Privacy-Preserving Data Exploration in Genome-Wide Association Studies

By Aaron Johnson, Vitaly Shmatikov. 2013

212 citations recorded by the source corpus.

Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers' access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.

Subfields: Artificial Intelligence, Molecular Biology, Public Health, Environmental and Occupational Health

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

In Public Health, Environmental and Occupational Health

Citations in the corpus

The power of synergy in differential privacy: Combining a small curator with local randomizers (2019, background): “Solving the problem while ensuring privacy is particularly challenging when the feature dimension is large compared to the number of available samples, which is the case for GWAS [13, 3, 39, 57].”
Understanding Membership Inferences on Well-Generalized Learning Models (2018, background): “ presence of high-dimensional data, which is particularly vulnerable to our attack, perturbation signiﬁcantly undermines the utility of the model before its privacy risk can be effectively controlled [21]. As an example, a recent study reports that a differentially-private stochastic gradient descen”
INSPECTRE: Privately Estimating the Unseen (2018, background): “This realization has motivated a surge of interest in developing data sharing techniques with an explicit focus on maintaining privacy of the data [JS13, USF13, YFSU14, SSB16].”
Differentially private model selection with penalized and constrained likelihood (2016, methodology): “, 2014; Karwa and Slavković, 2016), non-parametric density estimation (Wasserman and Zhou, 2010), hypothesis testing (Fienberg et al., 2011; Johnson and Shmatikov, 2013; Uhler et al., 2013; Yu et al., 2014; Karwa et al., 2014; Solea, 2014; Dwork et al., 2015; Sheffet, 2015; Wang et al., 2015; Gaboar”
Locally Private Mean Estimation: Z-test and Tight Confidence Intervals (2018, background): “Johnson and Shmatikov (2013), Uhler et al. (2013) and Yu et al. (2014) focused on the Pearson χ2-test, showing that the noise added by differential privacy vanishes asymptotically as the number of datapoints goes to infinity, and propose a private χ2-based test which they study empirically.”
Membership Privacy in MicroRNA-based Studies (2016, background): “Johnson and Shmatikov propose and implement algorithms for accurate and diﬀer-entially private computation of various statistics of interest, such as the location of the most signiﬁcant genomic variants, or the p -values of statistical tests between a given variant and the associated diseases [20].”
Improved Differentially Private Analysis of Variance (2019, background): “Many private chi-squared tests have been specifically motivated by genome-wide association studies (GWAS) [9, 11, 20].”
A New Class of Private Chi-Square Tests (2016, background): “The first group adds appropriately scaled noise to the sampled data (or histogram of data) to ensure differential privacy and uses existing classical hypothesis tests, disregarding the additional noise distribution [14].”
Differentially Private Nonparametric Hypothesis Testing (2019, background): “Several other papers, though they make asymptotic arguments on the uniformity of their p-values, have developed frameworks for private chi-squared tests specifically for the intent of genome-wide association study (GWAS) data [11, 15, 31].”
Machine learning and genomics: precision medicine versus patient privacy (2018, background): “Nevertheless, recent work [40, 41, 42] has shown how to implement differential privacy for GWAS, making it possible for users to query a genetic database for the differentially private top k most associated SNPs.”
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing (2016, background): “An interesting direction for future work would be to apply the distance-score mechanism introduced by (Johnson and Shmatikov, 2013) and later improved by (Yu et al., 2014; Yu and Ji, 2014; Simmons and Berger, 2016), to achieve a target level of signiﬁcance and high power in hypothesis testing for…”
Max-Information, Differential Privacy, and Post-selection Hypothesis Testing (2016, background): “I ∞,P (A, n) ≤ log(e) · ( (2)n/2 + √ n ln(2/β)/2 ) This connection is powerful, because there are a vast collection of data analyses for which we have differentially private algorithms— including a growing literature on differentially private hypothesis tests [12, 13, 14, 15, 16, 17, 18, 19].”
Local Private Hypothesis Testing: Chi-Square Tests (2017, background): “(2008), there are several works that deal with private statistical inference with GWAS data, (Uhler et al., 2013; Yu et al., 2014; Johnson and Shmatikov, 2013).”
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations (2016, unknown): “, 2006; Dwork, 2011) (Box 1) has been proposed as one promising solution to the privacy conundrum (Jiang et al., 2014; Yu et al., 2014a, 2014b; Johnson and Shmatikov, 2013; Uhlerop et al., 2013; Yu and Ji, 2014; Chen et al., 2014; Zhao et al., 2015; Zhang et al., 2013; Tramer et al., 2015).”