Package 'MungeSumstats'

Title: Standardise summary statistics from GWAS
Description: The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences.
Authors: Alan Murphy [aut, cre] , Brian Schilder [aut, ctb] , Nathan Skene [aut]
Maintainer: Alan Murphy <[email protected]>
License: Artistic-2.0
Version: 1.15.2
Built: 2024-11-08 16:59:28 UTC
Source: https://github.com/bioc/MungeSumstats

Help Index

Ensures that parameters are compatible with LDSC format

Description

Format summary statistics for direct input to Linkage Disequilibrium SCore (LDSC) regression without the need to use their munge_sumstats.py script first.

Usage

check_ldsc_format(
  sumstats_dt,
  save_format,
  convert_n_int,
  allele_flip_check,
  compute_z,
  compute_n
)

Arguments

sumstats_dt

data table obj of the summary statistics file for the GWAS.
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. NOTE - If LDSC format is used, the naming convention of A1 as the reference (genome build) allele and A2 as the effect allele will be reversed to match LDSC (A1 will now be the effect allele). See more info on this here. Note that any effect columns (e.g. Z) will be inrelation to A1 now instead of A2.
convert_n_int

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

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

Details

LDSC documentation.

Value

Formatted summary statistics

Source

LDSC GitHub

Check for N column if not present and user wants, impute N based on user's sample size. NOTE this will be the same value for each SNP which is not necessarily correct and may cause issues down the line. N can also be inputted with "ldsc", "sum", "giant" or "metal" by passing one or multiple of these.

Description

Check for N column if not present and user wants, impute N based on user's sample size. NOTE this will be the same value for each SNP which is not necessarily correct and may cause issues down the line. N can also be inputted with "ldsc", "sum", "giant" or "metal" by passing one or multiple of these.

Usage

compute_nsize(
  sumstats_dt,
  imputation_ind = FALSE,
  compute_n = c("ldsc", "giant", "metal", "sum"),
  standardise_headers = FALSE,
  force_new = FALSE,
  return_list = TRUE
)

Arguments

sumstats_dt

data table obj of the summary statistics file for the GWAS.
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). Note these columns will be in the formatted summary statistics returned. Default is FALSE.
compute_n

How to compute per-SNP sample size (new column "N").

  • 0: N will not be computed.

  • >0: If any number >0 is provided, that value will be set as N for every row. Note: Computing N this way is incorrect and should be avoided if at all possible.

  • "sum": N will be computed as: cases (N_CAS) + controls (N_CON), so long as both columns are present.

  • "ldsc": N will be computed as effective sample size: Neff =(N_CAS+N_CON)*(N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))(N_CAS+N_CON)==max(N_CAS+N_CON)).

  • "giant": N will be computed as effective sample size: Neff = 2 / (1/N_CAS + 1/N_CON).

  • "metal": N will be computed as effective sample size: Neff = 4 / (1/N_CAS + 1/N_CON).

standardise_headers

Standardise headers first.
force_new

If "Neff" (or "N") already exists in sumstats_dt, replace it with the recomputed version.
return_list

Return the sumstats_dt within a named list (default: TRUE).

Value

list("sumstats_dt"=sumstats_dt)

Examples

sumstats_dt <- MungeSumstats::formatted_example()
sumstats_dt2 <- MungeSumstats::compute_nsize(sumstats_dt=sumstats_dt,
                                             compute_n=10000)

Download VCF file and its index file from Open GWAS

Description

Ideally, we would use gwasvcf instead but it hasn't been made available on CRAN or Bioconductor yet, so we can't include it as a dep.

Usage

download_vcf(
  vcf_url,
  vcf_dir = tempdir(),
  vcf_download = TRUE,
  download_method = "download.file",
  force_new = FALSE,
  quiet = FALSE,
  timeout = 10 * 60,
  nThread = 1
)

Arguments

vcf_url

Remote URL to VCF file.
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.
download_method

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

Overwrite a previously downloaded VCF with the same path name.
quiet

Run quietly.
timeout

How many seconds before giving up on download. Passed to download.file. Default: 10*60 (10min).
nThread

Number of threads to parallelize over.

Value

List containing the paths to the downloaded VCF and its index file.

Examples

#only run the examples if user has internet access:
if(try(is.character(getURL("www.google.com")))==TRUE){
vcf_url <- "https://gwas.mrcieu.ac.uk/files/ieu-a-298/ieu-a-298.vcf.gz"
out_paths <- download_vcf(vcf_url = vcf_url)
}

Search Open GWAS for datasets matching criteria

Description

For each argument, searches for any datasets matching a case-insensitive substring search in the respective metadata column. Users can supply a single character string or a list/vector of character strings.

Usage

find_sumstats(
  ids = NULL,
  traits = NULL,
  years = NULL,
  consortia = NULL,
  authors = NULL,
  populations = NULL,
  categories = NULL,
  subcategories = NULL,
  builds = NULL,
  pmids = NULL,
  min_sample_size = NULL,
  min_ncase = NULL,
  min_ncontrol = NULL,
  min_nsnp = NULL,
  include_NAs = FALSE,
  access_token = check_access_token()
)

Arguments

ids

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

List of traits (e.g. c("parkinson", "Alzheimer")).
years

List of years (e.g. seq(2015,2021) or c(2010, 2012, 2021)).
consortia

List of consortia (e.g. c("MRC-IEU","Neale Lab").
authors

List of authors (e.g. c("Elsworth","Kunkle","Neale")).
populations

List of populations (e.g. c("European","Asian")).
categories

List of categories (e.g. c("Binary","Continuous","Disease","Risk factor"))).
subcategories

List of categories (e.g. c("neurological","Immune","cardio"))).
builds

List of genome builds (e.g. c("hg19","grch37")).
pmids

List of PubMed ID (exact matches only) (e.g. c(29875488, 30305740, 28240269)).
min_sample_size

Minimum total number of study participants (e.g. 5000).
min_ncase

Minimum number of case participants (e.g. 1000).
min_ncontrol

Minimum number of control participants (e.g. 1000).
min_nsnp

Minimum number of SNPs (e.g. 200000).
include_NAs

Include datasets with missing metadata for size criteria (i.e. min_sample_size, min_ncase, or min_ncontrol).
access_token

Google OAuth2 access token. Used to authenticate level of access to data

Details

By default, returns metadata for all studies currently in Open GWAS database.

