---
title: "`MungeSumstats`: Getting started"
author: "<h5><i>Authors</i>: Alan Murphy, Brian Schilder and Nathan Skene</h5>"
date: "<h5><i>Updated</i>: `r format(Sys.Date(), '%b-%d-%Y')`</h5>"
csl: nature.csl
output:
  BiocStyle::html_document:
vignette: >
    %\VignetteIndexEntry{MungeSumstats} 
    %\usepackage[utf8]{inputenc}
    %\VignetteEngine{knitr::rmarkdown} 
bibliography: MungeSumstats.bib
---
 
# Citation

If you use the *MungeSumstats* package, please cite

[Murphy et al. MungeSumstats: A Bioconductor package for the
standardisation and quality control of many GWAS summary
statistics](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab665/6380562).

# Overview

The *MungeSumstats* package is designed to facilitate the
standardisation of GWAS summary statistics as utilised in our Nature
Genetics paper [@Skene2018].

The package is designed to handle the lack of standardisation of output
files by the GWAS community. There is a group who have now manually
standardised many GWAS: [R interface to the IEU GWAS database API •
ieugwasr](https://mrcieu.github.io/ieugwasr/) and
[gwasvcf](https://github.com/MRCIEU/gwasvcf) but because a lot of GWAS
remain closed access, these repositories are not all encompassing.

The [GWAS-Download
project](https://github.com/mikegloudemans/gwas-download) has collated
summary statistics from 200+ GWAS. This repository has been utilsed to
identify the most common formats, all of which can be standardised with
*MungeSumstats*.

Moreover, there is an emerging standard of VCF format for summary
statistics files with multiple, useful, associated R packages such as
*vcfR*. However, there is currently no method to convert VCF formats to
a standardised format that matches older approaches.

The *MungeSumstats* package standardises both VCF and the most common
summary statistic file formats to enable downstream integration and
analysis.

*MungeSumstats* also offers comprehensive Quality Control (QC) steps which
are important prerequisites for downstream analysis like Linkage
disequilibrium score regression (LDSC) and MAGMA.

Moreover, *MungeSumstats* is efficiently written resulting in all reformatting
and quality control checks completing in minutes for GWAS summary statistics
with 500k SNPs on a standard desktop machine. This speed can be increased 
further by increasing the number of threads (nThread) for `data.table` to use.

Currently *MungeSumstats* only works on data from humans, as it uses
human-based genome references.

# Aim

*MungeSumstats* will ensure that the all essential columns for analysis
are present and syntactically correct. Generally, summary statistic
files include (but are not limited to) the columns:

-   SNP : SNP ID (rs IDs)
-   CHR : Chromosome number
-   BP : Base pair positions
-   A1 : reference allele
-   A2 : alternative allele
-   Z : Z-score
-   BETA : Effect size estimate relative to the alternative allele
-   P : Unadjusted p-value for SNP
-   SE : The standard error
-   N : Sample size
-   INFO: The imputation information score
-   FRQ: The minor/effect allele frequency (MAF/EAF) of the SNP

*MungeSumstats* uses a mapping file to infer the inputted column names (run
`data("sumstatsColHeaders")` to view these). This mapping file is far more 
comprehensive than any other publicly available munging tool containing more 
than 200 unique mappings at the time of writing this vignette. However, if your
column headers are missing or if you want to change the mapping, you can do so 
by passing your own mapping file (see `format_sumstats(mapping_file)`).

