Package 'GwasDataImport'

Title: Import EBI Data to OpenGWAS
Description: Determine the new EBI data not present in OpenGWAS. Download dataset and import metadata. Upload processed data and metadata to OpenGWAS.
Authors: Gibran Hemani [aut, cre] , Philip Haycock [aut] , Tom Palmer [aut]
Maintainer: Gibran Hemani <[email protected]>
License: MIT + file LICENSE
Version: 0.2
Built: 2025-01-08 06:31:25 UTC
Source: https://github.com/MRCIEU/GwasDataImport

Help Index

being_processed

Description

List of EBI datasets that are currently being processed

Usage

being_processed(dat)

Arguments

dat

Output from determine_new_datasets

Value

Updated dat

Create dataset of some hm3 SNPs and their build positions

Description

Create dataset of some hm3 SNPs and their build positions

Usage

create_build_reference()

Value

saves build_ref object

Create marts for b36-38

Description

Slow to create these so just make once and save

Usage

create_marts()

Value

Saves data to data/marts.rdata

Object that downloads, develops and uploads GWAS summary datasets for IEU OpenGWAS database

Description

Object that downloads, develops and uploads GWAS summary datasets for IEU OpenGWAS database

Object that downloads, develops and uploads GWAS summary datasets for IEU OpenGWAS database

Public fields

filename

Path to raw GWAS summary dataset

igd_id

ID to use for upload. If NULL then the next available ID in batch ieu-b will be used automatically

wd

Work directory in which to save processed files. Will be deleted upon completion

gwas_out

path to processed summary file

nsnp_read

Number of SNPs read initially

nsnp

Number of SNPs retained after reading

metadata

List of meta-data entries

metadata_test

List of outputs from tests of the effect allele, effect allele frequency columns and summary data using CheckSumStats

metadata_file

Path to meta-data json file

datainfo

List of GWAS file parameters

datainfo_file

Path to datainfo json file

params

Initial column identifiers specified for raw dataset

metadata_uploaded

TRUE/FALSE of whether the metadata has been uploaded

gwasdata_uploaded

TRUE/FALSE of whether the gwas data has been uploaded

metadata_upload_status

Response from server about upload process

gwasdata_upload_status

Response from server about upload process

Methods

Public methods

Method new()

Initialise

Usage
Dataset$new(filename = NULL, wd = tempdir(), igd_id = NULL)
Arguments
filename

Path to raw GWAS summary data file

wd

Path to directory to use as the working directory. Will be deleted upon completion - best to keep as the default randomly generated temporary directory

igd_id

Option to provide a specified ID for upload. If none provided then will use the next ieu-a batch ID

Returns

new ObtainEbiDataset object

Method is_new_id()

Check if the specified ID is unique within the database. It checks published GWASs and those currently being processed

Usage
Dataset$is_new_id(id = self$igd_id)
Arguments
id

ID to check

Method delete_wd()

Delete working directory

Usage
Dataset$delete_wd()

Method set_wd()

Set working directory (creates)

Usage
Dataset$set_wd(wd)
Arguments
wd

working directory

Method se_from_bp()

Estimate standard error from beta and p-value

Usage
Dataset$se_from_bp(beta, pval, minp = 1e-300)
Arguments
beta

Effect size

pval

p-value

minp

Minimum p-value cutoff default = 1e-300

Method determine_columns()

Specify which columns in the dataset correspond to which fields.

Usage
Dataset$determine_columns(params, nrows = 100, gwas_file = self$filename, ...)
Arguments
params

List of column identifiers. Identifiers can be numeric position or column header name. Required columns are: c("chr_col", "pos_col", "ea_col", "oa_col", "beta_col", "se_col", "pval_col","rsid_col"). Optional columns are: c("snp_col", "eaf_col", "oaf_col", "ncase_col", "imp_z_col", "imp_info_col", "ncontrol_col").

nrows

How many rows to read to check that parameters have been specified correctly

gwas_file

Filename to read

...

Further arguments to pass to data.table::fread in order to correctly read the dataset

Method format_dataset()

Process dataset ready for uploading. Determins build and lifts over to hg19/b37 if necessary.

Usage
Dataset$format_dataset(
  gwas_file = self$filename,
  gwas_out = file.path(self$wd, "format.txt.gz"),
  params = self$params,
  metadata_test = self$metadata_test,
  ...
)
Arguments
gwas_file

GWAS filename

gwas_out

Filename to save processed dataset to

params

Column specifications (see determine_columns for more info)

metadata_test

List of outputs from tests of the effect allele, effect allele frequency columns and summary data using CheckSumStats

...