Value

(Filtered) GWAS metadata table.

Examples

# Only run the examples if user has internet access:
if(try(is.character(getURL("www.google.com")))==TRUE){
### By ID
metagwas <- find_sumstats(ids = c(
    "ieu-b-4760",
    "prot-a-1725",
    "prot-a-664"
))
### By ID amd sample size
metagwas <- find_sumstats(
    ids = c("ieu-b-4760", "prot-a-1725", "prot-a-664"),
    min_sample_size = 5000
)
### By criteria
metagwas <- find_sumstats(
    traits = c("alzheimer", "parkinson"),
    years = seq(2015, 2021)
)
}

Check that summary statistics from GWAS are in a homogeneous format

Description

Check that summary statistics from GWAS are in a homogeneous format

Usage

format_sumstats(
  path,
  ref_genome = NULL,
  convert_ref_genome = NULL,
  chain_source = "ensembl",
  local_chain = NULL,
  convert_small_p = TRUE,
  convert_large_p = TRUE,
  convert_neg_p = TRUE,
  compute_z = FALSE,
  force_new_z = FALSE,
  compute_n = 0L,
  convert_n_int = TRUE,
  impute_beta = FALSE,
  es_is_beta = TRUE,
  impute_se = FALSE,
  analysis_trait = NULL,
  ignore_multi_trait = FALSE,
  INFO_filter = 0.9,
  FRQ_filter = 0,
  pos_se = TRUE,
  effect_columns_nonzero = FALSE,
  N_std = 5,
  N_dropNA = TRUE,
  chr_style = "Ensembl",
  rmv_chr = c("X", "Y", "MT"),
  on_ref_genome = TRUE,
  infer_eff_direction = TRUE,
  eff_on_minor_alleles = FALSE,
  strand_ambig_filter = FALSE,
  allele_flip_check = TRUE,
  allele_flip_drop = TRUE,
  allele_flip_z = TRUE,
  allele_flip_frq = TRUE,
  bi_allelic_filter = TRUE,
  flip_frq_as_biallelic = FALSE,
  snp_ids_are_rs_ids = TRUE,
  remove_multi_rs_snp = FALSE,
  frq_is_maf = TRUE,
  indels = TRUE,
  drop_indels = FALSE,
  drop_na_cols = c("SNP", "CHR", "BP", "A1", "A2", "FRQ", "BETA", "Z", "OR", "LOG_ODDS",
    "SIGNED_SUMSTAT", "SE", "P", "N"),
  dbSNP = 155,
  check_dups = TRUE,
  sort_coordinates = TRUE,
  nThread = 1,
  save_path = tempfile(fileext = ".tsv.gz"),
  write_vcf = FALSE,
  tabix_index = FALSE,
  return_data = FALSE,
  return_format = "data.table",
  ldsc_format = FALSE,
  save_format = NULL,
  log_folder_ind = FALSE,
  log_mungesumstats_msgs = FALSE,
  log_folder = tempdir(),
  imputation_ind = FALSE,
  force_new = FALSE,
  mapping_file = sumstatsColHeaders,
  rmv_chrPrefix = NULL
)

Arguments

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").
local_chain

Path to local chain file to use instead of downlaoding. Default of NULL i.e. no local file to use. NOTE if passing a local chain file make sure to specify the path to convert from and to the correct build like GRCh37 to GRCh38. We can not sense check this for local files. The chain file can be submitted as a gz file (as downloaed from source) or unzipped.
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.
ignore_multi_trait

If you have multiple traits (p-values) in the study but you want to ignorwe these and instead use a standard named p-value, set to TRUE. By default is FALSE which will check for multi-traits.
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.
chr_style

Chromosome naming style to use in the formatted summary statistics file ("NCBI", "UCSC", "dbSNP", or "Ensembl"). The NCBI and Ensembl styles both code chromosomes as ⁠1-22, X, Y, MT⁠; the UCSC style is ⁠chr1-chr22, chrX, chrY, chrM⁠; and the dbSNP style is ⁠ch1-ch22, chX, chY, chMT⁠. Default is Ensembl.
rmv_chr

Chromosomes to exclude from the formatted summary statistics file. Use NULL if no filtering is necessary. Default is c("X", "Y", "MT") which removes all non-autosomal SNPs.
on_ref_genome

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

Binary Should a check take place to ensure the alleles match the effect direction? Default is TRUE.
eff_on_minor_alleles

Binary Should MungeSumstats assume that the effects are majoritively measured on the minor alleles? Default is FALSE as this is an assumption that won't be appropriate in all cases. However, the benefit is that if we know the majority of SNPs have their effects based on the minor alleles, we can catch cases where the allele columns have been mislabelled.
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-bi-allelic SNPs be removed. Default is TRUE.
flip_frq_as_biallelic

Binary Should non-bi-allelic SNPs frequency values be flipped as 1-p despite there being other alternative alleles? Default is FALSE but if set to TRUE, this allows non-bi-allelic SNPs to be kept despite needing flipping.
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.
drop_indels

Binary, should any indels found in the sumstats be dropped? These can not be checked against a reference dataset and will have the same RS ID and position as SNPs which can affect downstream analysis. Default is False.
drop_na_cols

A character vector of column names to be checked for missing values. Rows with missing values in any of these columns (if present in the dataset) will be dropped. If NULL, all columns will be checked for missing values. Default columns are SNP, chromosome, position, allele 1, allele2, effect columns (frequency, beta, Z-score, standard error, log odds, signed sumstats, odds ratio), p value and N columns.
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
nThread

Number of threads to use for parallel processes.
save_path

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

Whether to write as VCF (TRUE) or tabular file (FALSE).
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. NOTE - If LDSC format is used, the naming convention of A1 as the reference (genome build) allele and A2 as the effect allele will be reversed to match LDSC (A1 will now be the effect allele). See more info on this here. Note that any effect columns (e.g. Z) will be inrelation to A1 now instead of A2.
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.
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.
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.
rmv_chrPrefix

Is now deprecated, do. not use. Use chr_style instead - chr_style = 'Ensembl' will give the same result as rmv_chrPrefix=TRUE used to give.

