The IEU GWAS database comprises over 10,000 curated, QC’d and harmonised complete GWAS summary datasets and can be queried using an API. See here for documentation on the API itself. This R package is a wrapper to make generic calls to the API, plus convenience functions for specific queries.

Authentication

Most datasets in the database are public and don’t need authentication. But if you want to access a private dataset that is linked to your (gmail) email address, you need to authenticate the query using a method known as Google OAuth2.0.

Essentially - you run this command at the start of your session:

ieugwasr::get_access_token()

which will open up a web browser asking you to provide your google username and password, and upon doing so a directory will be created in your working directory called ieugwasr_oauth. This directory contains a file that looks like this: <random_string>_<email@address>. It is a binary file (not human readable), which contains your access token, and it acts as a convenient way to hold a randomly generated password.

If you are using a server which doesn’t have a graphic user interface then the ieugwasr::get_access_token() method is not going to work. You need to generate the ieugwasr_oauth directory and token file on a computer that has a web browser, and then copy that directory (containing the token file) to your server (to the relevant work directory).

If you are using R in a working directory that does not have write permissions then this command will fail, please navigate to a directory that does have write permissions.

If you need to run this in a non-interactive script then you can generate the token file on an interactive computer, copy that file to the working directory that R will be running from, and then run a batch (non-interactive).

You can test to see if you have authenticated using the function

ieugwasr::check_access_token()

It will return NULL if you are not authenticated, or a long random token string if you are.

To unauthenticate, simply delete the relevant file in the ieugwasr_oauth folder, or delete the folder entirely.

General API queries

The API has a number of endpoints documented here. A general way to access them in R is using the api_query function. There are two types of endpoints - GET and POST.

• GET - you provide a single URL which includes the endpoint and query. For example, for the association endpoint you can obtain some rsids in some studies, e.g.
• api_query("associations/ieu-a-2,ieu-a-7/rs234,rs123")
• POST - Here you send a “payload” to the endpoint. So, the path specifies the endpoint and you add a list of query specifications. This is useful for long lists of rsids being queried, for example
• api_query("associations", query=list(rsid=c("rs234", "rs123"), id=c("ieu-a-2", "ieu-a-7")))

The api_query function returns a response object from the httr package. See below for a list of functions that make the input and output to api_query more convenient.

Get API status

api_status()

Get list of all available studies

gwasinfo()

Get list of a specific study

gwasinfo("ieu-a-2")

Extract particular associations from particular studies

Provide a list of variants to be obtained from a list of studies:

associations(variants=c("rs123", "7:105561135"), id=c("ieu-a-2", "ieu-a-7"))

By default this will look for LD proxies using 1000 genomes reference data (Europeans only, the reference panel has INDELs removed and only retains SNPs with MAF > 0.01). This behaviour can be turned off using proxies=0 as an argument.

Note that the queries are performed on rsids, but chromosome:position values will be automatically converted. A range query can be done using e.g.

associations(variants="7:105561135-105563135", id=c("ieu-a-2"), proxies=0)

Get the tophits from a study

The tophits can be obtained using

tophits(id="ieu-a-2")

Note that it will perform strict clumping by default (r2 = 0.001 and radius = 10000kb). This can be turned off with clump=0.

Perform PheWAS

Lookup association of specified variants across every study, returning at a particular threshold. Note that no LD proxy lookups are made here.

phewas(variants="rs1205", pval=1e-5)

PheWAS can also be performed in only specific subsets of the data. The datasets in the IGD are organised by batch, you can see info about it here: https://gwas.mrcieu.ac.uk/datasets/ or get a list of batches and their descriptions using:

batches()

You can perform PheWAS in only specified batches using:

phewas(variants="rs1205", pval=1e-5, batch=c('ieu-a', 'ukb-b'))

By default PheWAS is performed in all batches (which is of course somewhat slower).

LD clumping

The API has a wrapper around plink version 1.90 and can use it to perform clumping with an LD reference panel from 1000 genomes reference data.

a <- tophits(id="ieu-a-2", clump=0)
b <- ld_clump(
dplyr::tibble(rsid=a$name, pval=a$p, id=a$id) ) There are 5 super-populations that can be requested via the pop argument. By default this will use the Europeans subset (EUR super-population). The reference panel has INDELs removed and only retains SNPs with MAF > 0.01 in the selected population. Note that you can perform the same operation locally if you provide a path to plink and a bed/bim/fam LD reference dataset. e.g. ld_clump( dplyr::tibble(rsid=a$name, pval=a$p, id=a$id),
bfile = "/path/to/reference_data"
)

LD matrix

Similarly, a matrix of LD r values can be generated using

ld_matrix(b$variant) This uses the API by default but is limited to only 500 variants. You can use, instead, local plink and LD reference data in the same manner as in the ld_clump function, e.g. ld_matrix(b$variant, plink_bin = "/path/to/plink", bfile = "/path/to/reference_data")

There are 5 super-populations that can be requested via the pop argument. By default this will use the Europeans subset (EUR super-population). The reference panel has INDELs removed and only retains SNPs with MAF > 0.01 in the selected population.

Super-populations:

• EUR = European
• EAS = East Asian
• SAS = South Asian
• AFR = African

Variant information

Translating between rsids and chromosome:position, while also getting other information, can be achieved.

The chrpos argument can accept the following

• <chr>:<position>
• <chr>:<start>-<end>

For example

a <- variants_chrpos(c("7:105561135-105563135", "10:44865737"))

This provides a table with dbSNP variant IDs, gene info, and various other metadata. Similar data can be obtained from searching by rsid

b <- variants_rsid(c("rs234", "rs333"))

And a list of variants within a particular gene region can also be found. Provide a ensembl or entrez gene ID (e.g. ENSG00000123374 or 1017) to the following:

c <- variants_gene("ENSG00000123374")

Extracting GWAS summary data based on gene region

Here is an example of how to obtain summary data for some datasets for a gene region. As an example, we’ll extract CDK2 (HGNC number 1017) from a BMI dataset (ieu-a-2)

Use the mygene bioconductor package to query the mygene.info API.

library(mygene)
a <- mygene::getGene("1017", fields="genomic_pos_hg19")
r <- paste0(a[[1]]$genomic_pos_hg19$chr, ":", a[[1]]$genomic_pos_hg19$start, "-", a[[1]]$genomic_pos_hg19$end)
b <- ieugwasr::associations(r, "ieu-a-2")

1000 genomes annotations

The OpenGWAS database contains a database of population annotations from the 1000 genomes project - the alternative allele frequencies and the LD scores for each variant, calculated for each super population separately. Only variants are present if they are MAF > 1% in at least one super population. You can access this info in different ways

1. Look up a particular set of rsids

ieugwasr::afl2_rsid(c("rs234", "rs123"))
2. Look up a set of positions or regions

ieugwasr::afl2_chrpos("1:100000-900000")
3. Extract annotations for a list of 20k variants that are common in all super populations, and evenly spaced across the genome

ieugwasr::afl2_list()
4. Extract annotations for a 1.3 million HapMap3 variants

ieugwasr::afl2_list("hapmap3")
5. Infer the ancestry of a particular study by comparing the allele frequencies with different super population reference frequencies

snplist <- ieugwasr::afl2_list()
eur_example <- associations(snplist$rsid, "ieu-a-2") ieugwasr::infer_ancestry(eur_example, snplist) eas_example <- associations(snplist$rsid, "bbj-a-10")
ieugwasr::infer_ancestry(eur_example, snplist)