Further arguments to pass to data.table::fread in order to correctly read the dataset

Method view_metadata_options()

View the specifications for available meta data fields, as taken from https://api.opengwas.io/api/docs

Usage
Dataset$view_metadata_options()

Method get_gwasdata_fields()

Get a list of GWAS data fields and whether or not they are required

Usage
Dataset$get_gwasdata_fields()
Returns

data.frame

Method get_metadata_fields()

Get a list of metadata fields and whether or not they are required

Usage
Dataset$get_metadata_fields()
Returns

data.frame

Method collect_metadata()

Input metadata

Usage
Dataset$collect_metadata(metadata, igd_id = self$igd_id)
Arguments
metadata

List of meta-data fields and their values, see view_metadata_options for which fields need to be inputted.

igd_id

ID to be used for uploading to the database

Method check_meta_data()

Check that the reported effect allele and effect allele frequency columns are correct.

Usage
Dataset$check_meta_data(
  gwas_file = self$filename,
  params = self$params,
  metadata = self$metadata
)
Arguments
gwas_file

Filename to read

params

column names from x$determine_columns(). Required columns are: c("snp_col", "ea_col", "oa_col", "eaf_col" )

metadata

metadata from x$collect_metadata()

Method write_metadata()

Write meta data to json file

Usage
Dataset$write_metadata(
  metadata = self$metadata,
  datainfo = self$datainfo,
  outdir = self$wd
)
Arguments
metadata

List of meta data fields and their values

datainfo

List of data column parameters

outdir

Output directory to write json files

Method api_metadata_upload()

Upload meta data to API

Usage
Dataset$api_metadata_upload(
  metadata = self$metadata,
  metadata_test = self$metadata_test,
  access_token = ieugwasr::check_access_token()
)
Arguments
metadata

List of meta data fields and their values

metadata_test

List of outputs from tests of the effect allele, effect allele frequency columns and summary data using CheckSumStats

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_metadata_edit()

Upload meta data to API

Usage
Dataset$api_metadata_edit(
  metadata = self$metadata,
  access_token = ieugwasr::check_access_token()
)
Arguments
metadata

List of meta data fields and their values

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_metadata_check()

View meta-data

Usage
Dataset$api_metadata_check(
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
id

ID to check

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_metadata_delete()

Delete a dataset. This deletes the metadata AND any uploaded GWAS data (and related processing files)

Usage
Dataset$api_metadata_delete(
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
id

ID to delete

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_gwasdata_upload()

Upload gwas dataset

Usage
Dataset$api_gwasdata_upload(
  datainfo = self$datainfo,
  gwasfile = self$gwas_out,
  metadata_test = self$metadata_test,
  access_token = ieugwasr::check_access_token()
)
Arguments
datainfo

List of data column parameters

gwasfile

Path to processed gwasfile

metadata_test

List of outputs from tests of the effect allele, effect allele frequency columns and summary data using CheckSumStats

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_gwasdata_check()

Check status of API processing pipeline

Usage
Dataset$api_gwasdata_check(
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
id

ID to check

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_gwasdata_delete()

Delete a dataset. This deletes the metadata AND any uploaded GWAS data (and related processing files)

Usage
Dataset$api_gwasdata_delete(
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
id

ID to delete

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_qc_status()

Check the status of the GWAS QC processing pipeline

Usage
Dataset$api_qc_status(
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
id

ID to delete

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_report()

View the html report for a processed dataset

Usage
Dataset$api_report(
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
id

ID of report to view

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method api_gwas_release()

Release a dataset

Usage
Dataset$api_gwas_release(
  comments = NULL,
  passed_qc = "True",
  id = self$igd_id,
  access_token = ieugwasr::check_access_token()
)
Arguments
comments

Optional comments to provide when uploading

passed_qc

True or False

id

ID to release

access_token

Google OAuth2.0 token. See ieugwasr documentation for more info

Method clone()

The objects of this class are cloneable with this method.

Usage
Dataset$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Determine build based on reference dataset

Description

Determine build based on reference dataset

Usage

determine_build(rsid, chr, pos, build = c(37, 38, 36), fallback = "position")

Arguments

rsid

rsid
chr

chr
pos

pos
build

Builds to try e.g. c(37,38,36)
fallback

Whether to try "position" (fast) or "biomart" (more accurate if you have rsids) based approaches instead

Value

build if detected, or dataframe of matches if not

Determines which build a set of SNPs are on

Description

Determines which build a set of SNPs are on

Usage

determine_build_biomart(rsid, chr, pos, build = c(37, 38, 36))

Arguments

rsid

rsid
chr

chr
pos

pos
build

Builds to try e.g. c(37,38,36)

Value

build if detected, or dataframe of matches if not

Determine build just from positions

Description

A bit sketchy but computationally fast - just assumes that there will be at least 50x more position matches in the true build than either of the others.

