SecureMA: protecting participant privacy in genetic association meta-analysis

Abstract

Motivation: Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.

Results: We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries.

Availability and implementation: Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService.

Fig. 1.

The SecureMA protocol (secure computation step). (a) The process begins when a scientist submits a meta-analysis study inquiry. Each data manager in the study submits encrypted local statistics (e.g. effect size and the inverse of its variance) to the Mediator for secure summation. (b) The Mediator then coordinates with one random data manager to securely divide the numerator by the denominator of the meta-analysis function. (c) The results of the meta-analysis are partially decrypted by the data managers, which are composed into the final full decryption of the meta-analysis P-value at the scientist’s computer

Fig. 2.

Protocol accuracy. The correlation plots correspond to (a) the P-values (secure protocol versus original publication) based on the 16 SNPs from eMERGE; (b) the P-values (secure protocol versus original publication) based on the 25 SNP-ethnicity pairs from PAGE (all SNPs annotated correspond to one ethnicity subpopulation, except for rs6548238’, which corresponds to another); and (c) the P-values (secure protocol versus standard non-secure meta-analysis) based on a controlled comparison of 100 SNPs from eMERGE)

Fig. 3.

Average running time of SecureMA, per SNP, as a function of the number of sites providing data (all times reported in seconds)

Fig. 4.

Impact of the scale-up factor on (a) computational accuracy; (b) running time efficiency. Results are based on the 10 SNPs from the eMERGE dataset (mean ± 1 SD)

Fig. 5.

The impact of the maximum exponent on (a) computational accuracy and (b) running time efficiency. The results are based on 10 SNPs from the eMERGE dataset (mean ± 1 SD)

Fig. 6.

The impact of the number of steps in the Taylor series [i.e., k in Equation (3)] on (a) computational accuracy and (b) running time efficiency. The results are based on 10 SNPs from the eMERGE dataset (mean ± 1 SD)