*MungeSumstats* offers unmatched levels of quality control to ensure, for 
example, consistency of allele assignment and direction of effects. Tests run by
*MungeSumstats* include:

-   Check VCF format
-   Check tab, space or comma delimited, zipped, csv or tsv file
-   Check for header name synonyms
-   Check for multiple models or traits in GWAS
-   Check for uniformity in SNP ID - no mix of rs/missing rs/chr:bp
-   Check for CHR:BP:A2:A1 all in one column
-   Check for CHR:BP in one column
-   Check for A1/A2 in one column
-   Check if CHR and/or BP is missing (infer from reference genome)
-   Check if SNP ID is missing (infer from reference genome)
-   Check if A1 and/or A2 are missing (infer from reference genome)
-   Check that vital columns are present (SNP,CHR,BP,P,A1,A2)
-   Check for one signed/effect column
    (Z,OR,BETA,LOG_ODDS,SIGNED_SUMSTAT)
-   Check for missing data
-   Check for duplicated columns
-   Check for small p-values (lower than 5e-324)
-   Check N column is an integer
-   Check for SNPs with N greater than 5 times standard dev. plus the
    mean
-   Check SNPs are RS ID's
-   Check for uniformity of SNP ID format
-   Check for duplicated rows, based on SNP ID
-   Check for duplicated rows, based on base-pair position
-   Check for SNPs on reference genome. Correct not found SNP IDs using
    CHR and BP (infer from reference genome)
-   Check INFO score
-   Check FRQ value
-   Check FRQ is minor allele frequency (MAF)
-   Check that the SNPs' standard error (SE) is positive
-   Check that SNPs' effect columns (like BETA) aren't equal to 0
-   Check for strand-ambiguous SNPs
-   Check for non-biallelic SNPs (infer from reference genome)
-   Check for allele flipping
-   Check for SNPs on chromosome X, Y, and mitochondrial SNPs (MT)
-   Check for z-score (Z) and impute if missing
-   Check for N and impute if missing
-   Check output format is LDSC ready
-   Check and perform liftover to desired reference genome if necessary 

Users can specify which checks to run on their data. A **note** on the allele 
flipping check: **MungeSumstats** infers the effect allele will always be the 
A2 allele, this is the approach done for 
[IEU GWAS VCF](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805039/) and has 
such also been adopted here. This inference is first from the inputted file's 
column headers however, the allele flipping check ensures this by comparing A1, 
what should be the reference allele, to the reference genome. If a SNP's A1 
DNA base doesn't match the reference genome but it's A2 (what should be the
alternative allele) does, the alleles will be flipped along with the effect 
information (e.g. Beta, Odds Ratio, signed summary statistics, FRQ, Z-score*).

*-by default the Z-score is assumed to be calculated off the effect size not 
the P-value and so will be flipped. This can be changed by a user.

If a test is failed, the user will be notified and if possible, the
input will be corrected. The QC steps from the checks above can also be
adjusted to suit the user's analysis, see
`MungeSumstats::format_sumstats`.

*MungeSumstats* can handle VCF, txt, tsv, csv file types or .gz/.bgz versions of
these file types. The package also gives the user the flexibility to export the
reformatted file as tab-delimited, VCF or R native objects such as data.table,
GRanges or VRanges objects. The output can also be outputted in an 
**LDSC ready** format which means the file can be fed directly into LDSC without
the need for additional munging.

# Data

The *MungeSumstats* package contains small subsets of GWAS summary
statistics files. Firstly, on Educational Attainment by Okbay et al
2016: PMID: 27898078 PMCID: PMC5509058 DOI: 10.1038/ng1216-1587b.

Secondly, a VCF file (VCFv4.2) relating to the GWAS Amyotrophic lateral
sclerosis from ieu open GWAS project. Dataset: ebi-a-GCST005647:
<https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST005647/>

These datasets will be used to showcase *MungeSumstats* functionality.

# Running *MungeSumstats*

*MungeSumstats* is available on Bioconductor. To install the package on
Bioconductor run the following lines of code:

    if (!require("BiocManager"))
        install.packages("BiocManager")

    BiocManager::install("MungeSumstats")

Once installed, load the package:

```{r setup}
library(MungeSumstats)
```

To standardise the summary statistics' file format, simply call
`format_sumstats()` passing in the path to your summary statistics file or 
directly pass the summary statistics as a dataframe or datatable.
You can specify which genome build was used in the GWAS(GRCh37 or
GRCh38) or, as default, infer the genome build from the data.The
reference genome is used for multiple checks like deriving missing data
such SNP/BP/CHR/A1/A2 and for QC steps like removing non-biallelic SNPs,
strand-ambiguous SNPs or ensuring correct allele and direction of SNP effects. 
The path to the reformatted summary statistics file can be returned by the 
function call, the user can specify a location to save the file or the user can 
return an R native object for the data: data.table, VRanges or GRanges 
object.

