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Format of GWAS summary statistics.

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# SUMSTATS

## Description of GWAS summary statistics

The repository shows how to provide input for PW-pipeline, FM-pipeline including GCTA, among others; it also shows how to generate SNPid-RSid pairs which would be required for consortium meta-analysis.

## The summary table

Briefly, the format has the following columns,

Column | Name | Description
-------|------|------------
1 | SNP | RSid
2 | A1 | Effect allele
3 | A2 | Other allele
4 | freqA1 | A1 frequency
5 | beta | effect estimate
6 | se | standard error of effect
7 | P | P-value
8 | N | sample size
9*  | chr | chromosome
10* | pos | position

\* see next section.

## SNP information

It is a personal choice to use either UCSC or dbSNP.

### UCSC

The chromosomes and positions can be obtained from <https://genome.ucsc.edu/>, which
should be helpful for GWAS summary statistics either using chromosomal positions from different
builds or without these at all.
```bash
wget http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/snp150.txt.gz
gunzip -c snp150.txt.gz | \
awk '{split($2,a,"_");sub(/chr/,"",a[1]);print a[1],$4,$5}' | \
sort -k3,3 > snp150.txt
```
where it first obtains build 37 positions[^hg18], sorts them by RSid into the file `snp150.txt`. More flexiblly, 
we can do as in [TWAS-pipeline](https://github.com/jinghuazhao/TWAS-pipeline/wiki/Building-reference-panel)
on refGene by selecting appropriate columns,
```bash
# from the MySQL database
mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A -D hg19 -e 'select * from snp150' > snp150.txt
# into a MySQL database
gunzip -c snp150.txt.gz > snp150.txt
(
  wget -qO- http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/snp150.sql
  echo load data local infile 'snp150.txt' into table snp150;
) > snp150.sql
```
while [snp150.sql](http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/snp150.sql) is amended. The option -A on the MysQL command line makes it faster and -D specifies database.
More generally, it is possible to use MySQL interactively,
```bash
mysql --user=genome --host=genome-mysql.cse.ucsc.edu -A <<END
show databases;
use hg19;
show tables;
describe snp150;
select * from snp150 LIMIT 1,30;
select chrom, chromStart, chromEnd, strand, name, refNCBI, observed from snp150 WHERE name="rs548419688";
END
```
which illustrate some useful commands.

The RSid -- SNPid (chromosome:position_allele1_allele2 such that allele1 \< allele2) pairing can be achieved as follows,
```bash
gunzip -c snp150.txt.gz | \
cut -f2,4,5,10 | \
awk '$1~/^chr[0-9]+$|^chrX$|^chrY$/{if(!index($4,"-")) {split($4,a,"/"); print $1 ":" $2 "_" a[1] "_" a[2], $3}}' | \
sort -k1,1 | \
gzip -f > snp150.snpid_rsid.gz
```

### dbSNP

The script below downloads SNP154, replacing Genomic accession numbers (GANs) with familiar chromosome names via ctreepo, <https://github.com/vkkodali/cthreepo> and indexing the output via tabix[^tabix].

```bash
module load ceuadmin/bcftools
module load ceuadmin/htslib
source ~/rds/public_databases/software/py38/bin/activate
pip install cthreepo

# hg19
wget https://ftp.ncbi.nih.gov/snp/archive/b154/VCF/GCF_000001405.25 -O snp154_GCF_000001405.25

cthreepo --infile snp154_GCF_000001405.25 --id_from rs --id_to uc --format vcf --mapfile h37 --outfile snp154_hg19.vcf
bgzip -f snp154_hg19.vcf
tabix -p vcf -f snp154_hg19.vcf.gz

# hg38
wget https://ftp.ncbi.nih.gov/snp/archive/b154/VCF/GCF_000001405.38 -O snp154_GCF_000001405.38

cthreepo --infile snp154_GCF_000001405.38 --id_from rs --id_to uc --format vcf --mapfile h38 --outfile snp154_hg38.vcf
bgzip -f snp154_hg38.vcf
tabix -p vcf -f snp154_hg38.vcf.gz

# index snp154_GCF.*
bgzip -f snp154_GCF_000001405.25
tabix -p vcf snp154_GCF_000001405.25.gz
bgzip -f snp154_GCF_000001405.38
tabix -p vcf snp154_GCF_000001405.38.gz
```

