/STAAR

An R package for performing STAAR procedure in whole genome sequencing studies

Primary LanguageC++GNU General Public License v3.0GPL-3.0

R build status Build Status Build status License: GPL v3

STAAR (variant-Set Test for Association using Annotation infoRmation)

This is an R package for performing STAAR procedure in whole genome sequencing studies.

Description

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.

Workflow Overview

STAAR_workflow

Prerequisites

R (recommended version >= 3.4.2)

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.

Dependencies

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.

Installation

library(devtools)
devtools::install_github("xihaoli/STAAR")

Usage

Please see the STAAR user manual for detailed usage of STAAR package.

Version

The current version is 0.9.5 (April 24, 2020).

Citation

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, Joanne E. Curran, L. Adrienne Cupples, Barry I. Freedman, Xiuqing Guo, George Hindy, Sharon L.R. Kardia, Sekar Kathiresan, Alyna T. Khan, Charles L. Kooperberg, Marguerite R. Irvin, Cathy C. Laurie, X. Shirley Liu, Ani W. Manichaikul, Michael C. Mahaney, Rasika A. Mathias, Alanna C. Morrison, Lisa W. Martin, Stephen T. McGarvey, Braxton D. Mitchell, May E. Montasser, Jill Moore, 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 and Xihong Lin (2019+) "Dynamic incorporation of multiple in-silico functional annotations empowers rare variant association analysis of large whole genome sequencing studies at scale". Submitted.

License

This software is licensed under GPLv3.

GPLv3 GNU General Public License, GPLv3