An Automated Framework for Integrating Genetic Susceptibility and Air Pollution Exposure into Individualized Asthma Risk Reports

Modern personalized medicine needs the integration of genetic, environmental, and spatial data for the comprehensive assessment of individual disease risk. However, combining disparate data sources into a single, interpretable result remains a complex bioinformatics challenge. Existing approaches to generating individual reports often rely on disparate analytical steps and partial manual processing, limiting their scalability, reproducibility, and standardization. Python pipeline was developed to integrate genetic marker filtering and annotation, individual polygenic risk score (PRS) calculation with percentile ranking, spatial air pollution estimates (LUR), and report generation via Jinja2 and WeasyPrint with separation of logic and visual presentation. The system generates an individual asthma research report with two blocks: genetic predisposition to bronchial asthma and genetic susceptibility to four pollutants corresponding to PM2.5, CO, NO2 and SO2. Interpretation is based on OR, PRS with positioning in the sampling distribution and an environmental exposure map linked to the participant’s location, ensuring reproducibility and reducing time costs. The proposed tool allows for standardized feedback from study participants, reduces the likelihood of errors in manual report preparation, and ensures transparent interpretation of genetic and environmental data, which is critical for bioinformatics and epidemiological work.