### GAN

It refers to the reference sequences for each chromosome, often part of databases such as GenBank or the Genome Reference Consortium (GRC).
The information is available for instance in `snp154_GCF_000001405.*` above.

| Chromosome | hg19 (GRCh37)       | hg38 (GRCh38)       |
|------------|---------------------|---------------------|
| 1          | NC_000001.10        | NC_000001.11        |
| 2          | NC_000002.11        | NC_000002.12        |
| 3          | NC_000003.11        | NC_000003.12        |
| 4          | NC_000004.11        | NC_000004.12        |
| 5          | NC_000005.9         | NC_000005.10        |
| 6          | NC_000006.11        | NC_000006.12        |
| 7          | NC_000007.13        | NC_000007.14        |
| 8          | NC_000008.10        | NC_000008.11        |
| 9          | NC_000009.11        | NC_000009.12        |
| 10         | NC_000010.10        | NC_000010.11        |
| 11         | NC_000011.9         | NC_000011.10        |
| 12         | NC_000012.11        | NC_000012.12        |
| 13         | NC_000013.10        | NC_000013.11        |
| 14         | NC_000014.8         | NC_000014.9         |
| 15         | NC_000015.9         | NC_000015.10        |
| 16         | NC_000016.9         | NC_000016.10        |
| 17         | NC_000017.10        | NC_000017.11        |
| 18         | NC_000018.9         | NC_000018.10        |
| 19         | NC_000019.9         | NC_000019.10        |
| 20         | NC_000020.10        | NC_000020.11        |
| 21         | NC_000021.8         | NC_000021.9         |
| 22         | NC_000022.10        | NC_000022.11        |
| X          | NC_000023.10        | NC_000023.11        |
| Y          | NC_000024.9         | NC_000024.10        |

The "NC_" prefix stands for "nucleotide accession" and is part of the standard naming convention used in genomics databases.

An observation can be made such that the chromosome number in column 1 corresponds to the middle field between the _ and . delimiters in column 2 so would be extracted with `awk -F'[._]' '{print $2+0}'` but as a column in a file requires different handling (below).

The counterpart for RSid -- SNPid pairing can be furnished as follows,

```bash
bcftools query -f '%CHROM\t%POS\t%ID\t%REF\t%ALT[\t%SAMPLE=%GT]\n' snp154_GCF_000001405.25.gz | \
awk '{gsub(/NC_[0]+|\.[0-9]+/,"",$1);print $3,$1":"$2":"$4":"$5}'
```

It is also handy to append `-r NC_000001.10:10001` in the bcftools query for a specific region.

### hg38 reference genome assembly

Besides standard chromosomal positions, it also has other categories[^hg38],

> * **Random contigs** (e.g., chrY_KI270740v1_random). Unlocalized sequences that are known to originate from specic chromosomes, but whose exact location within the chromosomes is not known (e.g., chr9_KI270720v1_random).

> * **ChrUn** (e.g., chrUn_GL000218v1). Unplaced sequences that are known to originate from the human genome, but which cannot be condently placed on a specic chromosome.

> * **EBV** (e.g., -). Epstein-Barr virus sequence, representing the genome of Human herpes virus 4 type 1, the cause of infectious mononucleosis. This disease results in fever, sore throat, and enlarged cervical lymph nodes. About 98% of adults have been exposed to the virus by the age of 40 years. Since the virus remains latent in the body after infection, it is very common to nd EBV sequences when performing human genome sequencing.

> * **HLA** (e.g., HLA-C*01:08). Human leukocyte antigen (HLA) sequences representing selected alleles of the HLA A, B, C, DQA1, DQB1, and DRB1 loci. The HLA loci encode the major histocompatibility complex (MHC) proteins and are highly variable in the population.

