Import full genome-wide GWAS summary statistics from Open GWAS

Source: R/import_sumstats.R

Requires internet access to run.

import_sumstats(
  ids,
  vcf_dir = tempdir(),
  vcf_download = TRUE,
  save_dir = tempdir(),
  write_vcf = FALSE,
  download_method = "download.file",
  quiet = TRUE,
  force_new = FALSE,
  force_new_vcf = FALSE,
  nThread = 1,
  parallel_across_ids = FALSE,
  ...
)

Arguments

ids

List of Open GWAS study IDs (e.g. c("prot-a-664", "ieu-b-4760")).

vcf_dir

Where to download the original VCF from Open GWAS. WARNING: This is set to tempdir() by default. This means the raw (pre-formatted) VCFs be deleted upon ending the R session. Change this to keep the raw VCF file on disk (e.g. vcf_dir="./raw_vcf").

vcf_download

Download the original VCF from Open GWAS.

save_dir

Directory to save formatted summary statistics in.

write_vcf

Whether to write as VCF (TRUE) or tabular file (FALSE).

download_method

"axel" (multi-threaded) or "download.file" (single-threaded) .

quiet

Run quietly.

force_new

If a formatted file of the same names as save_path exists, formatting will be skipped and this file will be imported instead (default). Set force_new=TRUE to override this.

force_new_vcf

Overwrite a previously downloaded VCF with the same path name.

nThread

Number of threads to use for parallel processes.

parallel_across_ids

If parallel_across_ids=TRUE and nThread>1, then each ID in ids will be processed in parallel.

...

Arguments passed on to format_sumstats

path

Filepath for the summary statistics file to be formatted. A dataframe or datatable of the summary statistics file can also be passed directly to MungeSumstats using the path parameter.

ref_genome

name of the reference genome used for the GWAS ("GRCh37" or "GRCh38"). Argument is case-insensitive. Default is NULL which infers the reference genome from the data.

convert_ref_genome

name of the reference genome to convert to ("GRCh37" or "GRCh38"). This will only occur if the current genome build does not match. Default is not to convert the genome build (NULL).

chain_source

source of the chain file to use in liftover, if converting genome build ("ucsc" or "ensembl"). Note that the UCSC chain files require a license for commercial use. The Ensembl chain is used by default ("ensembl").

convert_small_p

Binary, should non-negative p-values <= 5e-324 be converted to 0? Small p-values pass the R limit and can cause errors with LDSC/MAGMA and should be converted. Default is TRUE.

convert_large_p

Binary, should p-values >1 be converted to 1? P-values >1 should not be possible and can cause errors with LDSC/MAGMA and should be converted. Default is TRUE.

convert_neg_p

Binary, should p-values <0 be converted to 0? Negative p-values should not be possible and can cause errors with LDSC/MAGMA and should be converted. Default is TRUE.

compute_z

Whether to compute Z-score column. Default is FALSE. This can be computed from Beta and SE with (Beta/SE) or P (Z:=sign(BETA)*sqrt(stats::qchisq(P,1,lower=FALSE))). Note that imputing the Z-score from P for every SNP will not be perfectly correct and may result in a loss of power. This should only be done as a last resort. Use 'BETA' to impute by BETA/SE and 'P' to impute by SNP p-value.

force_new_z

When a "Z" column already exists, it will be used by default. To override and compute a new Z-score column from P set force_new_z=TRUE.

compute_n

Whether to impute N. Default of 0 won't impute, any other integer will be imputed as the N (sample size) for every SNP in the dataset. Note that imputing the sample size for every SNP is not correct and should only be done as a last resort. N can also be inputted with "ldsc", "sum", "giant" or "metal" by passing one of these for this field or a vector of multiple. Sum and an integer value creates an N column in the output whereas giant, metal or ldsc create an Neff or effective sample size. If multiples are passed, the formula used to derive it will be indicated.

convert_n_int

Binary, if N (the number of samples) is not an integer, should this be rounded? Default is TRUE.

impute_beta

Binary, whether BETA should be imputed using other effect data if it isn't present in the sumstats. Note that this imputation is an approximation (for Z & SE approach) so could have an effect on downstream analysis. Use with caution. The different methods MungeSumstats will try and impute beta (in this order or priority) are:

  1. log(OR) 2. Z x SE Default value is FALSE.

es_is_beta

Binary, whether to map ES to BETA. We take BETA to be any BETA-like value (including Effect Size). If this is not the case for your sumstats, change this to FALSE. Default is TRUE.

impute_se

Binary, whether the standard error should be imputed using other effect data if it isn't present in the sumstats. Note that this imputation is an approximation so could have an effect on downstream analysis. Use with caution. The different methods MungeSumstats will try and impute se (in this order or priority) are:

  1. BETA / Z 2. abs(BETA/ qnorm(P/2)) Default is FALSE.

analysis_trait

If multiple traits were studied, name of the trait for analysis from the GWAS. Default is NULL.

INFO_filter

numeric The minimum value permissible of the imputation information score (if present in sumstats file). Default 0.9.

FRQ_filter

numeric The minimum value permissible of the frequency(FRQ) of the SNP (i.e. Allele Frequency (AF)) (if present in sumstats file). By default no filtering is done, i.e. value of 0.

pos_se

Binary Should the standard Error (SE) column be checked to ensure it is greater than 0? Those that are, are removed (if present in sumstats file). Default TRUE.

effect_columns_nonzero

Binary should the effect columns in the data BETA,OR (odds ratio),LOG_ODDS,SIGNED_SUMSTAT be checked to ensure no SNP=0. Those that do are removed(if present in sumstats file). Default FALSE.

