R/Gene_Centric_Noncoding_cond_spa.R

In xihaoli/STAARpipeline: STAARpipeline for Analyzing Whole-Genome/Whole-Exome Sequencing Data

#' Gene-centric conditional analysis of noncoding functional categories using STAAR procedure for imbalance case-control setting
#'
#' The \code{Gene_Centric_Noncoding_cond_spa} function takes in chromosome, gene name, functional category,
#' the object of opened annotated GDS file, the object from fitting the null model,
#' and the set of known variants to be adjusted for in conditional analysis to analyze the conditional association between an
#' imbalanced case-control phenotype and noncoding functional categories of a gene by using STAAR procedure.
#' For each noncoding functional category, the conditional STAAR-B p-value is a p-value from an omnibus test
#' that aggregated conditional Burden(1,25) and Burden(1,1),
#' together with conditional p-values of each test weighted by each annotation using Cauchy method.
#' @param chr chromosome.
#' @param gene_name name of the gene to be analyzed using STAAR procedure.
#' @param category the noncoding functional category to be analyzed using STAAR procedure. Choices include
#' \code{downstream}, \code{upstream}, \code{UTR}, \code{promoter_CAGE}, \code{promoter_DHS}, \code{enhancer_CAGE}, \code{enhancer_DHS} (default = \code{downstream}).
#' @param genofile an object of opened annotated GDS (aGDS) file.
#' @param obj_nullmodel an object from fitting the null model, which is either the output from \code{\link{fit_nullmodel}} function,
#' or the output from \code{fitNullModel} function in the \code{GENESIS} package and transformed using the \code{\link{genesis2staar_nullmodel}} function.
#' @param known_loci the data frame of variants to be adjusted for in conditional analysis and should
#' contain 4 columns in the following order: chromosome (CHR), position (POS), reference allele (REF),
#' and alternative allele (ALT) (default = NULL).
#' @param rare_maf_cutoff the cutoff of maximum minor allele frequency in
#' defining rare variants (default = 0.01).
#' @param rv_num_cutoff the cutoff of minimum number of variants of analyzing
#' a given variant-set (default = 2).
#' @param rv_num_cutoff_max the cutoff of maximum number of variants of analyzing
#' a given variant-set (default = 1e+09).
#' @param rv_num_cutoff_max_prefilter the cutoff of maximum number of variants
#' before extracting the genotype matrix (default = 1e+09).
#' @param QC_label channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").
#' @param variant_type type of variant included in the analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV").
#' @param geno_missing_imputation method of handling missing genotypes. Either "mean" or "minor" (default = "mean").
#' @param Annotation_dir channel name of the annotations in the aGDS file \cr (default = "annotation/info/FunctionalAnnotation").
#' @param Annotation_name_catalog a data frame containing the name and the corresponding channel name in the aGDS file.
#' @param Use_annotation_weights use annotations as weights or not (default = TRUE).
#' @param Annotation_name a vector of annotation names used in STAAR (default = NULL).
#' @param SPA_p_filter logical: are only the variants with a normal approximation based p-value smaller than a pre-specified threshold use the SPA method to recalculate the p-value, only used for imbalanced case-control setting (default = FALSE).
#' @param p_filter_cutoff threshold for the p-value recalculation using the SPA method, only used for imbalanced case-control setting (default = 0.05).
#' @return A data frame containing the conditional STAAR p-values (including STAAR-B) corresponding to the noncoding functional category of the given gene.
#' @references Li, Z., Li, X., et al. (2022). A framework for detecting
#' noncoding rare-variant associations of large-scale whole-genome sequencing
#' studies. \emph{Nature Methods}, \emph{19}(12), 1599-1611.
#' (\href{https://doi.org/10.1038/s41592-022-01640-x}{pub})
#' @references Li, X., Li, Z., et al. (2020). Dynamic incorporation of multiple
#' in silico functional annotations empowers rare variant association analysis of
#' large whole-genome sequencing studies at scale. \emph{Nature Genetics}, \emph{52}(9), 969-983.
#' (\href{https://doi.org/10.1038/s41588-020-0676-4}{pub})
#' @references Sofer, T., et al. (2019). A fully adjusted two-stage procedure for rank-normalization
#' in genetic association studies. \emph{Genetic Epidemiology}, \emph{43}(3), 263-275.
#' (\href{https://doi.org/10.1002/gepi.22188}{pub})
#' @export

Gene_Centric_Noncoding_cond_spa <- function(chr,gene_name,category=c("downstream","upstream","UTR","promoter_CAGE","promoter_DHS","enhancer_CAGE","enhancer_DHS"),
                                            genofile,obj_nullmodel,known_loci=NULL,rare_maf_cutoff=0.01,
                                            rv_num_cutoff=2,rv_num_cutoff_max=1e9,rv_num_cutoff_max_prefilter=1e9,
                                            QC_label="annotation/filter",variant_type=c("SNV","Indel","variant"),geno_missing_imputation=c("mean","minor"),
                                            Annotation_dir="annotation/info/FunctionalAnnotation",Annotation_name_catalog,
                                            Use_annotation_weights=c(TRUE,FALSE),Annotation_name=NULL,
                                            SPA_p_filter=FALSE,p_filter_cutoff=0.05){

	## evaluate choices
	category <- match.arg(category)
	variant_type <- match.arg(variant_type)
	geno_missing_imputation <- match.arg(geno_missing_imputation)

	if(is.null(known_loci))
	{
		known_loci <- data.frame(chr=logical(0),pos=logical(0),ref=character(0),alt=character(0))
	}

	if(category=="downstream")
	{
		results <- downstream_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                               known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                               rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                               QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                               Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                               Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                               SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	if(category=="upstream")
	{
		results <- upstream_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                             known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                             rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                             QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                             Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                             Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                             SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	if(category=="UTR")
	{
		results <- UTR_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                        known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                        rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                        QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                        Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                        Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                        SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	if(category=="promoter_CAGE")
	{
		results <- promoter_CAGE_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                                  known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                                  rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                                  QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                                  Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                                  Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                                  SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	if(category=="promoter_DHS")
	{
		results <- promoter_DHS_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                                 known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                                 rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                                 QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                                 Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                                 Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                                 SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	if(category=="enhancer_CAGE")
	{
		results <- enhancer_CAGE_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                                  known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                                  rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                                  QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                                  Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                                  Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                                  SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	if(category=="enhancer_DHS")
	{
		results <- enhancer_DHS_cond_spa(chr,gene_name,genofile,obj_nullmodel,
		                                 known_loci,rare_maf_cutoff=rare_maf_cutoff,rv_num_cutoff=rv_num_cutoff,
		                                 rv_num_cutoff_max=rv_num_cutoff_max,rv_num_cutoff_max_prefilter=rv_num_cutoff_max_prefilter,
		                                 QC_label=QC_label,variant_type=variant_type,geno_missing_imputation=geno_missing_imputation,
		                                 Annotation_dir=Annotation_dir,Annotation_name_catalog=Annotation_name_catalog,
		                                 Use_annotation_weights=Use_annotation_weights,Annotation_name=Annotation_name,
		                                 SPA_p_filter=SPA_p_filter,p_filter_cutoff=p_filter_cutoff)
	}

	return(results)
}