近日，美国密歇根大学Xiang Zhou团队发现，MESuSiE可对全基因组关联研究中的因果变异进行可扩展的、功能强大的多巢精细图谱绘制。这一研究成果于2024年1月2日在线发表在国际学术期刊《自然—遗传学》上。

研究人员提出了多祖先单效应模型总和（MESuSiE），这是一种概率性多祖先精细作图方法，通过利用跨祖先的关联信息来提高精细作图的准确性和分辨率。MESuSiE使用摘要统计作为输入，考虑了在不同祖先中观察到的多种多样的连锁不平衡模式，明确地建立了共享和祖先特异性因果SNP模型，并依靠变异推理算法实现了可扩展计算。

研究人员利用欧洲和非洲样本对四种脂质性状进行了全面模拟和多祖先精细图谱分析，并评估了MESuSiE的性能。在真实数据中，与现有方法相比，MESuSiE的精细作图分辨率提高了19.0%到72.0%，速度快了一个数量级，并能捕捉和分类共有的和祖先特异的因果信号，增强了功能富集。

研究人员表示，全基因组关联研究中的精细图谱法试图从感兴趣的局部基因组区域中的一组候选SNP中识别因果SNP，通常一次只在一个遗传祖先中进行。

附：英文原文

Title: MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies

Author: Gao, Boran, Zhou, Xiang

Issue&Volume: 2024-01-02

Abstract: Fine-mapping in genome-wide association studies attempts to identify causal SNPs from a set of candidate SNPs in a local genomic region of interest and is commonly performed in one genetic ancestry at a time. Here, we present multi-ancestry sum of the single effects model (MESuSiE), a probabilistic multi-ancestry fine-mapping method, to improve the accuracy and resolution of fine-mapping by leveraging association information across ancestries. MESuSiE uses summary statistics as input, accounts for the diverse linkage disequilibrium pattern observed in different ancestries, explicitly models both shared and ancestry-specific causal SNPs, and relies on a variational inference algorithm for scalable computation. We evaluated the performance of MESuSiE through comprehensive simulations and multi-ancestry fine-mapping of four lipid traits with both European and African samples. In the real data, MESuSiE improves fine-mapping resolution by 19.0% to 72.0% compared to existing approaches, is an order of magnitude faster, and captures and categorizes shared and ancestry-specific causal signals with enhanced functional enrichment.

DOI: 10.1038/s41588-023-01604-7

Source: https://www.nature.com/articles/s41588-023-01604-7