Protecting Genetic Privacy through Risk Assessment

描述（由申请人提供）：免费获取研究数据对促进科学发现至关重要。然而，隐私问题围绕着公开分享生物医学数据。共享这类数据使自愿匿名公布其信息用于医学研究的个人的身份和健康信息面临风险。通过高通量方法快速生成的一种基因组序列数据是单核苷酸多态性（snp）。SNPs值得大量的研究。这些个人基因型的自由交换也给保护隐私和信息安全带来了困难的挑战。为了应对这些挑战，我建议进行一项调查，以获得对snp在公共生物医学数据库中传播的研究对象所承担的隐私风险的准确评估。这些知识将为数据库隐私官员和政策制定者提供他们在保护研究对象隐私方面所需的信息。特别是，我将开发方法来检查整个基因组中snp之间的连锁不平衡（LD）模式，我将编制一个“风险图”，详细说明最有可能威胁隐私的基因组位置。由于LD，一小组标签SNP可以捕获基因组中大部分SNP信息内容。因此，它们是遗传学中有价值的工具，可以减少将基因映射到疾病和表型所需的努力。只需要检查标签snp，而不是整个侏儒。然而，由于这一特性，它们也是高风险的，可能导致个人身份识别。因此，研究标签snp与隐私之间的关系非常重要。我以前开发了一些方法来寻找具有良好性能的标签snp。我建议改进这些标记方法，并开发新的标记方法，以编制一个全面的人类基因组标记snp列表。我将评估标签snp在披露个体方面的能力。我之前也建立了一个用于风险评估的初始概率模型。我建议进一步开发标签SNP的位置和频率知识库，以及利用概率风险评估模型和标签SNP知识库的自动风险评估工具。我将通过应用于现有的公共基因组数据库来评估风险评估工具的可用性和功能。我还将在网络上提供结果方法，用于实时标记SNP检测和风险评估，并将分发用于开源开发的工具和软件。

DESCRIPTION (provided by applicant): Free access to research data is vital to promote scientific discovery. However, privacy concerns revolve around publicly sharing biomedical data. Sharing such data puts at risk the identity and health information of individuals who have volunteered to anonymously release their information for medical research. One type of genomic sequence data that are generated rapidly by high-throughput methods is single nucleotide polymorphisms (SNPs). SNPs merit tremendous research attentions. Free exchange of these personal genotypes also poses difficult challenges for protecting privacy and information security. To deal with the challenges, I propose an investigation to acquire an accurate assessment of the privacy risk assumed by research subjects whose SNPs are disseminated in public biomedical databases. This knowledge will provide database privacy officers and policy makers the information that they need in protecting privacy of research subjects. In particular, I will develop methods to examine linkage disequilibrium (LD) patterns among SNPs throughout the genome, and I will compile a "risk map" detailing the genomic locations most likely to threaten privacy. Because of LD, a small set of tag SNPs can capture the majority of SNP information content in the genome. They are thus valuable tools in genetics to reduce the effort necessary to map genes to diseases and phenotypes. Only the tag SNPs, rather than the entire gnome, needs to be examined. However, because of that very attribute, they are also the high-risk ones that would lead to individual identifications. Therefore, it is important to study the relationship between tag SNPs and privacy. I have previously developed methods to find tag SNPs with good performance. I propose improving these tagging methods as well as developing new ones to compile a comprehensive list of tag SNPs in the human genome. I will evaluate the ability of tag SNPs in disclosing individuals. I have also previously established an initial probabilistic model for the risk assessment. I propose to further develop a knowledgebase of tag SNPs with their locations and frequencies, and an automatic risk assessment tool that utilizes the probabilistic risk assessment model and the tag SNP knowledgebase. I will evaluate the usability and functionality of the risk assessment tool by applying to existing public genomic databases. I will also make the resulting methods available on the web for real-time tag SNP detection and risk assessment and will distribute the tools and software for open source development.