Value

The address for the modified sumstats file or the actual data dependent on user choice. Also, if log files wanted by the user, the return in both above instances are a list.

Examples

# Pass path to Educational Attainment Okbay sumstat file to a temp directory

eduAttainOkbayPth <- system.file("extdata", "eduAttainOkbay.txt",
    package = "MungeSumstats"
)

## 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
## Using dbSNP = 144 for speed as it's smaller but you should use 155 unless
## you know what you are doing and need 144

is_32bit_windows <-
    .Platform$OS.type == "windows" && .Platform$r_arch == "i386"
if (!is_32bit_windows) {
    reformatted <- format_sumstats(
        path = eduAttainOkbayPth,
        ref_genome = "GRCh37",
        dbSNP = 144
    )
} else {
    reformatted <- format_sumstats(
        path = eduAttainOkbayPth,
        ref_genome = "GRCh37",
        on_ref_genome = FALSE,
        strand_ambig_filter = FALSE,
        bi_allelic_filter = FALSE,
        allele_flip_check = FALSE,
        dbSNP=144
    )
}
# returned location has the updated summary statistics file

Formatted example

Description

Returns an example of summary stats that have had their column names already standardised with standardise_header.

Usage

formatted_example(
  path = system.file("extdata", "eduAttainOkbay.txt", package = "MungeSumstats"),
  formatted = TRUE,
  sorted = TRUE
)

Arguments

path

Path to raw example file. Default to built-in dataset.
formatted

Whether the column names should be formatted (default:TRUE).
sorted

Whether the rows should be sorted by genomic coordinates (default:TRUE).

Value

sumstats_dt

Examples

sumstats_dt <- MungeSumstats::formatted_example()

Get combinations of uncorrected allele and effect (and frq) columns

Description

Get combinations of uncorrected allele and effect (and frq) columns

Usage

get_eff_frq_allele_combns(
  mapping_file = sumstatsColHeaders,
  eff_frq_cols = c("BETA", "OR", "LOG_ODDS", "SIGNED_SUMSTAT", "Z", "FRQ")
)

Arguments

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.
eff_frq_cols

Corrected effect or frequency column names found in a sumstats. Default of BETA, OR, LOG_ODDS, SIGNED_SUMSTAT, Z and FRQ.

Value

datatable containing uncorrected and corrected combinations

Infer genome builds

Description

Infers the genome build of summary statistics files (GRCh37 or GRCh38) from the data. Uses SNP (RSID) & CHR & BP to get genome build.

Usage

get_genome_builds(
  sumstats_list,
  header_only = TRUE,
  sampled_snps = 10000,
  names_from_paths = FALSE,
  dbSNP = 155,
  nThread = 1,
  chr_filt = NULL
)

Arguments

sumstats_list

A named list of paths to summary statistics, or a named list of data.table objects.
header_only

Instead of reading in the entire sumstats file, only read in the first N rows where N=sampled_snps. This should help speed up cases where you have to read in sumstats from disk each time.
sampled_snps

Downsample the number of SNPs used when inferring genome build to save time.
names_from_paths

Infer the name of each item in sumstats_list from its respective file path. Only works if sumstats_list is a list of paths.
dbSNP

version of dbSNP to be used (144 or 155). Default is 155.
nThread

Number of threads to use for parallel processes.
chr_filt

Internal for testing - filter reference genomes and sumstats to specific chromosomes for testing. Pass a list of chroms in format: c("1","2"). Default is NULL i.e. no filtering

Details

Iterative version of get_genome_build.

Value

ref_genome the genome build of the data

Examples

# Pass path to Educational Attainment Okbay sumstat file to a temp directory

eduAttainOkbayPth <- system.file("extdata", "eduAttainOkbay.txt",
    package = "MungeSumstats"
)
sumstats_list <- list(ss1 = eduAttainOkbayPth, ss2 = eduAttainOkbayPth)

## 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
is_32bit_windows <-
    .Platform$OS.type == "windows" && .Platform$r_arch == "i386"
if (!is_32bit_windows) {
    
    #multiple sumstats can be passed at once to get all their genome builds:
    #ref_genomes <- get_genome_builds(sumstats_list = sumstats_list)
    #just passing first here for speed
    sumstats_list_quick <- list(ss1 = eduAttainOkbayPth)
    ref_genomes <- get_genome_builds(sumstats_list = sumstats_list_quick,
                                     dbSNP=144)
}

UCSC Chain file hg19 to hg38

Description

UCSC Chain file hg19 to hg38, .chain.gz file, downloaded from https://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/ on 09/10/21

Format

gunzipped chain file

Details

UCSC Chain file hg19 to hg38, .chain.gz file, downloaded on 09/10/21 To be used as a back up if the download from UCSC fails.

hg19ToHg38.over.chain.gz

NA

Source

The chain file was downloaded from https://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/ utils::download.file('ftp://hgdownload.cse.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz',tempdir())

UCSC Chain file hg38 to hg19

Description

UCSC Chain file hg38 to hg19, .chain.gz file, downloaded from https://hgdownload.cse.ucsc.edu/goldenpath/hg19/liftOver/ on 09/10/21

Format

gunzipped chain file

Details

UCSC Chain file hg38 to hg19, .chain.gz file, downloaded on 09/10/21 To be used as a back up if the download from UCSC fails.

hg38ToHg19.over.chain.gz

NA

Source

The chain file was downloaded from https://hgdownload.cse.ucsc.edu/goldenpath/hg38/liftOver/ utils::download.file('ftp://hgdownload.cse.ucsc.edu/goldenPath/hg38/liftOver/hg38ToHg19.over.chain.gz',tempdir())

Local ieu-a-298 file from IEU Open GWAS

Description

Local ieu-a-298 file from IEU Open GWAS, downloaded on 09/10/21.

