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Type:

Package

Title:

Circular Genomic Permutation using Genome Wide Association p-Values

Version:

1.7

Date:

2021-05-06

Author:

Claudia P Cabrera [aut, cre], Pau Navarro [aut], Chris S Haley [aut]

Maintainer:

Claudia P Cabrera <c.cabrera@qmul.ac.uk>

Imports:

stats,grDevices,utils,graphics,DBI,reactome.db,AnnotationDbi

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(Cabrera et al (2012) <doi:10.1534/g3.112.002618>). 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.

License:

GPL-2

NeedsCompilation:

Packaged:

2021-05-06 16:06:27 UTC; PallisDell

Depends:

R (≥ 3.5.0)

Repository:

CRAN

Date/Publication:

2021-05-08 08:00:05 UTC

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.

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 <c.cabrera@qmul.ac.uk>

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*

Examples

#############################################################################
#  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 
###############################################

Reactome Pathway examples

Description

Each file "RHSAXXXX" contains the gene identifiers.

Usage

data(RHSA164843)

Format

The format is: num [1:6] 11168 155030 155348 155459 155908 2547...

Source

reactome.db

Examples

data(RHSA164843)

SNPs-Genes annotation to Distance 0 (SNPs within a gene)

Description

SNPs annotated to genes. Annotation only when the SNPs fall within start and end of transcription of the genes.

Usage

data(SNPsAnnotation)

Format

Sample data frame with 339096 SNP observations on the following 6 variables.

name: a character vector
Chromosome: a character vector
Location: a numeric vector of the SNP location
GENE_ID: a numeric vector with entrez geneID
Symbol: a character vector ; other annotation slot 1
Orientation: a character vector; other annotation slot 2

	name      Chromosome  Location  GENE_ID    Symbol     Orientation
	rs1000313          1  15405489    23254    KIAA1026             +
	rs1000533          1 168282491     9095    TBX19                +
	rs1000731          1 231963491    27185    DISC1                +

Source

NCBI Gene database,(http://www.ncbi.nlm.nih.gov/gene ; Build.37.1).

Examples

data(SNPsAnnotation)

GWAS p_values demo data

Description

GWAS p-values (tab delimited file). First Column must contain the SNP ids and the column name = "name"

Usage

data(demo)

Format

A data frame with SNPs identifiers and gwas p-values of association

name: a character vector
Trait1: a numeric vector
Trait2: a numeric vector
Trait3: a numeric vector
Trait4: a numeric vector
Trait5: a numeric vector
Trait6: a numeric vector
Trait7: a numeric vector
Trait8: a numeric vector
Trait9: a numeric vector

	name       Trait1      Trait2    Trait3   Trait4 	Trait5    Trait6
	rs10000010 0.9122360 0.30088096 0.2332038 0.5193068 0.1255104 0.07253145 
	rs10000023 0.8642906 0.52064064 0.9243443 0.7177759 0.9512171 0.81716250 
	rs10000030 0.2832705 0.99021664 0.8359339 0.9662707 0.8491221 0.50208681

Examples

#Read input demo file for "read_pvals" function
data(demo)

Gene-level Permutations

Description

Performs gene-level circular genomic permutations. In each permutation,the complete set of
SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations.
Once these 'simulated' p-values are assigned,the joint gene p-values are calculated using
Fisher's combination test,and pathways' association tested using the hypergeometric test

Usage

genes_permutation(ordered_alldata = "", pers_ids = "", pathways = "", 
ntraits = "", nper = 100, threshold = 0.05, seed=10,saveto = "workspace", 
gs_locs="", envir = "")

Arguments

ordered_alldata

Return variable from "genome_order". Ordered genome and trait p-values

gs_locs

Return variable from "genome_order". SNP indexes

pers_ids

Return variable "per_ors" from "read2_paths". Gene indexes

pathways

Return variable "pathways" from "read2_paths"

ntraits

Traits INDEX to be analysed. Index according to "ordered_alldata".
Trait Columns index must start at 7. Example: ntraits=c(7:9), ntraits=7

nper

Number of permutations.Example: nper=1000

threshold

Threshold to be set by the hypergeometric test. threshold=0.05

seed

Set a number for random sampling

saveto

Save permutation results to "workspace" OR "directory"

envir

R environment to save the data to when saveto is set to "workspace"

Value

Returns "Permus_trait" variables or files (permutation datasets).

