Single_Variants_List_Analysis.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Single_Variants_List_Analysis.R
\name{Single_Variants_List_Analysis}
\alias{Single_Variants_List_Analysis}
\title{Calculate individual-variant p-values of a list of variants}
\usage{
Single_Variants_List_Analysis(
  agds_dir,
  single_variants_list,
  obj_nullmodel,
  QC_label = "annotation/filter",
  geno_missing_imputation = c("mean", "minor"),
  p_filter_cutoff = 0.05,
  tol = .Machine$double.eps^0.25,
  max_iter = 1000
)
}
\arguments{
\item{agds_dir}{file directory of annotated GDS (aGDS) files for all chromosomes (1-22).}

\item{single_variants_list}{name a data frame containing the information of variants to be functionally annotated. The data frame must include 4 columns with
the following names: "CHR" (chromosome number), "POS" (position), "REF" (reference allele), and "ALT" (alternative allele).}

\item{obj_nullmodel}{an object from fitting the null model, which is either the output from \code{fit_nullmodel} function in the \code{STAARpipeline} package,
or the output from \code{fitNullModel} function in the \code{GENESIS} package and transformed using the \code{genesis2staar_nullmodel} function in the \code{STAARpipeline} package.}

\item{QC_label}{channel name of the QC label in the GDS/aGDS file (default = "annotation/filter").}

\item{geno_missing_imputation}{method of handling missing genotypes. Either "mean" or "minor" (default = "mean").}

\item{p_filter_cutoff}{threshold for the p-value recalculation using the SPA method (default = 0.05)}

\item{tol}{a positive number specifying tolerance, the difference threshold for parameter
estimates in saddlepoint approximation algorithm below which iterations should be stopped (default = ".Machine$double.eps^0.25").}

\item{max_iter}{a positive integer specifying the maximum number of iterations for applying the saddlepoint approximation algorithm (default = "1000").}
}
\value{
a data frame containing the basic information (chromosome, position, reference allele and alternative allele)
the score test p-values, and the effect sizes for the input variants.
}
\description{
The \code{Single_Variants_List_Analysis} function takes in a list of variants to calculate the p-values and effect sizes of the input variants
(effect size estimations are not provided for imbalanced case-control setting).
Note: this function only supports for null model fitting using sparse GRM.
}
\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})
}