在异质人群中实现隐私保护GWAS
Enabling Privacy-Preserving GWAS in Heterogeneous Human Populations
摘要：
临床中基因分型的预计增加和大型基因组数据库的兴起，使得使用患者医疗数据以比以往更大的规模和更低的成本进行全基因组关联研究（GWAS）成为可能。然而，由于隐私问题，这些数据的访问仅限于少数受信任的个人，大大降低了其对生物医学研究的影响。隐私保护方法已被建议作为一种允许更多人访问这些宝贵数据的方式，同时保护患者。特别是，已经有越来越多的兴趣在应用的概念，差分隐私GWAS的结果。不幸的是，以前用于执行差异私有GWAS的方法是基于相当简单的统计数据的，这些统计数据具有一些主要的局限性。特别是，他们不正确的人口分层，一个主要的问题时，处理遗传多样性的人口在现代GWAS。为了解决这个问题，我们引入了一种新的计算框架，用于执行GWAS，从差异隐私中定制想法，以保护私人表型信息，同时纠正人口分层。该框架允许我们基于两个最常用的GWAS统计数据生成隐私保护GWAS结果：EIGENSTRAT和基于线性混合模型（LMM）的统计数据。我们在模拟和真实的GWAS数据集上测试了我们的差异私有统计数据PrivSTRAT和PrivLMM，发现它们能够在返回有意义的GWAS结果的同时保护隐私。
英文摘要：
The projected increase of genotyping in the clinic and the rise of large genomic databases has led to the possibility of using patient medical data to perform genomewide association studies (GWAS) on a larger scale and at a lower cost than ever before. Due to privacy concerns, however, access to this data is limited to a few trusted individuals, greatly reducing its impact on biomedical research. Privacy preserving methods have been suggested as a way of allowing more people access to this precious data while protecting patients. In particular, there has been growing interest in applying the concept of differential privacy to GWAS results. Unfortunately, previous approaches for performing differentially private GWAS are based on rather simple statistics that have some major limitations. In particular, they do not correct for population stratification, a major issue when dealing with the genetically diverse populations present in modern GWAS. To address this concern we introduce a novel computational framework for performing GWAS that tailors ideas from differential privacy to protect private phenotype information, while at the same time correcting for population stratification. This framework allows us to produce privacy preserving GWAS results based on two of the most commonly used GWAS statistics: EIGENSTRAT and linear mixed model (LMM) based statistics. We test our differentially private statistics, PrivSTRAT and PrivLMM, on both simulated and real GWAS datasets and find that they are able to protect privacy while returning meaningful GWAS results.
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