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

现代医学很大程度上是由基因组数据驱动的，基因组数据集的大小和复杂性正在快速增长。自然，任何对人类基因组数据的使用都会引发严重的隐私问题。这是因为可以用看似无关紧要的问题来查询多个基因组数据库，比如“你有没有包含有X突变的基因组？”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紧要的问题：人们能否对基因组数据实施隐私保护，以便仍然可以进行有意义的数据分析，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进行统计假设并利用这些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。该项目还将为数据隐私、机器学习和基因组学交叉的研究生班生成课程材料。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。使用的隐私概念是差异隐私，它被证明可以防止重新识别攻击，并在学术界和工业界都得到了大规模采用。第一个研究问题是等位基因频率和连锁不平衡的估计，同时保护个人隐私。给定一组人类基因组，等位基因频率估计的目标是估计染色体上不同位置的不同突变的频率。连锁不平衡是指一对等位基因偏离独立性。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)，这些变异往往是一起遗传的。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。BRCA1和BRCA2基因的变异被认为是乳腺癌的致病因素。然而，许多变异体仍然没有被归类为致病/非致病，而是VUS变异体，其意义未知。其目标是开发一个隐私保护系统，从单独测序的基因中收集有关VUS的统计数据。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.