TRIPODS+X:RES: Collaborative Research:Privacy-Preserving Genomic Data Analysis

现代医学的很大一部分是由基因组数据驱动的，基因组数据集的规模和复杂性正在迅速增加。自然，任何对人类基因组数据的使用都会引发严重的隐私担忧。这是因为，用看似无关紧要的问题（如“你是否包含有突变X的基因组？”）查询多个基因组数据库的能力，足以确定一个人的基因组是否存在于数据库中。这样的重新识别攻击提出了一个密切相关的问题：人们能否对基因组数据实施隐私保护，以便有意义的数据分析仍然是可能的，但这样的攻击变得不可能？这个项目的主要思想是通过对数据做出和利用统计假设来实现这一目标，这样如果假设是错误的，数据分析将受到影响，但隐私不会受到影响。该项目还将为研究生班提供数据隐私、机器学习和基因组学交叉领域的课程材料。该项目考虑了在基因组数据背景下保护隐私的三个主要研究问题。所使用的隐私概念是差分隐私，可以证明它可以防止再次识别攻击，并且在学术界和工业界都得到了大规模的采用。第一个研究问题是在保护个体隐私的同时估计等位基因频率和连锁不平衡。给定一组人类基因组，等位基因频率估计的目的是估计染色体中不同位置的不同突变的频率。连锁不平衡是等位基因对独立性的偏离。第二个问题是单倍型抽样。单倍型对应于一组遗传变异（通常延伸到多个基因上），这些变异倾向于一起遗传。在单倍型采样中，目标是在给定人类基因组数据集的情况下生成合成单倍型，同时尊重这些遗传变异背后的生物学。最后，该项目旨在估计乳腺癌基因的致病变异。已知brca1和brca2基因的变异是乳腺癌的致病性。然而，许多变异仍未被分类为致病性/非致病性，而是VUSs -未知意义的变异。目标是开发一个隐私保护系统，从单个测序基因中收集有关VUSs的统计数据。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Much of modern day medicine is driven by genomic data, with the size and complexity of genomic datasets increasing at a rapid pace. Naturally, any use of human genomic data raises grave privacy concerns. This is because the power to query multiple genomic databases with seemingly innocuous questions such as "Do you contain any genome that has mutation X?" is enough to determine whether an individual's genome is present in the databases. Such re-identification attacks have raised a germane question: can one implement privacy protection for genomic data so that meaningful data analysis remains possible, but attacks such as these become impossible? The main idea of this project is to achieve this goal by making and exploiting statistical assumptions about the data, such that if the assumptions are false, data analysis will suffer but privacy will not. The project will also generate curricular material for a graduate class at the intersection of data privacy, machine learning, and genomics.The project considers three major research questions on preserving privacy in the context of genomic data. The notion of privacy used is differential privacy, which provably protects against re-identification attacks, and has found large-scale adoption in both academia and industry. The first research question is the estimation of allele frequencies, and of linkage disequilibrium, while preserving individual privacy. Given a set of human genomes, the objective of allele frequency estimation is to estimate the frequency of the different mutations across various locations in the chromosome. Linkage disequilibrium is the deviation from independence for pairs of alleles. The second question is haplotype sampling. Haplotypes correspond to sets of genetic variations (typically extending over multiple genes), that tend to be inherited together. In haplotype sampling, the objective is to generate synthetic haplotypes given a data set of human genomes, while respecting biology behind these genetic variations. Finally, the project aims to estimate pathogenic variants of breast cancer genes. Variants of the BRCA 1 and 2 genes are known to be pathogenic for breast cancer. However, a lot of the variants are still not classified as pathogenic / non-pathogenic and are VUSs - Variants of Unknown Significance. The objective is to develop a privacy-preserving system to gather statistics about the VUSs from individually sequenced genes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.