References

Imports phyper (from stats)

Examples

#load data
data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)

# Prepare 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 data:
gper.env <- new.env()

# Get pathways
data(RHSA164843,RHSA446343,RHSA8876384,RHSA8964572,RHSA109582,RHSA1474244,envir=gper.env)

# Map Genes to pathways
paths_res <- read2_paths(ordered_alldata=ordered_alldata,gs_locs=gs_locs,
sets_from="workspace",sets_prefix="RHSA",level="gene",envir=gper.env)
pers_ids <- paths_res$per_ors
pathways<- paths_res$pathways

# Perform Permutations:
genes_permutation(ordered_alldata=ordered_alldata,
pers_ids=pers_ids,pathways=pathways,ntraits=c(7:9),
nper=10,threshold=0.05, saveto="workspace",
gs_locs=gs_locs,envir = gper.env)

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

Genome Order

Description

Orders the SNPs according to their genomic location

Usage

genome_order(all_data = "")

Arguments

all_data

SNPs to Genes Annotation and Trait Pvalues of Association
all_data = (read_pvals output) OR matrix/dataframe.

Details

Input Columns with "*" must be included for analysis
 
NOTE: Trait p-values must start at Column #7

# *Column 1: "name" (SNP_IDs - any SNP ID as character)
# *Column 2: Chromosome Location
# *Column 3: SNP Location 
# *Column 4: Gene ID 
# Column 5: Symbol (OR Annotation Field 1)
# Column 6: Annotaiton Field 2
# *Column 7: First trait pvalues of association
# Column N: Next trait pvalues of association
# Example Input Data:
name       Chromosome  Location GENE_ID Symbol Orientation abpi
rs10000010          4  21618674   80333 KCNIP4           - 0.91
rs10000023          4  95733906     658 BMPR1B           + 0.86
rs10000092          4  21895517   80333 KCNIP4           - 0.20
rs1000022          13  100461219 171425 CLYBL            + 0.26
rs10000300          4  40466547   54502 RBM47            - 0.58

Value

ordered_alldata

SNPs annotated to Genes and Trait p-values

gs_locs

Gene annotations, location indexes and number of observations

Format

	
	SNPs annotated to Genes and Trait p-values
#ordered_alldata[1:5,1:8]
name   Chromosome	Location GENE_ID  Symbol Orientation Trait1 Trait2
rs3934834	1  		1005806  NA     <NA>        <NA>  	 0.97  0.92
rs3737728	1		1021415  54991 	C1orf159      - 	 0.91  0.69
rs6687776	1		1030565  54991  C1orf159      - 	 0.71  0.45
rs9651273	1		1031540  54991  C1orf159      - 	 0.22  0.60
rs4970405	1		1048955  54991  C1orf159      - 	 0.77  0.56

	Gene annotations, location indexes and number of observations
#gs_locs[1:5,]
#      Symbol     Chromosome Location    Gene_ID Start_Indx Observations
# [1,] "A1BG"     "19"       "58864479"  "1"     "293976"   "1"         
# [2,] "A2M"      "12"       "9232268"   "2"     "215264"   "5"         
# [3,] "NAT1"     "8"        "18077310"  "9"     "151804"   "1"         
# [4,] "NAT2"     "8"        "18257280"  "10"    "151831"   "2"         
# [5,] "SERPINA3" "14"       "95080803"  "12"    "249519"   "2"

Examples


## DEMO WORKSPACE 

data(demo,SNPsAnnotation)
all_data<-read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
# GENOME ORDER
genome_results <- genome_order(all_data=all_data)
	ordered_alldata <- genome_results$ordered_alldata
	gs_locs <- genome_results$gs_locs

Pathways

Description

Helper function to download pathways and their gene identifiers. reactome.db used for pathway annotations.

Usage

get_pathways(source="reactome",all_paths=TRUE,envir = "")

Arguments

source

"reactome"

all_paths

TRUE or FALSE. If FALSE a subset will be asked by the function

envir

R environment to save Pathways to

Value

Returns "Pathways description" All downloaded pathways are saved in the workspace User will be prompt to set a prefix.

