美国哈佛大学陈曾熙公共卫生学院Xihong Lin和Zilin Li共同合作近期取得重要工作进展，他们研究开发了一个检测大规模全基因组测序研究的非编码罕见变异关联框架。该项研究成果2022年10月27日在线发表于《自然—方法学》杂志上。

研究人员提出了一个计算效率高且稳健的非编码RV关联检测框架STAARpipeline，用于自动注释全基因组测序研究并进行灵活的非编码RV关联分析，包括以基因为中心的分析和基于固定窗口和动态窗口的非基因中心分析。在以基因为中心的分析中，STAARpipeline使用STAAR根据基因的功能类别对非编码变体进行分组，并纳入多种功能注释。在以非基因为中心的分析中，STAARpipeline使用SCANG-STAAR来纳入动态窗口大小和多种功能注释。

研究人员应用STAARpipeline在21,015个来自Trans-Omics for Precision Medicine（TOPMed）计划的发现样本中，识别与四种脂质性状相关的非编码RV集，并在另外9,123个TOPMed样本中复制了其中的几个样本。研究人员还分析了五种非脂质TOPMed性状。

据了解，大规模全基因组测序研究使分析非编码罕见变异（RV）与复杂人类疾病和性状的关系成为可能。变异集分析是研究RV关联的有效方法。然而，现有的方法对非编码基因组的分析能力有限。

附：英文原文

Title: A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Author: Li, Zilin, Li, Xihao, Zhou, Hufeng, Gaynor, Sheila M., Selvaraj, Margaret Sunitha, Arapoglou, Theodore, Quick, Corbin, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K., Auer, Paul L., Bielak, Lawrence F., Bis, Joshua C., Blackwell, Thomas W., Blangero, John, Boerwinkle, Eric, Bowden, Donald W., Brody, Jennifer A., Cade, Brian E., Conomos, Matthew P., Correa, Adolfo, Cupples, L. Adrienne, Curran, Joanne E., de Vries, Paul S., Duggirala, Ravindranath, Franceschini, Nora, Freedman, Barry I., Gring, Harald H. H., Guo, Xiuqing, Kalyani, Rita R., Kooperberg, Charles, Kral, Brian G., Lange, Leslie A., Lin, Bridget M., Manichaikul, Ani, Manning, Alisa K., Martin, Lisa W., Mathias, Rasika A., Meigs, James B., Mitchell, Braxton D., Montasser, May E., Morrison, Alanna C., Naseri, Take, OConnell, Jeffrey R., Palmer, Nicholette D., Peyser, Patricia A., Psaty, Bruce M., Raffield, Laura M., Redline, Susan, Reiner, Alexander P., Reupena, Muagututia Sefuiva, Rice, Kenneth M., Rich, Stephen S., Smith, Jennifer A., Taylor, Kent D., Taub, Margaret A., Vasan, Ramachandran S., Weeks, Daniel E., Wilson, James G., Yanek, Lisa R., Zhao, Wei, Rotter, Jerome I., Willer, Cristen J.

Issue&Volume: 2022-10-27

Abstract: Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

DOI: 10.1038/s41592-022-01640-x

Source: https://www.nature.com/articles/s41592-022-01640-x