title: A highly specific algorithm for identifying asthma cases and controls for genome-wide association studies.
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.
authors.affiliation: Northwestern University, Chicago, IL;
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Pacheco
authors.forename: Pedro C
authors.initials: PC
authors.lastname: Avila
authors.forename: Jason A
authors.initials: JA
authors.lastname: Thompson
authors.forename: May
authors.initials: M
authors.lastname: Law
authors.forename: Jihan A
authors.initials: JA
authors.lastname: Quraishi
authors.forename: Alyssa K
authors.initials: AK
authors.lastname: Greiman
authors.forename: Eric M
authors.initials: EM
authors.lastname: Just
authors.forename: Abel
authors.initials: A
authors.lastname: Kho
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000175:diagnosis* / Q000235:genetics; D030561:Databases, Nucleic Acid; D057286:Electronic Health Records; D055106:Genome-Wide Association Study; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*