Usage

determine_build_position(pos, build = c(37, 38, 36))

Arguments

pos

Vector of positions
build

c(37,38,36)

Value

build or if not determined then dataframe

determine_new_datasets

Description

Figure out which datasets are not present in the database

Usage

determine_new_datasets(
  ebi_ftp_url = options()$ebi_ftp_url,
  blacklist = NULL,
  exclude_multi_datasets = TRUE
)

Arguments

ebi_ftp_url

FTP url default=options()$ebi_ftp_url
blacklist

List of EBI datasets to ignore default=NULL
exclude_multi_datasets

If a EBI ID has more than one dataset then should it be ignored

Value

data frame

ebi_datasets

Description

Convert output from listftp into something that is easier to read

Usage

ebi_datasets(ebi_ftp_url = options()$ebi_ftp_url)

Arguments

ebi_ftp_url

EBI FTP default=options()$ebi_ftp_url

Value

data frame

Object that downloads, develops and uploads EBI dataset

Description

Object that downloads, develops and uploads EBI dataset

Object that downloads, develops and uploads EBI dataset

Super class

GwasDataImport::Dataset -> EbiDataset

Public fields

ebi_id

EBI ID to look for

traitname

Name of trait

ftp_path

Path to files in EBI FTP

or_flag

TRUE/FALSE if had to convert OR to beta

gwas_out1

Path to first look at EBI dataset

Methods

Public methods

Inherited methods

Method new()

Initialise object

Usage
EbiDataset$new(
  ebi_id,
  wd = tempdir(),
  ftp_path = NULL,
  igd_id = paste0("ebi-a-", ebi_id),
  traitname = NULL
)
Arguments
ebi_id

e.g. GCST005522

wd

Directory in which to download and develop dataset. Default=tempdir(). Deleted automatically upon object removal

ftp_path

Pre-specified path to data. Default=NULL

igd_id

Defaults to "ebi-a-<ebi_id>"

traitname

Option to provide traitname of dataset

Returns

A new EbiDataset object

Method download_dataset()

Download

Usage
EbiDataset$download_dataset(
  ftp_path = self$ftp_path,
  ftp_url = options()$ebi_ftp_url,
  outdir = self$wd
)
Arguments
ftp_path

Pre-specified path to data. Default=self$ftp_path

ftp_url

Default=options()$ebi_ftp_url

outdir

Default=self$wd

Method format_ebi_dataset()

organise data before formatting. This is slow but doesn't really matter

Usage
EbiDataset$format_ebi_dataset(
  filename = self$filename,
  output = file.path(self$wd, "step1.txt.gz")
)
Arguments
filename

Filename of GWAS dataset

output

Where to save formatted dataset

Method organise_metadata()

Download and parse metadata

Usage
EbiDataset$organise_metadata(
  ebi_id = self$ebi_id,
  or_flag = self$or_flag,
  igd_id = self$igd_id,
  units = NULL,
  sex = "NA",
  category = "NA",
  subcategory = "NA",
  build = "HG19/GRCh37",
  group_name = "public",
  traitname = self$traitname
)
Arguments
ebi_id

Default=self$ebi_id

or_flag

Default=self$or_flag

igd_id

Default=NULL

units

Default=NULL

sex

Default="NA"

category

Default="NA"

subcategory

Default="NA"

build

Default="HG19/GRCh37"

group_name

Default="public"

traitname

Default=self$traitname

Method pipeline()

Once initialised this function will string together everything i.e. downloading, processing and uploading

Usage
EbiDataset$pipeline()

Method clone()

The objects of this class are cloneable with this method.

Usage
EbiDataset$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Get harmonised file for specific EBI ID

Description

Get harmonised file for specific EBI ID

Usage

get_ftp_path(ebi_id, ebi_ftp_url = options()$ebi_ftp_url)

Arguments

ebi_id

EBI ID e.g. GCST000879
ebi_ftp_url

EBI FTP default=options()$ebi_ftp_url

Value

ftp path (excluding server)

Lookup positions for given rsids in particular build

Description

Lookup positions for given rsids in particular build

Usage

get_positions(
  rsid,
  build = 37,
  method = c("opengwas", "biomart")[1],
  splitsize = 50000
)

Arguments

rsid

rsid
build

build (36, 37 default or 38)
method

"opengwas" (fastest) or "biomart"
splitsize

Default 50000

Value

data frame

Liftover GWAS positions

Description

Determine GWAS build and liftover to required build

Usage

liftover_gwas(
  dat,
  build = c(37, 38, 36),
  to = 37,
  chr_col = "chr",
  pos_col = "pos",
  snp_col = "snp",
  ea_col = "ea",
  oa_col = "oa",
  build_fallback = "position"
)

Arguments

dat

Data frame with chr, pos, snp name, effect allele, non-effect allele columns
build

The possible builds to check data against Default = c(37,38,26)
to

Which build to lift over to. Default=37
chr_col

Name of chromosome column name. Required
pos_col

Name of position column name. Required
snp_col

Name of SNP column name. Optional. Uses less certain method of matching if not available
ea_col

Name of effect allele column name. Optional. Might lead to duplicated rows if not presented
oa_col

Name of other allele column name. Optional. Might lead to duplicated rows if not presented
build_fallback

Whether to try "position" (fast) or "biomart" (more accurate if you have rsids) based approaches instead

Value

Data frame

listftp

Description

List all files on the EBI FTP server

Usage

listftp(url = options()$ebi_ftp_url, recursive = TRUE)

Arguments

url

FTP url to look up
recursive

If false then just the top directory, otherwise list recursively

Value

Vector of paths