Note that for a number of the checks implored by *MungeSumstats* a
reference genome is used. If your GWAS summary statistics file of
interest relates to *GRCh38*, you will need to install
`SNPlocs.Hsapiens.dbSNP144.GRCh38` and `BSgenome.Hsapiens.NCBI.GRCh38`
from Bioconductor as follows:

    BiocManager::install("SNPlocs.Hsapiens.dbSNP144.GRCh38")
    BiocManager::install("BSgenome.Hsapiens.NCBI.GRCh38")

If your GWAS summary statistics file of interest relates to *GRCh37*,
you will need to install `SNPlocs.Hsapiens.dbSNP144.GRCh37` and
`BSgenome.Hsapiens.1000genomes.hs37d5` from Bioconductor as follows:

    BiocManager::install("SNPlocs.Hsapiens.dbSNP144.GRCh37")
    BiocManager::install("BSgenome.Hsapiens.1000genomes.hs37d5")

These may take some time to install and are not included in the package
as some users may only need one of *GRCh37*/*GRCh38*.

The Educational Attainment by Okbay GWAS summary statistics file is
saved as a text document in the package's external data folder so we can
just pass the file path to it straight to *MungeSumstats*.

**NOTE** - By default, Formatted results will be saved to `tempdir()`. This 
means all formatted summary stats will be deleted upon ending the R session if 
not copied to a local file path. Otherwise, to keep formatted summary stats, 
change `save_path` ( e.g.`file.path('./formatted',basename(path))`), or make 
sure to copy files elsewhere after processing 
( e.g.`file.copy(save_path, './formatted/' )`.

```{r, eval=FALSE, message=TRUE}
eduAttainOkbayPth <- system.file("extdata","eduAttainOkbay.txt",
                                  package="MungeSumstats")
reformatted <- 
  MungeSumstats::format_sumstats(path=eduAttainOkbayPth,
                                 ref_genome="GRCh37")
```

```{r,echo=FALSE}
#don't run time intensive checks
eduAttainOkbayPth <- system.file("extdata","eduAttainOkbay.txt",
                                  package="MungeSumstats")
reformatted <- 
  MungeSumstats::format_sumstats(path=eduAttainOkbayPth,
                                 on_ref_genome = FALSE,
                                 strand_ambig_filter = FALSE,
                                 bi_allelic_filter = FALSE,
                                 allele_flip_check = FALSE,
                                 ref_genome="GRCh37")
```

The arguments `format_sumstats` in that control the level of QC
conducted by *MungeSumstats* are:

