What Can Genetic Relatedness Tell Us About Risk Factors for Tuberculosis Transmission?

Abstract

Background: To stop tuberculosis (TB), the leading infectious cause of death globally, we need to better understand transmission risk factors. Although many studies have identified associations between individual-level covariates and pathogen genetic relatedness, few have identified characteristics of transmission pairs or explored how closely covariates associated with genetic relatedness mirror those associated with transmission.

Methods: We simulated a TB-like outbreak with pathogen genetic data and estimated odds ratios (ORs) to correlate each covariate and genetic relatedness. We used a naive Bayes approach to modify the genetic links and nonlinks to resemble the true links and nonlinks more closely and estimated modified ORs with this approach. We compared these two sets of ORs with the true ORs for transmission. Finally, we applied this method to TB data in Hamburg, Germany, and Massachusetts, USA, to find pair-level covariates associated with transmission.

Results: Using simulations, we found that associations between covariates and genetic relatedness had the same relative magnitudes and directions as the true associations with transmission, but biased absolute magnitudes. Modifying the genetic links and nonlinks reduced the bias and increased the confidence interval widths, more accurately capturing error. In Hamburg and Massachusetts, pairs were more likely to be probable transmission links if they lived in closer proximity, had a shorter time between observations, or had shared ethnicity, social risk factors, drug resistance, or genotypes.

Conclusions: We developed a method to improve the use of genetic relatedness as a proxy for transmission, and aid in understanding TB transmission dynamics in low-burden settings.

Figure 1.

Diagram of the associations with covariates. A) Diagram of the association between covariates, unobserved transmission (OR^T), and observed genetic relatedness (OR^G). B) Diagram of the relationship between the covariates, transmission (OR^T), and training links (ie. genetic relatedness), and the naive Bayes modified transmission links (OR^M) which are then used to estimate transmission probabilities.

Figure 2.

Bias of covariate association estimation. Plot of the mean estimated log odds ratio across 1000 simulated outbreaks representing the relationship between the simulated covariate and close genetic relatedness (log OR^G, light grey) or naive Bayes modified close genetic relatedness (log OR^M). The black dot represents the true log odds ratio (log OR^T) calculated as the average of the log odds ratio for the relationship between the covariates and true transmission across the 1000 simulations.

Figure 3.

Covariate contribution for Hamburg. Plot of the naive Bayes modified odds ratios (OR^M) of the relationship between various covariates and close genetic relatedness (dark grey) and having a confirmed contact (light grey) with 95% confidence intervals for a small TB outbreak in Hamburg, Germany. These odds ratios represent the contribution of each covariate value to the transmission probabilities estimated for the outbreak where a higher odds ratio indicates a higher probability of a probable transmission link.

Figure 4.

Covariate contribution for Massachusetts. Plot of the naive Bayes modified odds ratios (OR^M) of the relationship between various covariates and having a confirmed contact with 95% confidence intervals for TB surveillance data in Massachusetts between 2010-2016. (For this analysis we did not have genetic data so there is no comparison with a training dataset defined by close genetic relatedness). These odds ratios represent the contribution of each covariate value to the transmission probabilities estimated for the outbreak where a higher odds ratio indicates a higher probability of a probable transmission link.