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

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Pacheco JA, Avila PC, Thompson JA, Law M, Quraishi JA,

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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.
This article was published in AMIA Annu Symp Proc and referenced in International Journal of Biomedical Data Mining

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