-   **convert_small_p** Binary, should `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 from P. Default is FALSE.
    **Note** that imputing the Z-score for every SNP will not correct be 
    perfectly correct and may result in a loss of power. This should only be 
    done as a last resort.
-   **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 to 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. analysis_trait If
    multiple traits were studied, name of the trait for analysis from
    the GWAS. Default is NULL.
-   **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 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.
-   **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 value is FALSE.
-   **analysis_trait** If multiple traits were studied, name of the trait for
    analysis from the GWAS. Default is NULL.
-   **INFO_filter** 0-1 The minimum value permissible of the imputation
    information score (if present in sumstatsfile). Default 0.9
-   **FRQ_filter** 0-1 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 TRUE.    
-   **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** controls
    whether the SNPs with a missing N value are dropped or not (Default is 
    TRUE).
-   **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** controls whether "chr"/"CHR" is 
    removed from chromosome names (Default is TRUE).
-   **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. Any SNPs not on the reference genome,
    will be corrected from the reference genome (if possible) using the 
    chromosome and base pair position data. Default is TRUE
-   **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).    
-   **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** controls whether the SNPs for which neither 
    their A1 or A2 base pair values match a reference genome be dropped. Default
    is TRUE. **allele_flip_z** controls whether the Z-score value should be 
    flipped along with effect and FRQ columns (e.g. Beta). Default is TRUE.
    **allele_flip_frq**  controls whether the frequency (FRQ) value should be 
    flipped along with effect and Z-score columns (e.g. Beta). Default is TRUE.
-   **bi_allelic_filter** Binary, should non-biallelic SNPs be removed.
    Default is TRUE
-   **snp_ids_are_rs_ids** Binary, should the SNP IDs inputted be inferred as RS
    IDs or some arbitrary ID. 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** Binary, 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 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.
-   **sort_coordinates** Whether to sort by coordinates of resulting sumstats.    
-   **nThread** Number of threads to use for parallel processes.
-   **write_vcf** Whether to write as VCF (TRUE) or tabular file (FALSE). While
    **tabix_index** is a binary input for whether to index the formatted summary
    statistics with [tabix](http://www.htslib.org/doc/tabix.html) 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** Ensure that output format meets all requirements to be 
    passed directly into LDSC without the need for additional munging.
-   **log_folder_ind** 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. 
-   **log_mungesumstats_msgs** Binary Should a log be stored containing all 
    messages and errors printed by MungeSumstats in a run.
-   **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.
-   **log_folder** File path to the directory for the log files and the log of
    MungeSumstats messages to be stored. Default is a temporary directory.
-   **force_new** If a formatted file of the same names as \code{save_path}
    exists, formatting will be skipped and this file will be imported instead
    (default). Set \code{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.    

See `?MungeSumstats::format_sumstats()` for the full list of parameters to 
control MungeSumstats QC and standardisation steps.
    
VCF files can also be standardised to the same format as other summary
statistic files. A subset of the Amyotrophic lateral sclerosis GWAS from
the ieu open GWAS project (a .vcf file) has been added to
*MungeSumstats* to demonstrate this functionality.Simply pass the path
to the file in the same manner you would for other summary statistic
files:

```{r, message=TRUE}
#save ALS GWAS from the ieu open GWAS project to a temp directory
ALSvcfPth <- system.file("extdata","ALSvcf.vcf", package="MungeSumstats")
```

```{r,eval=FALSE}
reformatted_vcf <- 
  MungeSumstats::format_sumstats(path=ALSvcfPth, 
                                 ref_genome="GRCh37")
```

You can also get more information on the SNPs which have had data imputed or 
have been filtered out by *MungeSumstats* by using the `imputation_ind` and 
`log_folder_ind` parameters respectively. For example:

```{r, eval=FALSE, message=FALSE}
#set
reformatted_vcf_2 <- 
  MungeSumstats::format_sumstats(path=ALSvcfPth,
                                 ref_genome="GRCh37",
                                 log_folder_ind=TRUE,
                                 imputation_ind=TRUE,
                                 log_mungesumstats_msgs=TRUE)
```

```{r,echo=FALSE,message=FALSE}
#don't run time intensive checks
reformatted_vcf_2 <- 
  MungeSumstats::format_sumstats(path=ALSvcfPth,
                                 ref_genome="GRCh37",
                                 log_folder_ind=TRUE,
                                 imputation_ind=TRUE,
                                 log_mungesumstats_msgs=TRUE,
                                 on_ref_genome = FALSE,
                                 strand_ambig_filter = FALSE,
                                 bi_allelic_filter = FALSE,
                                 allele_flip_check = FALSE)
```

Note that using `log_folder_ind` returns a list from `format_sumstats` which
includes the file locations of the differing classes of removed SNPs. Using 
`log_mungesumstats_msgs` saves the messages to the console to a file which is 
returned in the same list. Note that not all the messages will also print to 
screen anymore when you set `log_mungesumstats_msgs`:

```{r, message=TRUE}
names(reformatted_vcf_2)
```

A user can load a file to view the excluded SNPs.

