Protecting Genetic Privacy through Risk Assessment

通过风险评估保护基因隐私

• 批准号: 7492263
• 负责人: Zhen Lin
• 金额: $ 15.47万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2010-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7492263
• 关键词: Adverse effects; Affect; Algorithms; Attention; Biological; Cataloging; Catalogs; Chromosome Mapping; Classification; Clinical; Computational algorithm; Computing Methodologies; Confidentiality; Data; Data Security; Data Set; Databases; Development; Diagnosis; Disclosure; Disease; Frequencies; Gene Frequency; General Population; Genes; Genetic; Genetic Privacy; Genetic Variation; Genome; Genomics; Genotype; Goals; Health; Human Genetics; Human Genome; Individual; Informatics; Institution; Internet; Investigation; Knowledge; Lead; Linkage Disequilibrium; Location; Maps; Medical; Medical Research; Methodology; Methods; Modeling; Molecular Profiling; Nature; Pattern; Performance; Pharmacogenetics; Pharmacogenomics; Play; Policy Maker; Polymorphism Analysis; Population; Privacy; Proteomics; Research; Research Personnel; Research Subjects; Risk; Risk Assessment; Role; Security; Sequence Analysis; Single Nucleotide Polymorphism; Site; Software Tools; Speed; Statistical Methods; Statistical Models; Structure; Testing; Time; United States National Institutes of Health; Variant; Work; cost; disease phenotype; improved; indexing; insight; knowledge base; novel; open source; programs; response; risk sharing; tool; usability; volunteer

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.

描述（由申请人提供）：免费访问研究数据对于促进科学发现至关重要。但是，隐私问题围绕公开共享生物医学数据。共享此类数据将处于危险中的身份和健康信息，这些个人自愿匿名发布其信息进行医学研究。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）。 SNP值得一提的研究。这些个人基因型的自由交换也对保护隐私和信息安全构成了困难的挑战。为了应对挑战，我提出了一项调查，以获取对SNP在公共生物医学数据库中传播的研究对象假定的隐私风险的准确评估。这些知识将为数据库隐私官和政策制定者提供保护研究主题隐私所需的信息。特别是，我将开发方法，以检查整个基因组中SNP之间的连锁不平衡（LD）模式，并将编译一个“风险图”，详细介绍最有可能威胁隐私的基因组位置。由于LD，一小部分TAG SNP可以捕获基因组中的大多数SNP信息内容。因此，它们是遗传学中有价值的工具，以减少将基因映射到疾病和表型所必需的努力。只需要检查标签SNP而不是整个侏儒。但是，由于这种属性，它们也是导致个人身份的高风险。因此，研究标签SNP与隐私之间的关系很重要。我以前已经开发了找到具有良好性能的标签SNP的方法。我建议改进这些标记方法，并开发新的标记方法，以汇编人类基因组中的TAG SNP的全面列表。我将评估标签SNP在公开个人中的能力。我还以前还建立了一种用于风险评估的初始概率模型。我建议进一步开发一个具有其位置和频率的标签SNP的知识库，以及使用概率风险评估模型和TAG SNP知识基础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最终的方法，以进行实时标签SNP检测和风险评估，并将分发用于开源开发的工具和软件。