A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
Technology
610
Biochemistry
Medical and Health Sciences
03 medical and health sciences
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Medicine and Health Sciences
Genetics
2.1 Biological and endogenous factors
Humans
Other Genetics and Genomics
Precision Medicine
Molecular Biology
0303 health sciences
Genome
Whole Genome Sequencing
Human Genome
Life Sciences
Genetic Variation
Genetics and Genomics
Cell Biology
Biological Sciences
TOPMed Lipids Working Group
Biological sciences
Good Health and Well Being
Phenotype
Public Health
Biotechnology
Developmental Biology
Genome-Wide Association Study
DOI:
10.1038/s41592-022-01640-x
Publication Date:
2022-10-27T16:06:35Z
AUTHORS (514)
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.
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CITATIONS (71)
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