In this case, no SNPs were filtered based on INFO criterion, 
so an NA is returned for this value
(instead of a path to a table of filtered SNPs). 

```{r, message=TRUE}
print(reformatted_vcf_2$log_files$info_filter)
```

The different types of exclusion which lead to the names are explained below:

-   **snp_multi_rs_one_row** - Where the SNP (RS ID) contained more than one RS 
    ID.
-   **snp_missing_rs** - Where the SNP (RS ID) was missing the rs prefix. Note 
    that these are only removed when other snps have an rs prefix.
-   **snp_multi_colon** - Where the SNP ID has mutliple colons (":") in one SNP.
-   **snp_not_found_from_bp_chr** - Where the RS ID was attempted to be imputed
    from the CHR and BP (Base-Pair) information, using the reference genome, but
    wasn't successful.
-   **chr_bp_not_found_from_snp** - Where the CHR and BP (Base-Pair) was 
    attempted to be imputed from the SNP (RS ID), using the reference genome, 
    but wasn't successful. 
-   **alleles_not_found_from_snp** - Where the alleles (A1 and/or A2) was 
    attempted to be imputed from the SNP (RS ID), using the reference genome, 
    but wasn't successful.
-   **alleles_dont_match_ref_gen** - Where the alleles (A1 and/or A2) don't 
    match what's on the reference genome.
-   **missing_data** - Where there is data missing across the inputted columns.
-   **dup_snp_id** - Where the SNP ID is duplicated in the input.
-   **dup_base_pair_position** - Where the base-pair position is duplicated in 
    the input.
-   **info_filter** - SNP INFO value below the specified threshold.
-   **se_neg** - SNPs SE (Standard Error) value is 0 or negative.
-   **effect_col_zero** - SNPs effect column(s) value is zero e.g. BETA=0.
-   **n_large** - SNPs N is N standard deviations greater than the mean.
-   **n_null** - SNPs N is null.
-   **chr_excl** - SNP is on a chromosome specified to be excluded.
-   **snp_strand_ambiguous** - SNP is strand ambiguous.
-   **snp_bi_allelic** - SNP is not bi-allelic.
-   **MungeSumstats_log_msg** - Text file of all messages to the console created
    during MungeSumstats run.
-   **MungeSumstats_log_output** - Text file of all errors to the console 
    created during MungeSumstats run.


Note to export to another type such as R native objects including data.table, 
GRanges, VRanges or save as a VCF file, set `return_format`:

```{r, message=FALSE,eval=FALSE}
#set
reformatted_vcf_2 <- 
  MungeSumstats::format_sumstats(path=ALSvcfPth,
                                 ref_genome="GRCh37", 
                                 log_folder_ind=TRUE,
                                 imputation_ind=TRUE,
                                 log_mungesumstats_msgs=TRUE,
                                 return_format="GRanges")
```


See our publication for further discussion of these checks and options:

[Murphy et al. MungeSumstats: A Bioconductor package for the
standardisation and quality control of many GWAS summary
statistics](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab665/6380562).

# Extra Functionality

## Get genome builds

*MungeSumstats* also contains a function to quickly infer the genome build of 
multiple summary statistic files. This can be called separately to 
`format_sumstats()` which is useful if you want to just quickly check the genome
build:

```{r, message=FALSE,eval=FALSE}
# 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)

ref_genomes <- MungeSumstats::get_genome_builds(sumstats_list = sumstats_list)

```


## Liftover

*MungeSumstats* exposes the `liftover()` function as a general utility for users.

