# Circular Genomic Permutations 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. package ## Details ||| |:--|:--| |Package:|genomicper| |Type:|Package| |Version:|1.7| |Date:|2020-05-06| |License:|GPL-2| ## Author(s) Claudia P. Cabrera, Pau Navarro, Chris S. Haley Maintainer: Claudia Cabrera ## 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) `get_pathways`, 4) `read2_paths`, 5A) `snps_permutation`, 5B) `genes_permutation`, 6) `get_results`, 7) `plot_results` ## Examples ```r ############################################################################# # Genomicper functions ########## # 1) read_pvals(data_name="",snps_ann="") # 2) genome_order(all_data="") # 3) get_pathways(source="reactome",all_paths="",envir="") # 4) read2_paths(ordered_alldata="",gs_locs="",sets_from="",sets_prefix="RHSA",level="") # 5A) snps_permutation(ordered_alldata="",pers_ids="",ntraits="",nper="",saveto="", # threshold="",gs_locs=gs_locs,envir = "") # 5B) genes_permutation(ordered_alldata="",pers_ids="",pathways="", # ntraits="",nper="",threshold="",saveto="",gs_locs=gs_locs,envir = "") # 6) get_results(res_pattern="Permus",level="snp",from="workspace", # threshold=0.05, envir = "") # 7) 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() # Pathways can be downloaded using the function get_pathways() # 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 ## Not run: #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(Not run) # -- END OF DEMO ############################################### ```

genomicper-package