Format

gunzipped tsv file

Details

Local ieu-a-298 file from IEU Open GWAS, downlaoded on 09/10/21. This is done in case the download in the package vignette fails.

ieu-a-298.tsv.gz

NA

Source

The file was downloaded with: MungeSumstats::import_sumstats(ids = "ieu-a-298",ref_genome = "GRCH37")

Import full genome-wide GWAS summary statistics from Open GWAS

Description

Requires internet access to run.

Usage

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").

local_chain

Path to local chain file to use instead of downlaoding. Default of NULL i.e. no local file to use. NOTE if passing a local chain file make sure to specify the path to convert from and to the correct build like GRCh37 to GRCh38. We can not sense check this for local files. The chain file can be submitted as a gz file (as downloaed from source) or unzipped.

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.

ignore_multi_trait

If you have multiple traits (p-values) in the study but you want to ignorwe these and instead use a standard named p-value, set to TRUE. By default is FALSE which will check for multi-traits.

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.

chr_style

Chromosome naming style to use in the formatted summary statistics file ("NCBI", "UCSC", "dbSNP", or "Ensembl"). The NCBI and Ensembl styles both code chromosomes as ⁠1-22, X, Y, MT⁠; the UCSC style is ⁠chr1-chr22, chrX, chrY, chrM⁠; and the dbSNP style is ⁠ch1-ch22, chX, chY, chMT⁠. Default is Ensembl.

rmv_chrPrefix

Is now deprecated, do. not use. Use chr_style instead - chr_style = 'Ensembl' will give the same result as rmv_chrPrefix=TRUE used to give.

rmv_chr

Chromosomes to exclude from the formatted summary statistics file. Use NULL if no filtering is necessary. Default is c("X", "Y", "MT") which removes all non-autosomal SNPs.

on_ref_genome

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

infer_eff_direction

Binary Should a check take place to ensure the alleles match the effect direction? Default is TRUE.

eff_on_minor_alleles

Binary Should MungeSumstats assume that the effects are majoritively measured on the minor alleles? Default is FALSE as this is an assumption that won't be appropriate in all cases. However, the benefit is that if we know the majority of SNPs have their effects based on the minor alleles, we can catch cases where the allele columns have been mislabelled.

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-bi-allelic SNPs be removed. Default is TRUE.

flip_frq_as_biallelic

Binary Should non-bi-allelic SNPs frequency values be flipped as 1-p despite there being other alternative alleles? Default is FALSE but if set to TRUE, this allows non-bi-allelic SNPs to be kept despite needing flipping.

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.

drop_indels

Binary, should any indels found in the sumstats be dropped? These can not be checked against a reference dataset and will have the same RS ID and position as SNPs which can affect downstream analysis. Default is False.

drop_na_cols

A character vector of column names to be checked for missing values. Rows with missing values in any of these columns (if present in the dataset) will be dropped. If NULL, all columns will be checked for missing values. Default columns are SNP, chromosome, position, allele 1, allele2, effect columns (frequency, beta, Z-score, standard error, log odds, signed sumstats, odds ratio), p value and N columns.

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. NOTE - If LDSC format is used, the naming convention of A1 as the reference (genome build) allele and A2 as the effect allele will be reversed to match LDSC (A1 will now be the effect allele). See more info on this here. Note that any effect columns (e.g. Z) will be inrelation to A1 now instead of A2.

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])
}

Tabix-index file: table

Description

Convert summary stats file to tabix format.

Usage

index_tabular(
  path,
  chrom_col = "CHR",
  start_col = "BP",
  end_col = start_col,
  overwrite = TRUE,
  remove_tmp = TRUE,
  verbose = TRUE
)

Arguments

path

Path to GWAS summary statistics file.
chrom_col

Name of the chromosome column in sumstats_dt (e.g. "CHR").
start_col

Name of the starting genomic position column in sumstats_dt (e.g. "POS","start").
end_col

Name of the ending genomic position column in sumstats_dt (e.g. "POS","end"). Can be the same as start_col when sumstats_dt only contains SNPs that span 1 base pair (bp) each.
overwrite

A logical(1) indicating whether dest should be over-written, if it already exists.
remove_tmp

Remove the temporary uncompressed version of the file (.tsv).
verbose

Print messages.

Value

Path to tabix-indexed tabular file

Source

Borrowed function from echotabix.

See Also

Other tabix: index_vcf()

Examples

sumstats_dt <- MungeSumstats::formatted_example() 
path <- tempfile(fileext = ".tsv")
MungeSumstats::write_sumstats(sumstats_dt = sumstats_dt, save_path = path)
indexed_file <- MungeSumstats::index_tabular(path = path)

Infer if effect relates to a1 or A2 if ambiguously named

Description

Three checks are made to infer which allele the effect/frequency information relates to if they are ambiguous (named A0, A1 and A2 or equivalent):

  1. Check if ambiguous naming conventions are used (i.e. allele 0, 1 and 2 or equivalent). If not exit, otherwise continue to next checks. This can be checked by using the mapping file and splitting A1/A2 mappings by those that contain 0, 1 or 2 (ambiguous) or doesn't contain 0, 1 or 2 e.g. effect, tested (unambiguous so fine for MSS to handle as is).

  2. Look for effect column/frequency column where the A0/A1/A2 explicitly mentioned, if found then we know the direction and should update A0/A1/A2 naming so A2 is the effect column. We can look for such columns by getting every combination of A0/A1/A2 naming and effect/frq naming.

