A highly specific algorithm for identifying asthma cases and controls for genome-wide association studies.

Jennifer A. Pacheco*, Pedro C. Avila, Jason A. Thompson, May Law, Jihan A. Quraishi, Alyssa K. Greiman, Eric M. Just, Abel Kho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Our aim was to identify asthmatic patients as cases, and healthy patients as controls, for genome-wide association studies (GWAS), using readily available data from electronic medical records. For GWAS, high specificity is required to accurately identify genotype-phenotype correlations. We developed two algorithms using a combination of diagnoses, medications, and smoking history. By applying stringent criteria for source and specificity of the data we achieved a 95% positive predictive value and 96% negative predictive value for identification of asthma cases and controls compared against clinician review. We achieved a high specificity but at the loss of approximately 24% of the initial number of potential asthma cases we found. However, by standardizing and applying our algorithm across multiple sites, the high number of cases needed for a GWAS could be achieved.

Original languageEnglish (US)
Pages (from-to)497-501
Number of pages5
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2009
StatePublished - 2009

ASJC Scopus subject areas

  • Medicine(all)

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