Dataset Information

On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy.

ABSTRACT: Recent advances in genome-scale, system-level measurements of quantitative phenotypes (transcriptome, metabolome, and proteome) promise to yield unprecedented biological insights. In this environment, broad dissemination of results from genome-wide association studies (GWASs) or deep-sequencing efforts is highly desirable. However, summary results from case-control studies (allele frequencies) have been withdrawn from public access because it has been shown that they can be used for inferring participation in a study if the individual's genotype is available. A natural question that follows is how much private information is contained in summary results from quantitative trait GWAS such as regression coefficients or p values. We show that regression coefficients for many SNPs can reveal the person's participation and for participants his or her phenotype with high accuracy. Our power calculations show that regression coefficients contain as much information on individuals as allele frequencies do, if the person's phenotype is rather extreme or if multiple phenotypes are available as has been increasingly facilitated by the use of multiple-omics data sets. These findings emphasize the need to devise a mechanism that allows data sharing that will facilitate scientific progress without sacrificing privacy protection.

SUBMITTER: Im HK

PROVIDER: S-EPMC3322234 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy.

Im Hae Kyung HK Gamazon Eric R ER Nicolae Dan L DL Cox Nancy J NJ

American journal of human genetics 20120328 4

Recent advances in genome-scale, system-level measurements of quantitative phenotypes (transcriptome, metabolome, and proteome) promise to yield unprecedented biological insights. In this environment, broad dissemination of results from genome-wide association studies (GWASs) or deep-sequencing efforts is highly desirable. However, summary results from case-control studies (allele frequencies) have been withdrawn from public access because it has been shown that they can be used for inferring pa ...[more]

PMID: 22463877

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Project description:BackgroundTwitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform.ObjectiveThis study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter.MethodsData were collected from 329 Twitter users in the United States using a web-based survey.ResultsResults suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (β=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (β=-.38; P<.001) and specific health information (β=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter.ConclusionsThe results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Project description:Genomic data is increasingly collected by a wide array of organizations. As such, there is a growing demand to make summary information about such collections available more widely. However, over the past decade, a series of investigations have shown that attacks, rooted in statistical inference methods, can be applied to discern the presence of a known individual's DNA sequence in the pool of subjects. Recently, it was shown that the Beacon Project of the Global Alliance for Genomics and Health, a web service for querying about the presence (or absence) of a specific allele, was vulnerable. The Integrating Data for Analysis, Anonymization, and Sharing (iDASH) Center modeled a track in their third Privacy Protection Challenge on how to mitigate the Beacon vulnerability. We developed the winning solution for this track.This paper describes our computational method to optimize the tradeoff between the utility and the privacy of the Beacon service. We generalize the genomic data sharing problem beyond that which was introduced in the iDASH Challenge to be more representative of real world scenarios to allow for a more comprehensive evaluation. We then conduct a sensitivity analysis of our method with respect to several state-of-the-art methods using a dataset of 400,000 positions in Chromosome 10 for 500 individuals from Phase 3 of the 1000 Genomes Project. All methods are evaluated for utility, privacy and efficiency.Our method achieves better performance than all state-of-the-art methods, irrespective of how key factors (e.g., the allele frequency in the population, the size of the pool and utility weights) change from the original parameters of the problem. We further illustrate that it is possible for our method to exhibit subpar performance under special cases of allele query sequences. However, we show our method can be extended to address this issue when the query sequence is fixed and known a priori to the data custodian, so that they may plan stage their responses accordingly.This research shows that it is possible to thwart the attack on Beacon services, without substantially altering the utility of the system, using computational methods. The method we initially developed is limited by the design of the scenario and evaluation protocol for the iDASH Challenge; however, it can be improved by allowing the data custodian to act in a staged manner.

Dataset Information

On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy.

Publications

On sharing quantitative trait GWAS results in an era of multiple-omics data and the limits of genomic privacy.

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