ArticleBioinformatics (Oxford, England)2017
Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.Bioinformatics (Oxford, England) · 2025Article
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Identifying disease-causing mutations with privacy protection.Bioinformatics (Oxford, England) · 2021Article
- A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data.Computational and structural biotechnology journal · 2021Article
- Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations.Genomics, proteomics & bioinformatics · 2019Review
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7 authors.
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Abstract
motivationInappropriate disclosure of human genomes may put the privacy of study subjects and of their family members at risk. Existing privacy-preserving mechanisms for Genome-Wide Association Studies (GWAS) mainly focus on protecting individual information in case-control studies. Protecting privacy in family-based studies is more difficult. The transmission disequilibrium test (TDT) is a powerful family-based association test employed in many rare disease studies. It gathers information about families (most frequently involving parents, affected children and their siblings). It is important to develop privacy-preserving approaches to disclose TDT statistics with a guarantee that the risk of family 're-identification' stays below a pre-specified risk threshold. 'Re-identification' in this context means that an attacker can infer that the presence of a family in a study.
methodsIn the context of protecting family-level privacy, we developed and evaluated a suite of differentially private (DP) mechanisms for TDT. They include Laplace mechanisms based on the TDT test statistic, P-values, projected P-values and exponential mechanisms based on the TDT test statistic and the shortest Hamming distance (SHD) score.
resultsUsing simulation studies with a small cohort and a large one, we showed that that the exponential mechanism based on the SHD score preserves the highest utility and privacy among all proposed DP methods. We provide a guideline on applying our DP TDT in a real dataset in analyzing Kawasaki disease with 187 families and 906 SNPs. There are some limitations, including: (1) the performance of our implementation is slow for real-time results generation and (2) handling missing data is still challenging. AVAILABILITY AND IMPLEMENTATION: The software dpTDT is available in https://github.com/mwgrassgreen/dpTDT. CONTACT: mengw1@stanford.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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