A highly specific algorithm for identifying asthma cases and controls for genome-wide association studies.
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.
Pacheco
Jennifer A
JA
Northwestern University, Chicago, IL;
Avila
Pedro C
PC
Thompson
Jason A
JA
Law
May
M
Quraishi
Jihan A
JA
Greiman
Alyssa K
AK
Just
Eric M
EM
Kho
Abel
A
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*