNA-viruses, thus, enabling heritability estimation for a broad range of pathogens.

Genetics↗