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

Jennifer A Pacheco¹, Pedro C Avila, Jason A Thompson

¹Northwestern University, Chicago, IL;

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 31, 2010

Summary

Researchers developed algorithms to identify asthma patients for genetic studies using electronic health records. These methods achieved high accuracy, enabling large-scale genome-wide association studies (GWAS) for asthma research.

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Area of Science:

Genetics
Computational Biology
Epidemiology

Background:

Genome-wide association studies (GWAS) require precise case and control identification.
Electronic medical records (EMRs) offer a valuable data source for genetic research.
Accurate phenotyping is crucial for robust genotype-phenotype correlations in GWAS.

Purpose of the Study:

To develop and validate algorithms for identifying asthma cases and controls from EMR data for GWAS.
To assess the accuracy and efficiency of these algorithms in a real-world setting.
To determine the feasibility of using EMR data for large-scale genetic association studies in asthma.

Main Methods:

Developed two algorithms combining diagnostic codes, medication records, and smoking history from EMRs.
Applied stringent data quality and specificity criteria for case and control selection.
Validated algorithm performance against manual clinician review.

Main Results:

Achieved 95% positive predictive value and 96% negative predictive value for asthma case/control identification.
High specificity was maintained, though approximately 24% of potential asthma cases were excluded.
Standardized algorithms demonstrated potential for multi-site application to achieve large GWAS cohorts.

Conclusions:

Validated algorithms using EMR data can accurately identify asthma cases and controls for GWAS.
The developed phenotyping strategy balances specificity with cohort size requirements.
This approach facilitates large-scale genetic research in asthma by leveraging routinely collected health data.