Genome-wide association studies (GWAS) have been widely used for identifying potential risk variants in various diseases. A statistically meaningful GWAS typically requires a large sample size to detect disease-associated single nucleotide polymorphisms (SNPs). However, a single institution usually only possesses a limited number of samples. Therefore, cross-institutional partnerships are required to increase sample size and statistical power. However, cross-institutional partnerships offer significant challenges, a major one being data privacy. For example, the privacy awareness of people, the impact of data privacy leakages and the privacy-related risks are becoming increasingly important, while there is no de-identification standard available to safeguard genomic data sharing. In this paper, we introduce a novel privacy-preserving federated GWAS framework (iPRIVATES). Equipped with privacy-preserving federated analysis, iPRIVATES enables multiple institutions to jointly perform GWAS analysis without leaking patient-level genotyping data. Only aggregated local statistics are exchanged within the study network. In addition, we evaluate the performance of iPRIVATES through both simulated data and a real-world application for identifying potential risk variants in ankylosing spondylitis (AS). The experimental results showed that the strongest signal of AS-associated SNPs reside mostly around the human leukocyte antigen (HLA) regions. The proposed iPRIVATES framework achieved equivalent results as traditional centralized implementation, demonstrating its great potential in driving collaborative genomic research for different diseases while preserving data privacy.

中文翻译：

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一种新型的隐私保护联合全基因组关联研究框架及其在识别强直性脊柱炎潜在风险变异中的应用。

全基因组关联研究 (GWAS) 已被广泛用于识别各种疾病的潜在风险变异。具有统计意义的 GWAS 通常需要大样本量来检测与疾病相关的单核苷酸多态性 (SNP)。然而，一个机构通常只拥有有限数量的样本。因此，需要跨机构合作来增加样本量和统计能力。然而，跨机构合作带来了重大挑战，其中一个主要挑战是数据隐私。例如，人们的隐私意识、数据隐私泄露的影响以及与隐私相关的风险变得越来越重要，而没有可用于保护基因组数据共享的去标识化标准。在本文中，我们介绍了一种新颖的隐私保护联合 GWAS 框架 (iPRIVATES)。配备隐私保护联合分析，iPRIVATES 使多个机构能够联合执行 GWAS 分析，而不会泄露患者级别的基因分型数据。在研究网络内仅交换汇总的本地统计数据。此外，我们通过模拟数据和实际应用程序评估 iPRIVATES 的性能，以识别强直性脊柱炎 (AS) 的潜在风险变异。实验结果表明，AS 相关 SNP 的最强信号主要存在于人类白细胞抗原 (HLA) 区域附近。提议的 iPRIVATES 框架取得了与传统集中式实施相同的结果，