MetaSTAAR v0.9.6.1

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: MetaSTAAR
 Type: Package
-Title: Meta-Analysis of STAAR (MetaSTAAR) Procedure for Dynamic Incorporation of Multiple Functional Annotations in Whole-Genome Sequencing Studies
+Title: Meta-Analysis of STAAR (MetaSTAAR) Procedure for Dynamic Incorporation of Multiple Functional Annotations in Whole Genome Sequencing Studies
 Version: 0.9.6.1
-Date: 2022-12-23
+Date: 2023-01-13
 Author: Xihao Li [aut, cre], Zilin Li [aut, cre], Corbin Quick [aut]
 Maintainer: Xihao Li <[email protected]>, Zilin Li <[email protected]>
-Description: An R package for performing MetaSTAAR procedure in whole-genome sequencing studies.
+Description: An R package for performing MetaSTAAR procedure in whole genome sequencing studies.
 License: GPL-3
 Copyright: See COPYRIGHTS for details.
 Imports: Rcpp, STAAR, Matrix, dplyr, methods, expm, MASS

R/Burden_Effect_Size_meta.R

@@ -19,9 +19,9 @@
 #' @return \code{Burden_pvalue}: the Burden-MS(1,1) p-value for the given variant-set.
 #' @return \code{Burden_Est}: the effect size estimate of Burden-MS(1,1) for the given variant-set.
 #' @return \code{Burden_SE_Est}: the standard error estimate of \code{Burden_Est} for the given variant-set.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR.R

@@ -56,9 +56,9 @@
 #' including ACAT-V-MS(1,1) p-value weighted by MAF, the ACAT-V-MS(1,1)
 #' p-values weighted by each annotation, and a MetaSTAAR-A(1,1)
 #' p-value by aggregating these p-values using Cauchy method.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @references Li, X., Li, Z., et al. (2020). Dynamic incorporation of multiple
 #' in silico functional annotations empowers rare variant association analysis of

R/MetaSTAAR_cond.R

@@ -57,9 +57,9 @@
 #' including conditional ACAT-V-MS(1,1) p-value weighted by MAF, the conditional ACAT-V-MS(1,1)
 #' p-values weighted by each annotation, and a conditional MetaSTAAR-A(1,1)
 #' p-value by aggregating these p-values using Cauchy method.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @references Li, X., Li, Z., et al. (2020). Dynamic incorporation of multiple
 #' in silico functional annotations empowers rare variant association analysis of

R/MetaSTAAR_individual_analysis.R

@@ -32,9 +32,9 @@
 #' the standard error associated with the score test statistic (Score_SE), the estimated effect size of the minor allele (Est),
 #' the standard error associated with the estimated effect size (Est_se).
 #' If a variant in the merged variant list has standard error equal to 0, the p-value will be set as 1.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_merge.R

@@ -40,9 +40,9 @@
 #' of interest whose combined minor allele frequency is below \code{rare_maf_cutoff}.
 #' @return \code{cov} the merged covariance matrix of all variants in the genetic region
 #' of interest whose combined minor allele frequency is below \code{rare_maf_cutoff}.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_merge_cond.R

@@ -50,9 +50,9 @@
 #' @return \code{cov_cond}: the merged conditional covariance matrix of all variants in the genetic region
 #' of interest whose combined minor allele frequency is below \code{rare_maf_cutoff},
 #' adjusting for a given list of variants.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_merge_varlist.R

@@ -38,9 +38,9 @@
 #' of interest whose combined minor allele frequency is below \code{rare_maf_cutoff}.
 #' @return \code{cov} the merged covariance matrix of all variants in the given variant position list
 #' of interest whose combined minor allele frequency is below \code{rare_maf_cutoff}.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_merge_varlist_cond.R

@@ -48,9 +48,9 @@
 #' @return \code{cov_cond}: the merged conditional covariance matrix of all variants in the given variant position list
 #' of interest whose combined minor allele frequency is below \code{rare_maf_cutoff},
 #' adjusting for a given list of variants.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_worker_cov.R

@@ -25,9 +25,9 @@
 #' @return \code{GTSinvG_rare}: the sparse matrix of all variants in the variant-set
 #' whose minor allele frequency is below \code{cov_maf_cutoff} (the sparse weighted
 #' covariance file), stored as a rectangle format.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_worker_cov_cond.R

@@ -31,9 +31,9 @@
 #' @return \code{variant_adj_info}: the data frame or matrix of adjusted variant information (unique identifier)
 #' with p_adj rows (listed in the same order as the columns of \code{GTPG_cond}) and 4 columns: chromosome (chr),
 #' position (pos), reference allele (ref), alternative allele (alt), score statistic (U), and variance (V).
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

R/MetaSTAAR_worker_sumstat.R

@@ -18,9 +18,9 @@
 #' alternative allele (alt), quality control status (qc_label, optional), alternative allele count (alt_AC), minor allele count (MAC),
 #' minor allele frequency (MAF), study sample size (N), score statistic (U), variance (V), and
 #' the (low-rank decomposed) dense component of the covariance file.
-#' @references Li, X., et al. (2022). Powerful, scalable and resource-efficient
+#' @references Li, X., et al. (2023). Powerful, scalable and resource-efficient
 #' meta-analysis of rare variant associations in large whole genome sequencing studies.
-#' \emph{Nature Genetics}.
+#' \emph{Nature Genetics}, \emph{55}(1), 154-164.
 #' (\href{https://doi.org/10.1038/s41588-022-01225-6}{pub})
 #' @export
 