N_std

numeric The number of standard deviations above the mean a SNP's N is needed to be removed. Default is 5.

N_dropNA

Drop rows where N is missing.Default is TRUE.

rmv_chr

vector or character The chromosomes on which the SNPs should be removed. Use NULL if no filtering necessary. Default is X, Y and mitochondrial.

rmv_chrPrefix

Remove "chr" or "CHR" from chromosome names. Default is TRUE.

on_ref_genome

Binary Should a check take place that all SNPs are on the reference genome by SNP ID. Default is TRUE.

strand_ambig_filter

Binary Should SNPs with strand-ambiguous alleles be removed. Default is FALSE.

allele_flip_check

Binary Should the allele columns be checked against reference genome to infer if flipping is necessary. Default is TRUE.

allele_flip_drop

Binary Should the SNPs for which neither their A1 or A2 base pair values match a reference genome be dropped. Default is TRUE.

allele_flip_z

Binary should the Z-score be flipped along with effect and FRQ columns like Beta? It is assumed to be calculated off the effect size not the P-value and so will be flipped i.e. default TRUE.

allele_flip_frq

Binary should the frequency (FRQ) column be flipped along with effect and z-score columns like Beta? Default TRUE.

bi_allelic_filter

Binary Should non-biallelic SNPs be removed. Default is TRUE.

snp_ids_are_rs_ids

Binary Should the supplied SNP ID's be assumed to be RSIDs. If not, imputation using the SNP ID for other columns like base-pair position or chromosome will not be possible. If set to FALSE, the SNP RS ID will be imputed from the reference genome if possible. Default is TRUE.

remove_multi_rs_snp

Binary Sometimes summary statistics can have multiple RSIDs on one row (i.e. related to one SNP), for example "rs5772025_rs397784053". This can cause an error so by default, the first RS ID will be kept and the rest removed e.g."rs5772025". If you want to just remove these SNPs entirely, set it to TRUE. Default is FALSE.

frq_is_maf

Conventionally the FRQ column is intended to show the minor/effect allele frequency (MAF) but sometimes the major allele frequency can be inferred as the FRQ column. This logical variable indicates that the FRQ column should be renamed to MAJOR_ALLELE_FRQ if the frequency values appear to relate to the major allele i.e. >0.5. By default this mapping won't occur i.e. is TRUE.

indels

Binary does your Sumstats file contain Indels? These don't exist in our reference file so they will be excluded from checks if this value is TRUE. Default is TRUE.

dbSNP

version of dbSNP to be used for imputation (144 or 155).

check_dups

whether to check for duplicates - if formatting QTL datasets this should be set to FALSE otherwise keep as TRUE. Default is TRUE.

sort_coordinates

Whether to sort by coordinates of resulting sumstats

save_path

File path to save formatted data. Defaults to tempfile(fileext=".tsv.gz").

tabix_index

Index the formatted summary statistics with tabix for fast querying.

return_data

Return data.table, GRanges or VRanges directly to user. Otherwise, return the path to the save data. Default is FALSE.

return_format

If return_data is TRUE. Object type to be returned ("data.table","vranges","granges").

ldsc_format

DEPRECATED, do not use. Use save_format="LDSC" instead.

save_format

Output format of sumstats. Options are NULL - standardised output format from MungeSumstats, LDSC - output format compatible with LDSC and openGWAS - output compatible with openGWAS VCFs. Default is NULL.

log_folder_ind

Binary Should log files be stored containing all filtered out SNPs (separate file per filter). The data is outputted in the same format specified for the resulting sumstats file. The only exception to this rule is if output is vcf, then log file saved as .tsv.gz. Default is FALSE.

log_mungesumstats_msgs

Binary Should a log be stored containing all messages and errors printed by MungeSumstats in a run. Default is FALSE

log_folder

Filepath to the directory for the log files and the log of MungeSumstats messages to be stored. Default is a temporary directory. Note the name of the log files (log messages and log outputs) are now the same as the name of the file specified in the save path parameter with the extension '_log_msg.txt' and '_log_output.txt' respectively.

imputation_ind

Binary Should a column be added for each imputation step to show what SNPs have imputed values for differing fields. This includes a field denoting SNP allele flipping (flipped). On the flipped value, this denoted whether the alelles where switched based on MungeSumstats initial choice of A1, A2 from the input column headers and thus may not align with what the creator intended.Note these columns will be in the formatted summary statistics returned. Default is FALSE.

mapping_file

MungeSumstats has a pre-defined column-name mapping file which should cover the most common column headers and their interpretations. However, if a column header that is in youf file is missing of the mapping we give is incorrect you can supply your own mapping file. Must be a 2 column dataframe with column names "Uncorrected" and "Corrected". See data(sumstatsColHeaders) for default mapping and necessary format.

Value

Either a named list of data objects or paths, depending on the arguments passed to format_sumstats.

Examples

#only run the examples if user has internet access:
if(try(is.character(getURL("www.google.com")))==TRUE){
### Search by criteria
metagwas <- find_sumstats(
    traits = c("parkinson", "alzheimer"),
    min_sample_size = 5000
)
### Only use a subset for testing purposes
ids <- (dplyr::arrange(metagwas, nsnp))$id

### Default usage
## You can supply \code{import_sumstats()}
## with a list of as many OpenGWAS IDs as you want,
## but we'll just give one to save time.

## Call uses reference genome as default with more than 2GB of memory,
## which is more than what 32-bit Windows can handle so remove certain checks
## commented out down to runtime
# datasets <- import_sumstats(ids = ids[1])
}
#> Error in getURL("www.google.com") : could not find function "getURL"