Examples

## Not run: 
# get pathways source = "reactome"
if (!require("reactome.db")) install.packages("reactome.db") 
library(reactome.db)

# Create new environment to save data:
gper.env <- new.env()

paths <- get_pathways(source="reactome",all_paths=FALSE,envir=gper.env)
# when prompted introduce species as listed
Homo sapiens
# when prompted introduce prefix. Avoid characters "-" and "_" (e.g mypath, or leave blank)
# if all_paths set to TRUE. All pathways are downloaded automatically
# IF all_paths set to FALSE, select a subset of pathway identifiers from 
# list. Separated by ","
R-HSA-8964572,R-HSA-9613354,R-HSA-8876384,R-HSA-446343,R-HSA-9620244

## End(Not run)

Circular Permutation Results

Description

Creates a summary dataframe of the genomic permutations datasets

Usage

get_results(res_pattern="Permus",level="snp",from="workspace",
threshold=0.05,envir = "")

Arguments

res_pattern

Pattern of the Permutation files/variable. eg. res=pattern="Permus"

level

Permutation level performed.level values "snp" or "gene"

from

Location of the permutation datasets.from values "workspace" or "directory"

threshold

Threshold of significance set

envir

R environment where save the data to

Value

results

Data frame with Pathway ID, Trait, Threshold set by permutations,
Gene results include the theoretical hypergeometric p-value and the,
observed (Empirical Hypergeometric p-values)
SNP results include the count of significan SNPs and the overall score
Score is the proportion of tests observed with more significant results

Format

## SNP level results
     PathID    Trait Threshold RealCount Score
1  hsa00010     abpi         0         0 0.037
2  hsa00010 abpildfa         0         0 0.040
3  hsa04720     abpi         2         0 0.311	
## Gene level results	
     PathID Trait   Threshold     P-Value  Observed
1  hsa00010  abpi 0.040441176 0.058823529 1.0000000
2  hsa00020  abpi 0.000000000 0.000000000 0.1666667
3  hsa00030  abpi 0.040441176 0.058823529 1.0000000

Examples

data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
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 data
gper.env <- new.env()

# Get pathways
data(RHSA164843,RHSA446343,RHSA8876384,RHSA8964572,RHSA109582,RHSA1474244,envir=gper.env)

paths_res <- read2_paths(ordered_alldata=ordered_alldata,gs_locs=gs_locs,
sets_from="workspace",sets_prefix="RHSA",level="snp",envir=gper.env)
		pers_ids <- paths_res$per_ors
		pathways<- paths_res$pathways

snps_permutation(ordered_alldata=ordered_alldata,pers_ids=pers_ids,
ntraits=c(7,9),nper=10,saveto="workspace",threshold=0.05,
gs_locs=gs_locs,envir= gper.env)

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

Hypergeometric Test (phyper)

Description

Performs Hypergeometric test (phyper() from R)

Usage

hyprbg(Sig_in_Paths, uniSig, gns_in_Paths, universe)

Arguments

Sig_in_Paths

Number of significant genes in the pathway

uniSig

Number of significant genes in the dataset

gns_in_Paths

Number of genes in the pathway

universe

Number of genes in the dataset

Value

Returns hypergeometric test

References

hyprbg Imports phyper() (from stats)

Plot Results Circular Permutation

Description

QQ plots

Usage

plot_results(results="",by="",plot_all=TRUE, var = "", save_plot=TRUE, plot_name="", 
bf= FALSE , save_qq = TRUE)

Arguments

results

Results datarame from "get_results()"

by

Visualize results by "trait" OR by "set"

plot_all

plot_all = TRUE plots all the variables in the results dataframe and saves a pdf file in the working directory. Setting plot_all to FALSE plots a single variable(trait or set). The argument "var" must be declared.

var

Variable name to plot

save_plot

save_plot = TRUE saves the plots in the working directory. save_plot = FALSE the plot is visualized at the console. save_plot = FALSE can be used only when plot_all is set to FALSE. The plot displayed at the console is interactive, clicking on a point displays the points name.

plot_name

Argument used to save the file name for the plots. Default value = Results_genomicper_[set/trait]

bf

Displays the bonferroni correction

save_qq

TRUE returns the qq plot values

Value

qq

Data frame with qq plot values

Examples


data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
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 the data:
gper.env <- new.env()

# Load Pathways
data(RHSA164843,RHSA446343,RHSA8876384,RHSA8964572,RHSA109582,RHSA1474244,envir=gper.env)


paths_res <- read2_paths(ordered_alldata=ordered_alldata,gs_locs=gs_locs,
sets_from="workspace",sets_prefix="RHSA",level="snp",envir=gper.env)
		pers_ids <- paths_res$per_ors
		pathways<- paths_res$pathways

snps_permutation(ordered_alldata=ordered_alldata,pers_ids=pers_ids,
ntraits=c(7,9),nper=10,saveto="workspace",threshold=0.05,
gs_locs=gs_locs,envir = gper.env)

