Harvard T.H. Chan School of Public Health
Department of Epidemiology
Xueting Qiu is a research associate under the mentorship of Drs. Marc Lipsitch and Bill Hanage in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health. Her research at CCDD focuses on understanding the molecular evolution of Streptococcus Pneumoniae and identifying critical genes/loci under selection that shape the pathogen populations. This work can provide a better understanding of evolutionary trajectories of different genes and population dynamics of Streptococcus Pneumoniae and have the potential to optimize vaccine design. Xueting has actively conducted studies during the pandemic to understand the role of persistently infected individuals in SARS-CoV-2 evolution. Before joining CCDD, Xueting obtained her Medical Degree from School of Medicine, Shanghai Jiao Tong University (China) during 2008-2013. She received her Master of Science in Epidemiology from the University of Texas Health Science Center at Houston in 2015 and her Ph.D. in Infectious Diseases from the University of Georgia in 2019. Her dissertation work focused on applied phylodynamic modeling of respiratory viruses to enhance the understanding of viral evolution and diffusion dynamics in different populations.

Presenter Of 1 Presentation

O026 - WEIGHT SYSTEM ADJUSTS INVASIVE PNEUMOCOCCAL DISEASE SURVEILLANCE DATA TO REPRESENT THE GENOMIC COMPONENTS OF CARRIAGE POPULATION (ID 785)

Session Type
Parallel Session
Date
Mon, 20.06.2022
Session Time
15:20 - 16:35
Room
Birchwood Ballroom
Lecture Time
16:15 - 16:25

Abstract

Background

Invasive pneumococcal disease datasets can reflect the carriage population, but with invasive serotypes overrepresented. To enrich the genomic data-usage of population-based invasive disease data for studying evolutionary dynamics, we aimed to develop a weight system to adjust the invasive disease data to represent carriage population.

Methods

We calculated the weight for each serotype as the ratio of percentage of the serotype in the carriage data to its percentage in all invasive cases, during the pre-vaccine era 1998-2000, in the US. We applied the weights to the accessory gene absence/presence matrix for each sequence in the invasive dataset (n = 11,784), after we conducted a bioinformatic pipeline to generate the set of accessory genes existing in 5% to 95% of the bacteria population.

Results

The weight system significantly improved the correlation of accessory gene frequency between invasive data and carriage population, for pre-vaccine, post-PCV7, and post-PCV13. The correlations of gene frequency between different vaccine periods in invasive data became more similar to those in carriage population. As the negative frequency dependent selection is one of the evolutionary mechanisms for Streptococcus Pneumonia, the accessory gene frequency “rebounce” after vaccine perturbance found in previous studies was observed in the invasive data during post-PCV13, after the weights were applied.

Conclusions

The proper weight system can adjust invasive disease datasets to represent the genomic components of the carriage population of Streptococcus Pneumoniae. Our methods enrich the value of genomic sequences from invasive disease surveillance, as it is common, easy to collect, and of direct interest of public health.

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