首尔大学Seunggeun Lee研究团队取得一项新突破。他们提出了使用Meta-SAIGE进行可扩展和准确的罕见变异荟萃分析。2025年11月20日出版的《自然—遗传学》发表了这项成果。

在这里，该课题组引入Meta-SAIGE，这是一种可扩展的罕见变异荟萃分析方法，可以准确估计零分布以控制I型错误，并重用跨表型的连锁不平衡矩阵，以提高全表型分析的计算效率。模拟UK Biobank全外显子组测序数据显示Meta-SAIGE有效控制I误差，达到与使用SAIGE-GENE+的合并个人水平分析相当的能力。将Meta-SAIGE应用于UK Biobank和All of Us中的83种低患病率表型，确定了237种基因性状关联。值得注意的是，这些关联中有80个单独在两个数据集中都不显著，强调了他们的荟萃分析的力量。

据悉，荟萃分析通过结合多个队列的汇总统计数据来增强罕见变异关联检验的有效性。然而，现有的方法往往无法控制类型对于低流行率的二元特征，误差为1，并且计算密集。

附：英文原文

Title: Scalable and accurate rare variant meta-analysis with Meta-SAIGE

Author: Park, Eunjae, Nam, Kisung, Jeong, Seokho, Keat, Karl, Kim, Dokyoon, Bansal, Vikas, Zhou, Wei, Lee, Seunggeun

Issue&Volume: 2025-11-20

Abstract: Meta-analysis enhances the power of rare variant association tests by combining summary statistics across several cohorts. However, existing methods often fail to control typeI error for low-prevalence binary traits and are computationally intensive. Here we introduce Meta-SAIGE—a scalable method for rare variant meta-analysis that accurately estimates the null distribution to control typeI error and reuses the linkage disequilibrium matrix across phenotypes to boost computational efficiency in phenome-wide analyses. Simulations using UK Biobank whole-exome sequencing data show that Meta-SAIGE effectively controls typeI error and achieves power comparable to pooled individual-level analysis with SAIGE-GENE+. Applying Meta-SAIGE to 83 low-prevalence phenotypes in UK Biobank and All of Us whole-exome sequencing data identified 237 gene–trait associations. Notably, 80 of these associations were not significant in either dataset alone, underscoring the power of our meta-analysis.

DOI: 10.1038/s41588-025-02403-y

Source: https://www.nature.com/articles/s41588-025-02403-y