发表机构
Argonne National Laboratory; University of Massachusetts Amherst（阿贡国家实验室; 马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于APPFL框架和GA4GH TES，开发了含HiveWatch监控工具的联邦GWAS荟萃分析流水线，通过5位点模拟验证其可在不集中基因型数据的前提下实现隐私保护的国际GWAS荟萃分析。
AI 中文摘要
全基因组关联研究（GWAS）从大规模、祖先多样的队列中获得统计效力，但隐私法规和数据驻留约束常常阻止基因组数据在机构或国家边界间集中汇集。我们提出一种基于APPFL框架的隐私保护联邦GWAS荟萃分析流水线，其中每个位点计算局部GWAS汇总统计量，仅传输聚合结果，从不传输个体水平基因型。分析通过全球基因组与健康联盟（GA4GH）任务执行服务（TES）端点的全球网络执行，这使得计算可迁移至数据端而非相反方向。服务器执行逆方差加权固定效应荟萃分析并将聚合结果返回至所有位点，而我们开发的地理可观测性工具包HiveWatch提供分布式任务执行的实时监控。在针对2型糖尿病和体重指数的5位点模拟中，使用100,000名合成个体和约240,000个变异，联邦荟萃分析复现了汇集分析预期的关联信号，且无需集中任何基因型数据，表明基于标准的任务执行和联邦学习可为国际GWAS荟萃分析提供实用的隐私保护基础设施。
英文摘要
Genome-wide association studies (GWAS) gain statistical power from large, ancestrally diverse cohorts, but privacy regulations and data-residency constraints often prevent genomic data from being centrally pooled across institutional or national borders. We present a privacy-preserving federated GWAS meta-analysis pipeline built on the APPFL framework, in which each site computes local GWAS summary statistics and transmits only aggregate results, never individual-level genotypes. Analysis is executed through a global network of Global Alliance for Genomics and Health (GA4GH) Task Execution Service (TES) endpoints, which allows computation to move to the data rather than the reverse. The server performs inverse-variance-weighted fixed-effect meta-analysis and returns aggregated results to all sites, while HiveWatch, our developed geographic observability toolkit, provides real-time monitoring of distributed task execution. In a five-site simulation over 100,000 synthetic individuals and roughly 240,000 variants for Type 2 Diabetes and Body Mass Index, the federated meta-analysis reproduces the association signal expected from a pooled analysis without centralizing any genotype data, showing that standards-based task execution and federated learning enables a practical privacy-preserving infrastructure for international GWAS meta-analysis.
CommentsThis manuscript is accepted as a poster for 2026 IEEE eScience conference (https://www.escience-conference.org/2026/)
