4.6 Article

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

Journal

EPIDEMIOLOGY
Volume 33, Issue 1, Pages 55-64

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/EDE.0000000000001414

Keywords

Cluster analysis; Infectious disease transmission; Naive Bayes; Whole genome sequencing

Funding

  1. NIHGMS [T32GM074905, K01AI102944]
  2. US National Institutes of Health [NIH K01AI102944]
  3. Providence/Boston Center for AIDS Research [P30AI042853]
  4. Boston University/Rutgers Tuberculosis Research Unit [U19AI111276]
  5. Canadian Institutes of Health Research [MFE-152448]
  6. US-India Vaccine Action Program Initiative on Tuberculosis

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This study investigates the association between risk factors and genetic relatedness in tuberculosis transmission. By simulating a TB-like outbreak and modifying the genetic links, the study improves the accuracy of using genetic relatedness as a proxy for transmission. The findings suggest that pairs with closer proximity, shorter time between observations, and shared ethnicity, social risk factors, drug resistance, or genotypes are more likely to be transmission links.
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.

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