Article
A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
Nature methods - 1 Dec 2022
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, Göring 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, O'Connell Jeffrey R, Palmer Nicholette D, Peyser Patricia A, Psaty Bruce M, Raffield Laura M, Redline Susan, Reiner Alexander P, Reupena Muagututi'a 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, Natarajan Pradeep, Peloso Gina M, Lin Xihong
Abstract excerpt
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...
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