  3. If not found in 2, a final check should be against the reference genome, whichever of A0, A1 and A2 has more of a match with the reference genome should be taken as not the effect allele. There is an assumption in this but is still better than guessing the ambiguous allele naming.

Usage

infer_effect_column(
  sumstats_dt,
  dbSNP = 155,
  sampled_snps = 10000,
  mapping_file = sumstatsColHeaders,
  nThread = nThread,
  ref_genome = NULL,
  on_ref_genome = TRUE,
  infer_eff_direction = TRUE,
  eff_on_minor_alleles = FALSE,
  return_list = TRUE
)

Arguments

sumstats_dt

data table obj of the summary statistics file for the GWAS.
dbSNP

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

Downsample the number of SNPs used when inferring genome build to save time.
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.
nThread

Number of threads to use for parallel processes.
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.
on_ref_genome

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

Binary Should a check take place to ensure the alleles match the effect direction? Default is TRUE.
eff_on_minor_alleles

Binary Should MungeSumstats assume that the effects are majoritively measured on the minor alleles? Default is FALSE as this is an assumption that won't be appropriate in all cases. However, the benefit is that if we know the majority of SNPs have their effects based on the minor alleles, we can catch cases where the allele columns have been mislabelled.
return_list

Return the sumstats_dt within a named list (default: TRUE).

Details

Also, if eff_on_minor_alleles=TRUE, check 3 will be used in all cases. However, This assumes that the effects are majoritively measured on the minor alleles and should be used with caution as this is an assumption that won't be appropriate in all cases. However, the benefit is that if we know the majority of SNPs have their effects based on the minor alleles, we can catch cases where the allele columns have been mislabelled. IF eff_on_minor_alleles=TRUE, checks 1 and 2 will be skipped.

Value

list containing sumstats_dt, the modified summary statistics data table object

Examples

sumstats <- MungeSumstats::formatted_example()
#for speed, don't run on_ref_genome part of check (on_ref_genome = FALSE)
sumstats_dt2<-infer_effect_column(sumstats_dt=sumstats,on_ref_genome = FALSE)

Genome build liftover

Description

Transfer genomic coordinates from one genome build to another.

Usage

liftover(
  sumstats_dt,
  convert_ref_genome,
  ref_genome,
  chain_source = "ensembl",
  imputation_ind = TRUE,
  chrom_col = "CHR",
  start_col = "BP",
  end_col = start_col,
  as_granges = FALSE,
  style = "NCBI",
  local_chain = NULL,
  verbose = TRUE
)

Arguments

sumstats_dt

data table obj of the summary statistics file for the GWAS.
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).
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.
chain_source

chain file source used ("ucsc" as default, or "ensembl")
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.
chrom_col

Name of the chromosome column in sumstats_dt (e.g. "CHR").
start_col

Name of the starting genomic position column in sumstats_dt (e.g. "POS","start").
end_col

Name of the ending genomic position column in sumstats_dt (e.g. "POS","end"). Can be the same as start_col when sumstats_dt only contains SNPs that span 1 base pair (bp) each.
as_granges

Return results as GRanges instead of a data.table (default: FALSE).
style

Style to return GRanges object in (e.g. "NCBI" = 4; "UCSC" = "chr4";) (default: "NCBI").
local_chain

Path to local chain file to use instead of downlaoding. Default of NULL i.e. no local file to use. NOTE if passing a local chain file make sure to specify the path to convert from and to the correct build like GRCh37 to GRCh38. We can not sense check this for local files. The chain file can be submitted as a gz file (as downloaed from source) or unzipped.
verbose

Print messages.

Value

Lifted summary stats in data.table or GRanges format.

Source

liftOver

UCSC chain files

Ensembl chain files

Examples

sumstats_dt <- MungeSumstats::formatted_example()

sumstats_dt_hg38 <- liftover(sumstats_dt=sumstats_dt, 
                             ref_genome = "hg19",
                             convert_ref_genome="hg38")

List munged summary statistics

Description

Searches for and lists local GWAS summary statistics files munged by format_sumstats or import_sumstats.

Usage

list_sumstats(
  save_dir = getwd(),
  pattern = "*.tsv.gz$",
  ids_from_file = TRUE,
  verbose = TRUE
)

Arguments

save_dir

Top-level directory to recursively search for summary statistics files within.
pattern

Regex pattern to search for files with.
ids_from_file

Try to extract dataset IDs from file names. If FALSE, will infer IDs from the directory names instead.
verbose

Print messages.

Value

Named vector of summary stats paths.

Examples

save_dir <- system.file("extdata",package = "MungeSumstats")
munged_files <- MungeSumstats::list_sumstats(save_dir = save_dir)

Load the reference genome data for SNPs of interest

Description

Load the reference genome data for SNPs of interest

Usage

load_ref_genome_data(
  snps,
  ref_genome,
  dbSNP = c(144, 155),
  msg = NULL,
  chr_filt = NULL
)

Arguments

snps

Character vector SNPs by rs_id from sumstats file of interest.
ref_genome

Name of the reference genome used for the GWAS (GRCh37 or GRCh38)
dbSNP

version of dbSNP to be used (144 or 155)
msg

Optional name of the column missing from the dataset in question. Default is NULL
chr_filt

Internal for testing - filter reference genomes and sumstats to specific chromosomes for testing. Pass a list of chroms in format: c("1","2"). Default is NULL i.e. no filtering.

Value

data table of snpsById, filtered to SNPs of interest.

Source

sumstats_dt <- formatted_example() rsids <- MungeSumstats:::load_ref_genome_data(snps = sumstats_dt$SNP, ref_genome = "GRCH37", dbSNP=144)

Loads the SNP locations and alleles for Homo sapiens extracted from NCBI dbSNP Build 144. Reference genome version is dependent on user input.

Description

Loads the SNP locations and alleles for Homo sapiens extracted from NCBI dbSNP Build 144. Reference genome version is dependent on user input.

