Technical Report: Protocol for Characterizing Phenotype Variants Using Phenome-Wide Association Study (PheWAS)...

Area of Science:

• Genetics and Genomics
• Computational Biology
• Medical Informatics

Background:

• Phenome-wide association studies (PheWAS) are crucial for understanding genetic associations with complex diseases.
• The Nationwide Inpatient Sample (NIS) 2020 provides a large dataset for epidemiological research.
• Pancreatic cysts and lung cancer represent significant health challenges requiring further etiological investigation.

Purpose of the Study:

• To provide a comprehensive, step-by-step guide for conducting PheWAS.
• To specifically tailor the methodology for a cohort with pancreatic cysts and lung cancer.
• To ensure clarity, reproducibility, and robust data analysis in PheWAS.

Main Methods:

• Utilized Nationwide Inpatient Sample (NIS) 2020 data.
• Developed a curated cohort from a GitHub repository.
• Employed R for data analysis, including QQ plots and Manhattan plots.
• Implemented a standardized approach for segmenting International Classification of Disease version 10 (ICD-10) codes.

Main Results:

• Established a reproducible workflow for PheWAS in the target cohort.
• Demonstrated the utility of QQ and Manhattan plots for bias detection and result interpretation.
• Provided a practical method for analyzing associations across different ICD-10 diagnostic categories.

Conclusions:

• The developed workflow facilitates rigorous PheWAS for pancreatic cysts and lung cancer.
• Standardized ICD-10 code segmentation enhances the interpretability of diagnostic associations.
• This guide promotes transparency and reproducibility in genetic association studies.