Size matters: How population size influences genotype–phenotype association studies in anonymized data

• Published in: Journal of biomedical informatics 2014-12, Vol.52, p.243-250
• Main Authors: Heatherly, Raymond, Denny, Joshua C., Haines, Jonathan L., Roden, Dan M., Malin, Bradley A.
• Format: Article
• Language: English
• Subjects: Algorithms; Anonymization; Biomedical Research - methods; Clinical codes; Computer Simulation; Confidentiality; Correlation; Data publishing; Databases, Genetic; Electronic Health Records; Genetic Association Studies - statistics & numerical data; Genotype; Humans; Phenotype; Polymorphism, Single Nucleotide; Privacy; Releasing; Sample Size; Strategy; Utilities
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2014.07.005

[Display omitted] •Anonymization of large-scale clinical codes allows for reliable genome–phenome analysis.•Across various repository sizes full EMR most reliable.•Preserves utility for finding genome–phenome associations. Electronic medical records (EMRs) data is increasingly incorporated into genome–phenome association studies. Investigators hope to share data, but there are concerns it may be “re-identified” through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome–phenome association studies under various conditions. We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome–phenome association study and compare the discoveries using the protected data and the original data through the correlation (r2) of the p-values of association significance. Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome–phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000–75,000). We observed a general trend of increasing r2 for larger data set sizes: r2=0.9481 for small-sized datasets, r2=0.9493 for moderately-sized datasets, r2=0.9934 for large-sized datasets. This research implies that regardless of the overall size of an institution’s data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.