Usage

load_snp_loc_data(ref_genome, dbSNP = c(144, 155), msg = NULL)

Arguments

ref_genome

name of the reference genome used for the GWAS (GRCh37 or GRCh38)
dbSNP

version of dbSNP to be used (144 or 155)
msg

Optional name of the column missing from the dataset in question

Value

SNP_LOC_DATA SNP positions and alleles for Homo sapiens extracted from NCBI dbSNP Build 144

Examples

SNP_LOC_DATA <- load_snp_loc_data("GRCH37",dbSNP=144)

Parse data from log files

Description

Parses data from the log files generated by format_sumstats or import_sumstats when the argument log_mungesumstats_msgs is set to TRUE.

Usage

parse_logs(
  save_dir = getwd(),
  pattern = "MungeSumstats_log_msg.txt$",
  verbose = TRUE
)

Arguments

save_dir

Top-level directory to recursively search for log files within.
pattern

Regex pattern to search for files with.
verbose

Print messages.

Value

data.table of parsed log data.

Examples

save_dir <- system.file("extdata",package = "MungeSumstats")
log_data <- MungeSumstats::parse_logs(save_dir = save_dir)

GWAS Amyotrophic lateral sclerosis ieu open GWAS project - Subset

Description

VCF (VCFv4.2) of the GWAS Amyotrophic lateral sclerosis ieu open GWAS project Dataset: ebi-a-GCST005647. A subset of 99 SNPs

Format

vcf document with 528 items relating to 99 SNPs

Details

A VCF file (VCFv4.2) of the GWAS Amyotrophic lateral sclerosis ieu open GWAS project has been subsetted here to act as an example summary statistic file in VCF format which has some issues in the formatting. MungeSumstats can correct these issues and produced a standardised summary statistics format.

ALSvcf.vcf

NA

Source

The summary statistics VCF (VCFv4.2) file was downloaded from https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST005647/ and formatted to a .rda with the following: #Get example VCF dataset, use GWAS Amyotrophic lateral sclerosis ALS_GWAS_VCF <- readLines("ebi-a-GCST005647.vcf.gz") #Subset to just the first 99 SNPs ALSvcf <- ALS_GWAS_VCF[1:528] writeLines(ALSvcf,"inst/extdata/ALSvcf.vcf")

GWAS Educational Attainment Okbay 2016 - Subset

Description

GWAS Summary Statistics on Educational Attainment by Okbay et al 2016: PMID: 27898078 PMCID: PMC5509058 DOI: 10.1038/ng1216-1587b. A subset of 93 SNPs

Format

txt document with 94 items

Details

GWAS Summary Statistics on Educational Attainment by Okbay et al 2016 has been subsetted here to act as an example summary statistic file which has some issues in the formatting. MungeSumstats can correct these issues.

eduAttainOkbay.txt

NA

Source

The summary statistics file was downloaded from https://www.nature.com/articles/ng.3552 and formatted to a .rda with the following: #Get example dataset, use Educational-Attainment_Okbay_2016 link<-"Educational-Attainment_Okbay_2016/EduYears_Discovery_5000.txt" eduAttainOkbay<-readLines(link,n=100) #There is an issue where values end with .0, this 0 is removed in func #There are also SNPs not on ref genome or arebi/tri allelic #So need to remove these in this dataset as its used for testing tmp <- tempfile() writeLines(eduAttainOkbay,con=tmp) eduAttainOkbay <- data.table::fread(tmp) #DT read removes the .0's #remove those not on ref genome and withbi/tri allelic rmv <- c("rs192818565","rs79925071","rs1606974","rs1871109", "rs73074378","rs7955289") eduAttainOkbay <- eduAttainOkbay[!MarkerName data.table::fwrite(eduAttainOkbay,file=tmp,sep="\t") eduAttainOkbay <- readLines(tmp) writeLines(eduAttainOkbay,"inst/extdata/eduAttainOkbay.txt")

Read in file header

Description

Read in file header

Usage

read_header(path, n = 2L, skip_vcf_metadata = FALSE, nThread = 1)

Arguments

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.
n

integer. The (maximal) number of lines to read. Negative values indicate that one should read up to the end of input on the connection.
skip_vcf_metadata

logical, should VCF metadata be ignored
nThread

Number of threads to use for parallel processes.

Value

First n lines of the VCF header

Examples

path <- system.file("extdata", "eduAttainOkbay.txt", 
                    package = "MungeSumstats") 
header <- read_header(path = path)

Determine summary statistics file type and read them into memory

Description

Determine summary statistics file type and read them into memory

Usage

read_sumstats(
  path,
  nrows = Inf,
  standardise_headers = FALSE,
  samples = 1,
  sampled_rows = 10000L,
  nThread = 1,
  mapping_file = sumstatsColHeaders
)

Arguments

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.
nrows

integer. The (maximal) number of lines to read. If Inf, will read in all rows.
standardise_headers

Standardise headers first.
samples

Which samples to use:

  • 1 : Only the first sample will be used (DEFAULT).

  • NULL : All samples will be used.

  • c("<sample_id1>","<sample_id2>",...) : Only user-selected samples will be used (case-insensitive).

sampled_rows

First N rows to sample. Set NULL to use full sumstats_file. when determining whether cols are empty.
nThread

Number of threads to use for parallel processes.
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

data.table of formatted summary statistics

Examples

path <- system.file("extdata", "eduAttainOkbay.txt",
    package = "MungeSumstats"
)
eduAttainOkbay <- read_sumstats(path = path)

Read in VCF file

Description

Read in a VCF file as a VCF or a data.table. Can optionally save the VCF/data.table as well.