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

#saves plots to working directory
## Not run: 
qq <- plot_results(results=results,by="set",plot_all=TRUE)
qq <- plot_results(results=results,by="trait",plot_all=FALSE,var="trait1")
qq <- plot_results(results=results,by="set",
	plot_all=FALSE,var="R-HSA-8964572",
	save_plot=FALSE) ## IMPORTANT: to EXIT interactive plot 
	## right click on the plot to stop

## End(Not run)

Read to SNPs to sets; Map SNPs to gene-sets/pathways

Description

Reads the sets/pathways, map the SNPs and genes to the gene-sets/pathways read2_paths uses the "genome_order" output(ordered_alldata, gs_locs) to assign genomic location indexes to each element in the gene-set. The permutation method must be defined (i.e. level = "snp" OR level = "gene").

Usage

read2_paths(ordered_alldata="",gs_locs="",sets_from="workspace",
sets_prefix="RHSA",level="snp",envir="")

Arguments

ordered_alldata

Ordered data according to the SNPs genomic location. Traits start at column 7
Return variable from:
genome_results <-genome_order(all_data=all_data)
ordered_alldata <- genome_results$ordered_alldata

gs_locs

Gene annotation,indexes and number of observations
Return variable from genome_order():
genome_results <-genome_order(all_data=all_data)
gs_locs <- genome_results$gs_locs

sets_from

Location of the gene-sets. Default set to "workspace"
sets_from="workspace" OR sets_from="directory"
"directory", only will search for information in the working directory.

sets_prefix

Prefix of the gene-set variables or files.
Default set to sets_prefix= "RHSA" e.g. Variables "RHSA164843","RHSA446343","RHSA8876384"
each variable/file contains the list of gene identifiers part of that pathway

level

The level at which the permutations will be performed. Assigns the indexes according to snps or genes
Default value "snp" level values = "snp" OR "gene"

envir

R environment where pathway data is stored. e.g(envir=.GlobalEnv, envir=gper.env)

Value

pathways

Pathway Id, Description,Number of Genes in the pathway, Number of genes found in the dataset, Number of SNPs found in the dataset

per_ors

A list of identifiers mapped to each pathway

Format

Input: Ordered_alldata
name     Chromosome  Location GENE_ID   Symbol Orientation Trait1 Trait2
rs1001567          1 9194614     <NA>     <NA>        <NA> 0.96 0.89
rs1000313          1 15405489   23254 KIAA1026           + 0.93 0.57
rs1002365          1 19797248    <NA>     <NA>        <NA> 0.68 0.58
rs1002706          1 25051153    <NA>     <NA>        <NA> 0.71 0.02
rs1002487          1 26865971    6195  RPS6KA1           + 0.98 0.78

Input:gs_locs
      Symbol   Chromosome Location    Gene_ID Start_Indx Observations
 [1,] "ACYP2"  "2"        "54399633"  "98"    "35"       "1"         
 [2,] "AMPD3"  "11"       "10514707"  "272"   "898"      "1"         
 [3,] "ANK2"   "4"        "113830885" "287"   "479"      "4"
 
Input:pathway example
RHSA8964572
[1]   1149 128486 161247  29923 345275  63924
          
Output:pathways
     ID            GenesInPath GenesFound SNPsInPath
"RHSA109582"  "681"       "8"        "11"      
"RHSA1474244" "418"       "7"        "10"      
"RHSA164843"  "11"        "0"        "0"       
"RHSA446343"  "4"         "1"        "1"       
"RHSA8876384" "32"        "1"        "1"       
"RHSA8964572" "6"         "1"        "1"

Examples

## DEMO - SNP Level data stored in workspace #######################
# library(genomicper)
data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
genome_results <-genome_order(all_data=all_data)
	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()

data(RHSA164843,RHSA446343,RHSA8876384,RHSA8964572,RHSA109582,RHSA1474244,envir=gper.env)

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

Read GWAS p-values of association and Merge with SNP annotations

Description

Read GWAS p-values of association and Merge with SNP annotations for analysis

Usage

read_pvals(data_name="",snps_ann="",from="workspace")

