Application of clinical text data for phenome-wide association studies (PheWASs)

Bioinformatics. 2015 Jun 15;31(12):1981-7. doi: 10.1093/bioinformatics/btv076. Epub 2015 Feb 4.


Motivation: Genome-wide association studies (GWASs) are effective for describing genetic complexities of common diseases. Phenome-wide association studies (PheWASs) offer an alternative and complementary approach to GWAS using data embedded in the electronic health record (EHR) to define the phenome. International Classification of Disease version 9 (ICD9) codes are used frequently to define the phenome, but using ICD9 codes alone misses other clinically relevant information from the EHR that can be used for PheWAS analyses and discovery.

Results: As an alternative to ICD9 coding, a text-based phenome was defined by 23 384 clinically relevant terms extracted from Marshfield Clinic's EHR. Five single nucleotide polymorphisms (SNPs) with known phenotypic associations were genotyped in 4235 individuals and associated across the text-based phenome. All five SNPs genotyped were associated with expected terms (P<0.02), most at or near the top of their respective PheWAS ranking. Raw association results indicate that text data performed equivalently to ICD9 coding and demonstrate the utility of information beyond ICD9 coding for application in PheWAS.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Disease / genetics*
  • Genetic Predisposition to Disease / genetics*
  • Genome-Wide Association Study*
  • Genotype
  • Humans
  • Phenotype*
  • Polymorphism, Single Nucleotide / genetics*