Usage

read_vcf(
  path,
  as_datatable = TRUE,
  save_path = NULL,
  tabix_index = FALSE,
  samples = 1,
  which = NULL,
  use_params = TRUE,
  sampled_rows = 10000L,
  download = TRUE,
  vcf_dir = tempdir(),
  download_method = "download.file",
  force_new = FALSE,
  mt_thresh = 100000L,
  nThread = 1,
  verbose = TRUE
)

Arguments

path

Path to local or remote VCF file.
as_datatable

Return the data as a data.table (default: TRUE) or a VCF (FALSE).
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.
samples

Which samples to use:

  • 1 : Only the first sample will be used (DEFAULT).

  • NULL : All samples will be used.

  • c("<sample_id1>","<sample_id2>",...) : Only user-selected samples will be used (case-insensitive).

which

Genomic ranges to be added if supplied. Default is NULL.
use_params

When TRUE (default), increases the speed of reading in the VCF by omitting columns that are empty based on the head of the VCF (NAs only). NOTE that that this requires the VCF to be sorted, bgzip-compressed, tabix-indexed, which read_vcf will attempt to do.
sampled_rows

First N rows to sample. Set NULL to use full sumstats_file. when determining whether cols are empty.
download

Download the VCF (and its index file) to a temp folder before reading it into R. This is important to keep TRUE when nThread>1 to avoid making too many queries to remote file.
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").
download_method

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

When the number of rows (variants) in the VCF is < mt_thresh, only use single-threading for reading in the VCF. This is because the overhead of parallelisation outweighs the speed benefits when VCFs are small.
nThread

Number of threads to use for parallel processes.
verbose

Print messages.

Value

The VCF file in data.table format.

Source

#### Benchmarking #### library(VCFWrenchR) library(VariantAnnotation) path <- "https://gwas.mrcieu.ac.uk/files/ubm-a-2929/ubm-a-2929.vcf.gz" vcf <- VariantAnnotation::readVcf(file = path) N <- 1e5 vcf_sub <- vcf[1:N,] res <- microbenchmark::microbenchmark( "vcf2df"={dat1 <- MungeSumstats:::vcf2df(vcf = vcf_sub)}, "VCFWrenchR"= {dat2 <- as.data.frame(x = vcf_sub)}, "VRanges"={dat3 <- data.table::as.data.table( methods::as(vcf_sub, "VRanges"))}, times=1 )

Discussion on VariantAnnotation GitHub

Discussion on VariantAnnotation GitHub

Examples

#### Local file ####
path <- system.file("extdata","ALSvcf.vcf", package="MungeSumstats")
sumstats_dt <- read_vcf(path = path)

#### Remote file ####
## Small GWAS (0.2Mb)
# path <- "https://gwas.mrcieu.ac.uk/files/ieu-a-298/ieu-a-298.vcf.gz"
# sumstats_dt2 <- read_vcf(path = path)

## Large GWAS (250Mb)
# path <- "https://gwas.mrcieu.ac.uk/files/ubm-a-2929/ubm-a-2929.vcf.gz"
# sumstats_dt3 <- read_vcf(path = path, nThread=11)

### Very large GWAS (500Mb)
# path <- "https://gwas.mrcieu.ac.uk/files/ieu-a-1124/ieu-a-1124.vcf.gz"
# sumstats_dt4 <- read_vcf(path = path, nThread=11)

Register cores

Description

Register a multi-threaded instances using BiocParallel.

Usage

register_cores(workers = 1, progressbar = TRUE)

Arguments

workers

integer(1) Number of workers. Defaults to the maximum of 1 or the number of cores determined by detectCores minus 2 unless environment variables R_PARALLELLY_AVAILABLECORES_FALLBACK or BIOCPARALLEL_WORKER_NUMBER are set otherwise. For a SOCK cluster, workers can be a character() vector of host names.

progressbar

logical(1) Enable progress bar (based on plyr:::progress_text).

Value

Null output.

Standardise the column headers in the Summary Statistics files

Description

Use a reference data table of common column header names (stored in sumstatsColHeaders or user inputted mapping file) to convert them to a standard set, i.e. chromosome -> CHR. This function does not check that all the required column headers are present. The amended header is written directly back into the file

Usage

standardise_header(
  sumstats_dt,
  mapping_file = sumstatsColHeaders,
  uppercase_unmapped = TRUE,
  convert_A0 = TRUE,
  return_list = TRUE
)

Arguments

sumstats_dt

data table obj of the summary statistics file for the GWAS.
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.
uppercase_unmapped

For columns that could not be identified in the mapping_file, return them in the same format they were input as (without forcing them to uppercase).
convert_A0

Whether to convert A* (representing A0) to A1/A2. This should be done unless checking if A0 was present in the input as if you do it you can't infer this. Default is TRUE
return_list

Return the sumstats_dt within a named list (default: TRUE).

Value

list containing sumstats_dt, the modified summary statistics data table object

Examples

sumstats_dt <- data.table::fread(system.file("extdata", "eduAttainOkbay.txt",
                                             package = "MungeSumstats"))
sumstats_dt2 <- standardise_header(sumstats_dt=sumstats_dt)

Summary Statistics Column Headers

Description

List of uncorrected column headers often found in GWAS Summary Statistics column headers. Note the effect allele will always be the A2 allele, this is the approach done for VCF(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805039/). This is enforced with the column header corrections here and also the check allele flipping test.

