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