小柯机器人

美国哈佛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。他们对大型全基因组测序研究中的罕见变异体关联进行了强大、可扩展和资源高效的荟萃分析。相关研究成果2022年12月23日在线发表于《自然—遗传学》杂志上。

据介绍，全基因组测序/全外显子组测序（WGS/WES）研究的荟萃分析为收集大样本量以发现与复杂表型相关的罕见变异提供了一个有吸引力的解决方案。现有的罕见变异荟萃分析方法无法扩展到生物库规模的WGS数据。

研究人员提出了MetaSTAAR方案，这是一个用于大规模WGS/WES研究的强大且高效的罕见变体荟萃分析框架。MetaSTAAR考虑了相关性和种群结构，可以分析定量和二分性特征，并通过结合多个变异函数注释提高罕见变异测试的能力。通过对来自14项精准医学反式组学（TOPMed）计划研究的30138个不同样本的四种脂质特征进行荟萃分析，研究人员发现MetaSTAAR进行了大规模的罕见变异荟萃分析，并产生了与使用汇总数据相当的结果。

此外，研究人员还发现了几种与脂质性状相关的条件性显著的罕见变异。通过对TOPMed WGS数据和约200000个样本的英国生物样本库WES数据的荟萃分析，研究人员进一步证明，MetaSTAAR可扩展到生物库规模的群组结构。

附：英文原文

Title: Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Author: Li, Xihao, Quick, Corbin, Zhou, Hufeng, Gaynor, Sheila M., Liu, Yaowu, Chen, Han, Selvaraj, Margaret Sunitha, Sun, Ryan, Dey, Rounak, Arnett, Donna K., Bielak, Lawrence F., Bis, Joshua C., Blangero, John, Boerwinkle, Eric, Bowden, Donald W., Brody, Jennifer A., Cade, Brian E., Correa, Adolfo, Cupples, L. Adrienne, Curran, Joanne E., de Vries, Paul S., Duggirala, Ravindranath, Freedman, Barry I., Gring, Harald H. H., Guo, Xiuqing, Haessler, Jeffrey, Kalyani, Rita R., Kooperberg, Charles, Kral, Brian G., Lange, Leslie A., Manichaikul, Ani, Martin, Lisa W., McGarvey, Stephen T., 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., Sitlani, Colleen M., Smith, Jennifer A., Taylor, Kent D., Vasan, Ramachandran S., Willer, Cristen J., Wilson, James G., Yanek, Lisa R., Zhao, Wei, Rotter, Jerome I., Natarajan, Pradeep, Peloso, Gina M., Li, Zilin, Lin, Xihong

Issue&Volume: 2022-12-23

Abstract: Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

DOI: 10.1038/s41588-022-01225-6

Source: https://www.nature.com/articles/s41588-022-01225-6

Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
官方网址：https://www.nature.com/ng/
投稿链接：https://mts-ng.nature.com/cgi-bin/main.plex