Arguments

data_name

GWAS p_values (tab delimited file)(SNP_IDs Trait1 Trait2 ...TraitN)

snps_ann

SNPs Annotation (SNPsAnnotation). Genomicper uses entrez gene ids to annotate associate SNPs-to genes-pathways
The annotation MUST match your data input (coordinates and chromosome format)
Any SNP ID is valid, as long the ID is set as character
The examples below show an option on how to annotate the SNPs prior the use of genomicper

from

Datasets location. Values "workspace" OR "directory"

Value

Dataframe: name; chromosome; Location; GeneID; Symbol; Orientation; Trait1; TraitN

Formats

GWAS p_values (tab delimited file)(SNP_IDs Trait1 Trait2 ...TraitN)
name       Trait1      Trait2    TraitN  
rs10000010 0.9122360 0.30088096 0.2332038
rs10000023 0.8642906 0.52064064 0.9243443 
rs10000030 0.2832705 0.99021664 0.8359339

SNPs Annotation (SNPsAnnotation)
name        Chromosome  Location  GENE_ID   Symbol    Orientation
rs1000313   1           15405489  23254     KIAA1026  +
rs1000533   1           168282491 9095      TBX19     +
rs1000731   1           231963491 27185     DISC1     +

Output:
name       Chromosome  Location GENE_ID Symbol Orientation   Trait1  
rs10000010          4  21618674   80333 KCNIP4           - 0.9122360
rs10000023          4  95733906     658 BMPR1B           + 0.8642906
rs10000030          4 103374154      NA   <NA>        <NA> 0.2832705

Examples

## DEMO // WORKSPACE
data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)

SNP-level permutations

Description

Performs SNP-level circular genomic permutations. In each permutation,
the complete set of SNP association p-values are permuted by rotation
with respect to the SNPs' genomic locations.
Once these 'simulated' p-values are assigned,the proportion of SNPs per
set above a pre-defined threshold is calculated

Usage

snps_permutation(ordered_alldata = "", pers_ids = "", ntraits = "", 
nper = 100, threshold = 0.05, seed=10,saveto = "workspace", 
gs_locs = "",envir ="")

Arguments

ordered_alldata

Return variable from "genome_order". Ordered genome and trait p-values

gs_locs

Return variable from "genome_order". SNP indexes

pers_ids

Return variable "per_ors" from "read2_paths". SNP indexes

ntraits

Traits INDEX to be analysed. Index according to "ordered_alldata".
Trait Columns index must start at 7. Example: ntraits=c(7:9), ntraits=7

nper

Number of permutations.Example: nper=1000

threshold

Threshold to be set by the hypergeometric test. threshold=0.05

seed

Set number for random sampling

saveto

Save permutation results to "workspace" OR "directory"

envir

R environment to save the Permutations to when saveto is set to "workspace"

Value

Returns "Permus_genesetsname" variables or files (permutation datasets).

Examples


data(demo,SNPsAnnotation)
all_data <- read_pvals(data_name=demo,snps_ann=SNPsAnnotation)
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 the permutations to:
gper.env <- new.env()

data(RHSA164843,RHSA446343,RHSA8876384,RHSA8964572,RHSA109582,RHSA1474244,envir=gper.env)

paths_res <- read2_paths(ordered_alldata=ordered_alldata,gs_locs=gs_locs,
sets_from="workspace",sets_prefix="RHSA",level="snp",envir=gper.env)
		pers_ids <- paths_res$per_ors
		pathways<- paths_res$pathways

		
# SNP permutations
snps_permutation(ordered_alldata=ordered_alldata,pers_ids=pers_ids,
ntraits=c(7,9),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)

Circular Genomic Permutations

Description

Details

Author(s)

References

See Also

Examples

Reactome Pathway examples

Description

Usage

Format

Source

Examples

SNPs-Genes annotation to Distance 0 (SNPs within a gene)

Description

Usage

Format

Source

Examples

GWAS p_values demo data

Description

Usage

Format

Examples

Gene-level Permutations

Description

Usage

Arguments

Value

References

See Also

Examples

Genome Order

Description

Usage

Arguments

Details

Value

Format

See Also

Examples

Pathways

Description

Usage

Arguments

Value

See Also

Examples

Circular Permutation Results

Description

Usage

Arguments

Value

Format

Examples

Hypergeometric Test (phyper)

Description

Usage

Arguments

Value

References

Plot Results Circular Permutation

Description

Usage

Arguments

Value

See Also

Examples

Read to SNPs to sets; Map SNPs to gene-sets/pathways

Description

Usage

Arguments

Value

Format

See Also

Examples

Read GWAS p-values of association and Merge with SNP annotations

Description

Usage

Arguments