STAAR (variant-Set Test for Association using Annotation infoRmation)
This is an R package for performing STAAR procedure in whole-genome sequencing studies.
STAAR is an R package for performing variant-Set Test for Association using Annotation infoRmation (STAAR) procedure in whole-genome sequencing (WGS) studies. STAAR is a general framework that incorporates both qualitative functional categories and quantitative complementary functional annotations using an omnibus multi-dimensional weighting scheme. STAAR accounts for population structure and relatedness, and is scalable for analyzing large WGS studies of continuous and dichotomous traits.
R (recommended version >= 3.5.1)
For optimal computational performance, it is recommended to use an R version configured with the Intel Math Kernel Library (or other fast BLAS/LAPACK libraries). See the instructions on building R with Intel MKL.
STAAR links to R packages Rcpp and RcppArmadillo, and also imports R packages Rcpp, GMMAT, GENESIS, Matrix. These dependencies should be installed before installing STAAR.
A docker image for STAAR, including R (version 3.6.1) built with Intel MKL and all STAAR-related packages (STAAR, STAARpipeline, STAARpipelineSummary) pre-installed, is located in the Docker Hub. The docker image can be pulled using
docker pull zilinli/staarpipeline:0.9.6
Please see the STAAR user manual for detailed usage of STAAR package. Please see the STAAR tutorial for an example of analyzing sequencing data using STAAR procedure. Please see the STAARpipeline tutorial for a detailed example of analyzing sequencing data using STAAR and STAARpipeline.
The whole-genome functional annotation data assembled from a variety of sources and the precomputed annotation principal components are available at the Functional Annotation of Variant - Online Resource (FAVOR) site and FAVOR Essential Database.
The current version is 0.9.6.2 (January 13, 2023).
If you use STAAR for your work, please cite:
Xihao Li*, Zilin Li*, Hufeng Zhou, Sheila M. Gaynor, Yaowu Liu, Han Chen, Ryan Sun, Rounak Dey, Donna K. Arnett, Stella Aslibekyan, Christie M. Ballantyne, Lawrence F. Bielak, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jai G. Broome, Matthew P. Conomos, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Barry I. Freedman, Xiuqing Guo, George Hindy, Marguerite R. Irvin, Sharon L. R. Kardia, Sekar Kathiresan, Alyna T. Khan, Charles L. Kooperberg, Cathy C. Laurie, X. Shirley Liu, Michael C. Mahaney, Ani W. Manichaikul, Lisa W. Martin, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, May E. Montasser, Jill E. Moore, Alanna C. Morrison, Jeffrey R. O'Connell, Nicholette D. Palmer, Akhil Pampana, Juan M. Peralta, Patricia A. Peyser, Bruce M. Psaty, Susan Redline, Kenneth M. Rice, Stephen S. Rich, Jennifer A. Smith, Hemant K. Tiwari, Michael Y. Tsai, Ramachandran S. Vasan, Fei Fei Wang, Daniel E. Weeks, Zhiping Weng, James G. Wilson, Lisa R. Yanek, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Benjamin M. Neale, Shamil R. Sunyaev, Gonçalo R. Abecasis, Jerome I. Rotter, Cristen J. Willer, Gina M. Peloso, Pradeep Natarajan, & Xihong Lin. (2020). Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nature Genetics, 52(9), 969-983. PMID: 32839606. PMCID: PMC7483769. DOI: 10.1038/s41588-020-0676-4.
Zilin Li*, Xihao Li*, Hufeng Zhou, Sheila M. Gaynor, Margaret Sunitha Selvaraj, Theodore Arapoglou, Corbin Quick, Yaowu Liu, Han Chen, Ryan Sun, Rounak Dey, Donna K. Arnett, Paul L. Auer, Lawrence F. Bielak, Joshua C. Bis, Thomas W. Blackwell, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jennifer A. Brody, Brian E. Cade, Matthew P. Conomos, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Paul S. de Vries, Ravindranath Duggirala, Nora Franceschini, Barry I. Freedman, Harald H. H. Göring, Xiuqing Guo, Rita R. Kalyani, Charles Kooperberg, Brian G. Kral, Leslie A. Lange, Bridget M. Lin, Ani Manichaikul, Alisa K. Manning, Lisa W. Martin, Rasika A. Mathias, James B. Meigs, Braxton D. Mitchell, May E. Montasser, Alanna C. Morrison, Take Naseri, Jeffrey R. O’Connell, Nicholette D. Palmer, Patricia A. Peyser, Bruce M. Psaty, Laura M. Raffield, Susan Redline, Alexander P. Reiner, Muagututi’a Sefuiva Reupena, Kenneth M. Rice, Stephen S. Rich, Jennifer A. Smith, Kent D. Taylor, Margaret A. Taub, Ramachandran S. Vasan, Daniel E. Weeks, James G. Wilson, Lisa R. Yanek, Wei Zhao, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Jerome I. Rotter, Cristen J. Willer, Pradeep Natarajan, Gina M. Peloso, & Xihong Lin. (2022). A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies. Nature Methods, 19(12), 1599-1611. PMID: 36303018. DOI: 10.1038/s41592-022-01640-x.
This software is licensed under GPLv3.