2 research outputs found
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations
The projected increase of genotyping in the clinic and the rise of large
genomic databases has led to the possibility of using patient medical data to
perform genomewide association studies (GWAS) on a larger scale and at a lower
cost than ever before. Due to privacy concerns, however, access to this data is
limited to a few trusted individuals, greatly reducing its impact on biomedical
research. Privacy preserving methods have been suggested as a way of allowing
more people access to this precious data while protecting patients. In
particular, there has been growing interest in applying the concept of
differential privacy to GWAS results. Unfortunately, previous approaches for
performing differentially private GWAS are based on rather simple statistics
that have some major limitations. In particular, they do not correct for
population stratification, a major issue when dealing with the genetically
diverse populations present in modern GWAS. To address this concern we
introduce a novel computational framework for performing GWAS that tailors
ideas from differential privacy to protect private phenotype information, while
at the same time correcting for population stratification. This framework
allows us to produce privacy preserving GWAS results based on two of the most
commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based
statistics. We test our differentially private statistics, PrivSTRAT and
PrivLMM, on both simulated and real GWAS datasets and find that they are able
to protect privacy while returning meaningful GWAS results.Comment: To be presented at RECOMB 201