View source: R/Gene_Centric_Coding_Info.R
Gene_Centric_Coding_Info | R Documentation |
The Gene_Centric_Coding_Info
function takes in a coding mask of a gene to functionally annotate the rare variants in the mask.
Gene_Centric_Coding_Info(
category = c("plof", "plof_ds", "missense", "disruptive_missense", "synonymous", "ptv",
"ptv_ds"),
chr,
genofile,
obj_nullmodel,
gene_name,
known_loci = NULL,
rare_maf_cutoff = 0.01,
method_cond = c("optimal", "naive"),
QC_label = "annotation/filter",
variant_type = c("SNV", "Indel", "variant"),
geno_missing_imputation = c("mean", "minor"),
Annotation_dir = "annotation/info/FunctionalAnnotation",
Annotation_name_catalog,
Annotation_name
)
category |
the coding functional category of rare variants to be functionally annotated. Choices include
|
chr |
chromosome. |
genofile |
an object of opened annotated GDS (aGDS) file. |
obj_nullmodel |
an object from fitting the null model, which is either the output from |
gene_name |
name of the gene to be annotated. |
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). |
rare_maf_cutoff |
the cutoff of maximum minor allele frequency in defining rare variants (default = 0.01). |
method_cond |
a character value indicating the method for conditional analysis.
|
QC_label |
channel name of the QC label in the GDS/aGDS file. |
variant_type |
type of variant included in the conditional analysis. Choices include "SNV", "Indel", or "variant" (default = "SNV"). |
geno_missing_imputation |
method of handling missing genotypes. Either "mean" or "minor" (default = "mean"). |
Annotation_dir |
channel name of the annotations in the aGDS file |
Annotation_name_catalog |
a data frame containing the name and the corresponding channel name in the aGDS file. |
Annotation_name |
a vector of qualitative/quantitative annotation names user wants to extract. |
A data frame containing the basic information (chromosome, position, reference allele and alternative allele), unconditional and conditional the score test p-values (not provided for imbalanced case-control setting), and annotation scores for the rare variants of the input coding mask.
Li, Z., Li, X., et al. (2022). A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nature Methods, 19(12), 1599-1611. (pub)
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