Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
复制标题
用于检测 GWAS 数据库中多 SNP 关联的差分隐私 Logistic 回归
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Fienberg
中科院分区：
文献类型：
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作者：
Fei Yu;Michal Rybár;Caroline Uhler;S. Fienberg
在霍默等人发表了对全基因组关联研究（GWAS）数据的攻击之后，已经对开发用于以隐私保护方式发布GWAS数据的方法给予了相当大的关注。在这里，我们开发了一种端到端的差分私有方法，用于解决具有凸罚函数的回归问题，并通过交叉验证选择罚参数。特别是，我们专注于弹性网络正则化的惩罚逻辑回归，这是一种广泛用于GWAS分析以识别致病基因的方法。我们展示了如何将弹性网络正则化惩罚逻辑回归的差分私有程序应用于GWAS数据的分析并评估我们方法的性能。
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way. Here, we develop an end-to-end differentially private method for solving regression problems with convex penalty functions and selecting the penalty parameters by cross-validation. In particular, we focus on penalized logistic regression with elastic-net regularization, a method widely used to in GWAS analyses to identify disease-causing genes. We show how a differentially private procedure for penalized logistic regression with elastic-net regularization can be applied to the analysis of GWAS data and evaluate our method’s performance.