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

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 HH

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

NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

TOPMed Lipids Working Group

Rotter, Jerome I

Willer, Cristen J

Natarajan, Pradeep

Peloso, Gina M

Lin, Xihong

2024-02-01T17:27:05Z

2024-02-01T17:27:05Z

2022-12

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.

10.1038/s41592-022-01640-x

1548-7091

1548-7105

https://hdl.handle.net/10161/30072

eng

Springer Science and Business Media LLC

Nature methods

10.1038/s41592-022-01640-x

https://creativecommons.org/licenses/by-nc/4.0

NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium

TOPMed Lipids Working Group

Humans

Phenotype

Genome

Genetic Variation

Genome-Wide Association Study

Whole Genome Sequencing

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

Journal article

1599

1611

12

Duke

Sanford School of Public Policy

School of Medicine

Basic Science Departments

Clinical Science Departments

Institutes and Centers

Biostatistics & Bioinformatics

Molecular Genetics and Microbiology

Medicine

Pathology

Medicine, Hematology

Medicine, Nephrology

Duke Cancer Institute

Institutes and Provost's Academic Units

University Institutes and Centers

Duke Global Health Institute

Duke Institute for Brain Sciences

Duke Molecular Physiology Institute

Center for Child and Family Policy

Published

19