genomicper (version 1.8)

genomicper-package: Circular Genomic Permutations

Description

Description: Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user.

Author

Claudia P. Cabrera, Pau Navarro, Chris S. Haley
Maintainer: Claudia Cabrera <[e-mail]>

Details

Package:	genomicper
Type:	Package
Version:	1.8
Date:	2026-02-21
License:	GPL-2

References

SNP-level Permutations:
Genomicper: genome-wide association SNP-set analysis
Claudia P. Cabrera*, Pau Navarro*, Jennifer E. Huffman, Alan F. Wright, Caroline Hayward,Harry Campbell, James F. Wilson, Igor Rudan, Nicholas D. Hastie, Veronique Vitart, Chris S. Haley*

Gene-level Permutations:
Uncovering Networks from Genome-Wide Association Studies via
Circular Genomic Permutation. G3: Genes|Genomes|Genetics 2, 1067-1075.
Claudia P. Cabrera*, Pau Navarro*, Jennifer E. Huffman, Alan F. Wright, Caroline Hayward,Harry Campbell, James F. Wilson, Igor Rudan, Nicholas D. Hastie, Veronique Vitart, Chris S. Haley*

See Also

Genomicper functions: 1) read_pvals, 2) genome_order, 3) read2_paths, 4A) snps_permutation, 4B) genes_permutation, 5) get_results, 6) plot_results

Examples

#############################################################################
#  Genomicper functions                                            ##########
# 1)  read_pvals(data_name="",snps_ann="")
# 2)  genome_order(all_data="")
# 3)  read2_paths(ordered_alldata="",gs_locs="",sets_from="",sets_prefix="RHSA",level="")
# 4A) snps_permutation(ordered_alldata="",pers_ids="",ntraits="",nper="",saveto="",
#		threshold="",gs_locs=gs_locs,envir = "")	
# 4B) genes_permutation(ordered_alldata="",pers_ids="",pathways="",
#		ntraits="",nper="",threshold="",saveto="",gs_locs=gs_locs,envir = "")
# 5)  get_results(res_pattern="Permus",level="snp",from="workspace",
#		threshold=0.05, envir = "")
# 6) plot_results(results = "", by = "", plot_all = TRUE, var = "", save_plot = TRUE, 
# 						plot_name = "", bf = FALSE, save_qq = TRUE)  
#############################################################################
############## DEMO: #######################################################

#### SNP-level  #############################################################
# SNPs annotation and Pathways provided by user
# all data stored at the WORKSPACE

### Load files for analysis
data(demo,SNPsAnnotation)

# Read & format GWAS pvalues
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)

# Order data according to the genome
genome_results <-genome_order(all_data=all_data)
	# Results from genome_order
	ordered_alldata <- genome_results$ordered_alldata
	gs_locs <- genome_results$gs_locs

# Create new environment to save variables (e.g. pathways, permutations):
gper.env <- new.env()

# Load example pathways into the new environment. 
data(RHSA164843,RHSA446343,RHSA8876384,RHSA8964572,RHSA109582,RHSA1474244,envir=gper.env)

# Map SNPs to pathways 
paths_res <- read2_paths(ordered_alldata=ordered_alldata,
gs_locs=gs_locs,sets_from="workspace",sets_prefix="RHSA",
level="snp",envir=gper.env)
	# Results from read2_paths:		
	pers_ids <- paths_res$per_ors
	pathways<- paths_res$pathways

# Perform permutations:
snps_permutation(ordered_alldata=ordered_alldata,
pers_ids=pers_ids,ntraits=c(7:13),nper=10,saveto="workspace",
threshold=0.05,gs_locs=gs_locs,envir = gper.env)		  

# Get results						
results <- get_results(res_pattern="Permus",level="snp",
from="workspace",threshold=0.05,envir = gper.env)

# Plot results
if (FALSE) {
#saves plots to working directory
qq <- plot_results(results=results,by="set",plot_all=TRUE)
qq <- plot_results(results=results,by="trait",
plot_all=FALSE,var="trait1")
# Displays interactive plot. Select a trait/set to plot and 
# set arguments save_plot=FALSE, plot_all = FALSE
# IMPORTANT: to EXIT interactive plot, RIGHT CLICK on the
# plot and STOP.
qq <- plot_results(results=results,by="set",plot_all=FALSE,
var="RHSA109582",save_plot=FALSE)
}
# -- END OF DEMO 
###############################################