Privacy-preserving construction of generalized linear mixed model for biomedical computation
Privacy-preserving construction of generalized linear mixed model for biomedical computation
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DOI：
10.1093/bioinformatics/btaa478
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发表时间：
2020-07-01
期刊：
影响因子：
5.8
通讯作者：
Tang, Haixu
中科院分区：
文献类型：
--
作者：
Zhu, Rui;Jiang, Chao;Tang, Haixu
动机：广义线性混合模型（GLMM）是广义线性模型（GLM）的扩展，其中线性预测器考虑了随机效应。鉴于其对多个随机变异来源的混合效应进行精确建模的能力，该方法已广泛应用于生物医学计算，例如全基因组关联研究（GWAS），旨在检测与人类疾病等表型显着相关的遗传变异。对多个机构的大量患者进行 GWAS 协作往往会因共享个人基因组和其他健康数据的隐私问题而受到阻碍。为了解决这些问题，我们在本文中提出了一种保护隐私的期望最大化（EM）算法，用于在输入数据分布到多个参与方且无法传输到中央服务器时协作构建 GLMM。我们假设数据在参与方之间进行水平划分：即每一方都拥有一个记录子集（包括固定效应变量的观测值及其相应的结果），并且对于所有记录，结果由同一组已知的固定效应和随机效应调节。结果：我们的协作 EM 算法在数学上等同于 GLMM 构建中常用的原始 EM 算法。该算法在模拟和真实人类基因组数据上进行测试时也能高效运行，因此可实际用于隐私保护的 GLMM 构建。我们在 R 中实现了协作 GLMM (cGLMM) 构建算法。数据通信是使用 rsocket 包实现的。
Motivation: The generalized linear mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predictor takes random effects into account. Given its power of precisely modeling the mixed effects from multiple sources of random variations, the method has been widely used in biomedical computation, for instance in the genome-wide association studies (GWASs) that aim to detect genetic variance significantly associated with phenotypes such as human diseases. Collaborative GWAS on large cohorts of patients across multiple institutions is often impeded by the privacy concerns of sharing personal genomic and other health data. To address such concerns, we present in this paper a privacy-preserving Expectation-Maximization (EM) algorithm to build GLMM collaboratively when input data are distributed to multiple participating parties and cannot be transferred to a central server. We assume that the data are horizontally partitioned among participating parties: i.e. each party holds a subset of records (including observational values of fixed effect variables and their corresponding outcome), and for all records, the outcome is regulated by the same set of known fixed effects and random effects.Results: Our collaborative EM algorithm is mathematically equivalent to the original EM algorithm commonly used in GLMM construction. The algorithm also runs efficiently when tested on simulated and real human genomic data, and thus can be practically used for privacy-preserving GLMM construction. We implemented the algorithm for collaborative GLMM (cGLMM) construction in R. The data communication was implemented using the rsocket package.