Selvaraj Margaret Sunitha, Li Xihao, Li Zilin, Van Buren Eric, Haidermota Sara, Postupaka Darina, Hornsby Whitney, Bis Joshua C, Brody Jennifer A, Cade Brian E, Chung Ren-Hua, Curran Joanne E, Damrauer Scott M, de Las Fuentes Lisa, de Vries Paul S, Duggirala Ravindranath, Freedman Barry I, Graff MariaElisa, Guo Xiuqing, Hidalgo Bertha A, Hou Lifang, Irvin Ryan, Judy Renae, Kalyani Rita R, Kelly Tanika N, Konigsberg Iain R, Kral Brian G, Kwee Lydia Coulter, Levy Daniel, Li Changwei, Manichaikul Ani W, Martin Lisa Warsinger, Montasser May E, Morrison Alanna C, Naseri Take, North Kari E, O'Connell Jeffrey R, Palmer Nicholette D, Peyser Patricia A, Reiner Alex P, Shah Svati H, Smit Roelof A J, Smith Jennifer A, Taylor Kent D, Tiwari Hemant, Tsai Michael Y, Viali Satupa'itea, Wang Zhe, Wang Yuxuan, Zhao Wei, Arnett Donna K, Blangero John, Boerwinkle Eric, Bowden Donald W, Carlson Jenna C, Chen Yii-Der Ida, Ellinor Patrick T, Fornage Myriam, He Jiang, Heard-Costa Nancy, Kaplan Robert C, Kardia Sharon L R, Kooperberg Charles, Kraus William E, Lange Leslie A, Loos Ruth J F, Mitchell Braxton D, Psaty Bruce M, Rader Daniel J, Redline Susan, Rich Stephen S, Yanek Lisa R, Gibbs Richard, Gabriel Stacey, Viaud-Martinez Karine A, Dutcher Susan K, Germer Soren, Kim Ryan, Rotter Jerome I, Lin Xihong, Peloso Gina M, Natarajan Pradeep
Center for Genomic Medicine, Cardiovascular Research Center, , Massachusetts General Hospital Simches Research Center, 185 Cambridge Street, CPZN 5.238,, Boston, MA, 02114, USA.
Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, , Cambridge, MA , 02141, USA.
Genome Biol. 2025 Sep 9;26(1):273. doi: 10.1186/s13059-025-03698-0.
Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability.
Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246 K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86 K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate.
This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.
全基因组序列数据集所提供的罕见基因变异对人类性状的贡献相对较少被探索。对测序数据进行荟萃分析具有优势，它能整合来自不同队列的更大样本量，从而增加发现复杂性状新见解的可能性。此外，全基因组罕见变异关联测试中的新兴方法进一步提高了效能和可解释性。
在此，我们针对冠状动脉疾病的治疗靶点低密度脂蛋白胆固醇（LDL-C）进行了最大规模的全基因组测序荟萃分析，分析了来自24.6万名参与者的数据，并整合了英国生物银行和精准医学全基因组学（TOPMed）计划中的12.3亿个变异。我们识别出众多与LDL-C相关的罕见编码和非编码基因关联，并在“我们所有人”项目的8.6万名参与者中进行了验证。我们的发现基于单变异分析、罕见编码和非编码变异聚集测试以及滑动窗口方法。通过这种全面分析，我们识别出704个新的单变异关联、25个新的罕见编码变异聚集、28个新的罕见非编码变异聚集以及1个新的滑动窗口聚集。
本研究为来自不同人群组的大规模全基因组序列关联分析提供了一个荟萃分析框架，产生了新的罕见非编码变异关联。
