Student Posters Repository

Mapping the relationship for phenotypic traits is an essential way to help understand the mechanisms underlying common human diseases. However, limited researches has been conducted to investigate the interconnections among diseases using a combination of genotype data and phenotypic information, and existing methods for evaluating such association are not scalable to large number of phenotypic trait pairs. Our study utilizes parallel programming to efficiently quantify the cross-phenotypic association among 1,403 common human diseases using hypergeometric test based on phenotype information extracted from electronic health records (EHR) and results from phenome­wide association study (PheWAS) from UK Biobank.