Usage

data("sumstatsColHeaders")

Format

dataframe with 2 columns

Source

The code to prepare the .Rda file file from the marker file is: # Most the data in the below table comes from the LDSC github wiki data("sumstatsColHeaders") # Make additions to sumstatsColHeaders using github version of MungeSumstats- # Shown is an example of adding new A1 and A2 naming a1_name <- c("NON","RISK","ALLELE") a2_name <- c("RISK","ALLELE") all_delims <- c("_",".",""," ","-") all_uncorr_a1 <- vector(mode="list",length = length(all_delims)) all_corr_a1 <- vector(mode="list",length = length(all_delims)) all_uncorr_a2 <- vector(mode="list",length = length(all_delims)) all_corr_a2 <- vector(mode="list",length = length(all_delims)) for(i in seq_along(all_delims)){ delim <- all_delims[i] a1 <- unlist(paste(a1_name,collapse=delim)) a2 <- unlist(paste(a2_name,collapse=delim)) all_uncorr_a1[[i]] <- a1 all_uncorr_a2[[i]] <- a2 all_corr_a1[[i]] <- "A1" all_corr_a2[[i]] <- "A2" } se_cols <- data.frame("Uncorrected"=c(unlist(all_uncorr_a1),unlist(all_uncorr_a2)), "Corrected"=c(unlist(all_corr_a1),unlist(all_corr_a2))) # Or another example ..... # shown is an example of adding columns for Standard Error (SE) se_cols <- data.frame("Uncorrected"=c("SE","se","STANDARD.ERROR", "STANDARD_ERROR","STANDARD-ERROR"), "Corrected"=rep("SE",5)) sumstatsColHeaders <- rbind(sumstatsColHeaders,se_cols) #Once additions are made, order & save the new mapping dataset #now sort ordering -important for logic that # uncorrected=corrected comes first sumstatsColHeaders$ordering <- sumstatsColHeaders$Uncorrected==sumstatsColHeaders$Corrected sumstatsColHeaders <- sumstatsColHeaders[order(sumstatsColHeaders$Corrected, sumstatsColHeaders$ordering,decreasing = TRUE),] rownames(sumstatsColHeaders)<-1:nrow(sumstatsColHeaders) sumstatsColHeaders$ordering <- NULL #manually move FREQUENCY to above MAR - github issue 95 frequency <- sumstatsColHeaders[sumstatsColHeaders$Uncorrected=="FREQUENCY",] maf <- sumstatsColHeaders[sumstatsColHeaders$Uncorrected=="MAF",] if(as.integer(rownames(frequency))>as.integer(rownames(maf))){ sumstatsColHeaders[as.integer(rownames(frequency)),] <- maf sumstatsColHeaders[as.integer(rownames(maf)),] <- frequency } usethis::use_data(sumstatsColHeaders,overwrite = TRUE, internal=TRUE) save(sumstatsColHeaders, file="data/sumstatsColHeaders.rda") # You will need to restart your r session for effects to take account

VCF to DF

Description

Function to convert a VariantAnnotation CollapsedVCF/ExpandedVCF object to a data.frame.

Usage

vcf2df(
  vcf,
  add_sample_names = TRUE,
  add_rowranges = TRUE,
  drop_empty_cols = TRUE,
  unique_cols = TRUE,
  unique_rows = TRUE,
  unlist_cols = TRUE,
  sampled_rows = NULL,
  verbose = TRUE
)

Arguments

vcf

Variant Call Format (VCF) file imported into R as a VariantAnnotation CollapsedVCF/ ExpandedVCF object.
add_sample_names

Append sample names to column names (e.g. "EZ" –> "EZ_ubm-a-2929").
add_rowranges

Include rowRanges from VCF as well.
drop_empty_cols

Drop columns that are filled entirely with: NA, ".", or "".
unique_cols

Only keep uniquely named columns.
unique_rows

Only keep unique rows.
unlist_cols

If any columns are lists instead of vectors, unlist them. Required to be TRUE when unique_rows=TRUE.
sampled_rows

First N rows to sample. Set NULL to use full sumstats_file. when determining whether cols are empty.
verbose

Print messages.

Value

data.frame version of VCF

Source

Original code source

vcfR

if(!require("pinfsc50")) install.packages("pinfsc50") vcf_file <- system.file("extdata", "pinf_sc50.vcf.gz", package = "pinfsc50") vcf <- read.vcfR( vcf_file, verbose = FALSE ) vcf_df_list <- vcfR::vcfR2tidy(vcf, single_frame=TRUE) vcf_df <- data.table::data.table(vcf_df_list$dat)

Examples

#### VariantAnnotation ####
# path <- "https://github.com/brentp/vcfanno/raw/master/example/exac.vcf.gz"
path <- system.file("extdata", "ALSvcf.vcf",
                    package = "MungeSumstats")
                    
vcf <- VariantAnnotation::readVcf(file = path)
vcf_df <- MungeSumstats:::vcf2df(vcf = vcf)

Write sum stats file to disk

Description

Write sum stats file to disk

Usage

write_sumstats(
  sumstats_dt,
  save_path,
  ref_genome = NULL,
  sep = "\t",
  write_vcf = FALSE,
  save_format = NULL,
  tabix_index = FALSE,
  nThread = 1,
  return_path = FALSE,
  save_path_check = FALSE
)

Arguments

sumstats_dt

data table obj of the summary statistics file for the GWAS.
save_path

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

The separator between columns. Defaults to the character in the set [,\t |;:] that separates the sample of rows into the most number of lines with the same number of fields. Use NULL or "" to specify no separator; i.e. each line a single character column like base::readLines does.
write_vcf

Whether to write as VCF (TRUE) or tabular file (FALSE).
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. NOTE - If LDSC format is used, the naming convention of A1 as the reference (genome build) allele and A2 as the effect allele will be reversed to match LDSC (A1 will now be the effect allele). See more info on this here. Note that any effect columns (e.g. Z) will be inrelation to A1 now instead of A2.
tabix_index

Index the formatted summary statistics with tabix for fast querying.
nThread

The number of threads to use. Experiment to see what works best for your data on your hardware.
return_path

Return save_path. This will have been modified in some cases (e.g. after compressing and tabix-indexing a previously un-compressed file).
save_path_check

Ensure path name is valid (given the other arguments) before writing (default: FALSE).

Value

If return_path=TRUE, returns save_path. Else returns NULL.

Source

VariantAnnotation::writeVcf has some unexpected/silent file renaming behavior

Examples

path <- system.file("extdata", "eduAttainOkbay.txt",
    package = "MungeSumstats"
)
eduAttainOkbay <- read_sumstats(path = path)
write_sumstats(
    sumstats_dt = eduAttainOkbay,
    save_path = tempfile(fileext = ".tsv.gz")
)