README.md

@@ -4,9 +4,9 @@
 [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
 
 # MetaSTAAR (Meta-analysis of variant-Set Test for Association using Annotation infoRmation)
-This is an R package for performing MetaSTAAR procedure in whole-genome sequencing studies.
+This is an R package for performing MetaSTAAR procedure in whole genome sequencing studies.
 ## Description
-MetaSTAAR is an R package for performing Meta-analysis of variant-Set Test for Association using Annotation infoRmation (MetaSTAAR) procedure in whole-genome sequencing (WGS) studies. MetaSTAAR enables functionally-informed rare variant meta-analysis of large WGS studies using an efficient, sparse matrix approach for storing summary statistic, while protecting data privacy of study participants and avoiding sharing subject-level data. MetaSTAAR accounts for relatedness and population structure of continuous and dichotomous traits, and boosts the power of rare variant meta-analysis by incorporating multiple variant functional annotations.
+MetaSTAAR is an R package for performing Meta-analysis of variant-Set Test for Association using Annotation infoRmation (MetaSTAAR) procedure in whole genome sequencing (WGS) studies. MetaSTAAR enables functionally-informed rare variant meta-analysis of large WGS studies using an efficient, sparse matrix approach for storing summary statistic, while protecting data privacy of study participants and avoiding sharing subject-level data. MetaSTAAR accounts for relatedness and population structure of continuous and dichotomous traits, and boosts the power of rare variant meta-analysis by incorporating multiple variant functional annotations.
 ## Workflow Overview
 ![MetaSTAAR_workflow](docs/MetaSTAAR_workflow.jpg)
 ## Prerequisites
@@ -23,13 +23,13 @@ devtools::install_github("xihaoli/MetaSTAAR",ref="main")
 ## Usage
 Please see the <a href="docs/MetaSTAAR_manual.pdf">**MetaSTAAR** user manual</a> for detailed usage of MetaSTAAR package. The scripts used to generate results in the <a href="https://doi.org/10.1038/s41588-022-01225-6">manuscript</a> are available on <a href="https://doi.org/10.5281/zenodo.6668274">_Zenodo_</a>.
 ## Data Availability
-The whole-genome individual functional annotation data assembled from a variety of sources and the computed annotation principal components are available at the [Functional Annotation of Variant - Online Resource (FAVOR)](https://favor.genohub.org) site and [FAVOR Essential Database](https://doi.org/10.7910/DVN/1VGTJI).
+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)](https://favor.genohub.org) site and [FAVOR Essential Database](https://doi.org/10.7910/DVN/1VGTJI).
 ## Version
-The current version is 0.9.6.1 (December 23, 2022).
+The current version is 0.9.6.1 (January 13, 2023).
 ## Citation
 If you use **MetaSTAAR** for your work, please cite:
 
-Xihao Li, Corbin Quick, Hufeng Zhou, Sheila M. Gaynor, Yaowu Liu, Han Chen, Margaret Sunitha Selvaraj, Ryan Sun, Rounak Dey, Donna K. Arnett, Lawrence F. Bielak, Joshua C. Bis, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jennifer A. Brody, Brian E. Cade, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Paul S. de Vries, Ravindranath Duggirala, Barry I. Freedman, Harald H. H. Göring, Xiuqing Guo, Jeffrey Haessler, Rita R. Kalyani, Charles Kooperberg, Brian G. Kral, Leslie A. Lange, Ani Manichaikul, Lisa W. Martin, Stephen T. McGarvey, 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, Colleen M. Sitlani, Jennifer A. Smith, Kent D. Taylor, Ramachandran S. Vasan, Cristen J. Willer, James G. Wilson, Lisa R. Yanek, Wei Zhao, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Jerome I. Rotter, Pradeep Natarajan, Gina M. Peloso, Zilin Li, & Xihong Lin. (2022). **Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies**. _Nature Genetics_. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36564505">36564505</a>. DOI: <a href="https://doi.org/10.1038/s41588-022-01225-6">10.1038/s41588-022-01225-6</a>.
+Xihao Li, Corbin Quick, Hufeng Zhou, Sheila M. Gaynor, Yaowu Liu, Han Chen, Margaret Sunitha Selvaraj, Ryan Sun, Rounak Dey, Donna K. Arnett, Lawrence F. Bielak, Joshua C. Bis, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jennifer A. Brody, Brian E. Cade, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Paul S. de Vries, Ravindranath Duggirala, Barry I. Freedman, Harald H. H. Göring, Xiuqing Guo, Jeffrey Haessler, Rita R. Kalyani, Charles Kooperberg, Brian G. Kral, Leslie A. Lange, Ani Manichaikul, Lisa W. Martin, Stephen T. McGarvey, 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, Colleen M. Sitlani, Jennifer A. Smith, Kent D. Taylor, Ramachandran S. Vasan, Cristen J. Willer, James G. Wilson, Lisa R. Yanek, Wei Zhao, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Jerome I. Rotter, Pradeep Natarajan, Gina M. Peloso, Zilin Li, & Xihong Lin. (2023). **Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies**. _Nature Genetics_, _55_(1), 154-164. PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/36564505">36564505</a>. DOI: <a href="https://doi.org/10.1038/s41588-022-01225-6">10.1038/s41588-022-01225-6</a>.
 ## License
 This software is licensed under GPLv3.