Useful features include:
  - Automatic standardisation of genome build names 
  (i.e. "hg19", "hg37", and "GRCh37" will all be 
  recognized as the same genome build.)
  - Ability to specify `chrom_col` as well as both 
  `start_col` and `end_col` (for variants that span >1bp).
  - Ability to return in `data.table` or `GRanges` format.
  - Ability to specify which chromosome format (e.g. "chr1" vs. 1) 
  to return `GRanges` as.

```{r}
sumstats_dt <- MungeSumstats::formatted_example()
sumstats_dt_hg38 <- MungeSumstats::liftover(sumstats_dt = sumstats_dt, 
                                            ref_genome = "hg19",
                                            convert_ref_genome = "hg38")
knitr::kable(head(sumstats_dt_hg38))
```


## Quick formatting

In some cases, users may not want to run the full munging pipeline provided by  
`MungeSumstats::format_sumstats`, but still would like to take advantage of
the file type conversion and column header standardisation features. 
This will not be nearly as robust as the full pipeline, but can still be 
helpful.

### From disk

To do this, simply run the following:

```{r}
eduAttainOkbayPth <- system.file("extdata", "eduAttainOkbay.txt",
                                  package = "MungeSumstats")
formatted_path <- tempfile(fileext = "eduAttainOkbay_standardised.tsv.gz")


#### 1. Read in the data and standardise header names ####
dat <- MungeSumstats::read_sumstats(path = eduAttainOkbayPth, 
                                    standardise_headers = TRUE)
knitr::kable(head(dat))
#### 2. Write to disk as a compressed, tab-delimited, tabix-indexed file ####
formatted_path <- MungeSumstats::write_sumstats(sumstats_dt = dat,
                                                save_path = formatted_path,
                                                tabix_index = TRUE,
                                                write_vcf = FALSE,
                                                return_path = TRUE)   
```

### From `data.table`

If you already have your data imported as an `data.table`, 
you can also standardise its headers like so:

```{r}
#### Mess up some column names ####
dat_raw <- data.table::copy(dat)
data.table::setnames(dat_raw, c("SNP","CHR"), c("rsID","Seqnames"))
#### Add a non-standard column that I want to keep the casing for ####
dat_raw$Support <- runif(nrow(dat_raw))

dat2 <- MungeSumstats::standardise_header(sumstats_dt = dat_raw,
                                          uppercase_unmapped = FALSE, 
                                          return_list = FALSE )
knitr::kable(head(dat2))
```





# Future Enhancements

The *MungeSumstats* package aims to be able to handle the most common
summary statistic file formats including VCF. If your file can not be
formatted by *MungeSumstats* feel free to report the bug on github:
<https://github.com/neurogenomics/MungeSumstats> along with your summary
statistic file header.

We also encourage people to edit the code to resolve their particular
issues too and are happy to incorporate these through pull requests on
github. If your summary statistic file headers are not recognised by
*MungeSumstats* but correspond to one of:

    SNP, BP, CHR, A1, A2, P, Z, OR, BETA, LOG_ODDS,
    SIGNED_SUMSTAT, N, N_CAS, N_CON, NSTUDY, INFO or FRQ 

feel free to update the `MungeSumstats::sumstatsColHeaders` following
the approach in the data.R file and add your mapping. Then use a pull
request on github and we will incorporate this change into the package.

A note on `MungeSumstats::sumstatsColHeaders` for summary statistic files with
A0/A1. The mapping in `MungeSumstats::sumstatsColHeaders` converts A0 to A*, 
this is a special case so that the code knows to map A0/A1 to A1/A2 (ref/alt). 
The special case is needed since ordinarily A1 refers to the reference not the
alternative allele.

# Further functionality

See the [Open GWAS vignette](https://neurogenomics.github.io/MungeSumstats/articles/OpenGWAS.html)
for how MungeSumstats can be used along with data from the MRC IEU Open GWAS 
Project and also Mungesumstats' functionality to handle lists of summary 
statistics files.



# Session Information

<details>

```{r, message=TRUE, echo=FALSE}
utils::sessionInfo()
```

</details>

# References