> * **Decoy sequences** (e.g., chrUn_KN707606v1_decoy). A major motivation for including the \decoy" sequences in the reference genome is that if a sample actually contains a genomic segment that is not in the reference assembly, aligners may spend a lot of CPU time trying to nd a good match, or worse, aligners may wrongly assign the reads with a low mapping quality to segments with similar sequences in the reference genome. If the segment matches to one of the decoy contigs and the decoy is included in the reference, then the segment will quickly be assigned to the decoy and prevent the aligner from uselessly searching for other matches. Thus, the decoy sequences are a pragmatic solution to this, contain EBV and human sequences that in eect \catch" reads that would otherwise map with low quality to other regions in the reference and lead to unnecessary computation and avoid false variant calls related to false mappings.

> * **Alternate contigs** (e.g., chr3_KI270778v1_alt). Alternative sequence paths in regions with complex structural variation in the form of additional locus sequences.

## Examples

These are only for illustrative purpose, as it may be preferable to use full GWAS summary statistics in the case of T2D and proteogenomic examples.

### BMI

We take data reprted by Locke, et al. (2015) as example which requires build 37 positions from UCSC described above.
```
# GWAS summary statistics
wget http://portals.broadinstitute.org/collaboration/giant/images/1/15/SNP_gwas_mc_merge_nogc.tbl.uniq.gz
gunzip -c SNP_gwas_mc_merge_nogc.tbl.uniq.gz |
awk 'NR>1' | \
sort -k1,1 | \
join -11 -23 - snp150.txt | \
awk '($9!="X" && $9!="Y" && $9!="Un")' | \
gzip -f > bmi.tsv.gz
```
where file containing the GWAS summary statistics is downloaded, its header dropped, sorted and positional information added leading to a compressed file named [bmi.tsv.gz](bmi.tsv.gz).
We also filter out nonautosomal SNPs.

The list of 97 SNPs can be extracted as follows,
```bash
R --no-save <<END
library(openxlsx)
xlsx <- "https://www.nature.com/nature/journal/v518/n7538/extref/nature14177-s2.xlsx"
snps <- read.xlsx(xlsx, sheet = 4, colNames=FALSE, skipEmptyRows = FALSE, cols = 1, rows = 5:101)
snplist <- sort(as.vector(snps[,1]))
write.table(snplist, file="97.snps", row.names=FALSE, col.names=FALSE, quote=FALSE)
END
```
The list is SNPs is contained in [97.snps](97.snps).

As described elsewhere, we are rather tempted to use a distance-based approach for independent signals,
```bash
(
  awk -vOFS="\t" 'BEGIN{print "MarkerName", "A1", "A2", "freq", "Effect", "StdErr", "P.value", "N", "Chrom", "End"}'
  zcat bmi.tsv.gz | \
  sed 's/ /\t/g' | \
  awk '(NR>1 && $7<=2.4e-7)' | \
  sort -k9,9n -k10,10n
) > bmi.dat
R --no-save -q < bmi.R > bmi.out
grep  -f 97.snps bmi.out | \
wc -l
```
with `bmi.R` as follows,
```r
options(echo=FALSE)
bmi <- read.delim("bmi.dat",as.is=TRUE)
require(reshape)
require(gap)
chrs <- with(bmi,unique(Chrom))
for(chr in chrs)
{
#  print(chr)
  ps <- subset(bmi[c("Chrom","End","MarkerName","Effect","StdErr","P.value")],Chrom==chr)
  row.names(ps) <- 1:nrow(ps)
  sentinels(ps,chr,1)
}
```
and the output is [bmi.out](bmi.out). One may wonder the overlap between the two, and the answer is 69. In this case,
awk has no problem to handle the small P value, i.e., with
```bash
(
  awk -vOFS="\t" 'BEGIN{print "MarkerName", "A1", "A2", "freq", "Effect", "StdErr", "P.value", "N", "Chrom", "End"}'
  zcat bmi.tsv.gz | \
  sed 's/ /\t/g' | \
  awk '
  function abs(x)
  {
    if (x < 0) return -x;
    else return x;
  }
  (NR>1 && abs($5/$6)>=5.165342)' | \
  sort -k9,9n -k10,10n
) > bmi.dat

R --no-save -q < bmi.R > bmi.out

grep  -f 97.snps bmi.out | \
wc -l
```
we still have 69.

### T2D

The data was reported by Scott, et al. (2017),
```
R -q --no-save <<END

library(openxlsx)
library(dplyr)

xlsx <- "http://diabetes.diabetesjournals.org/highwire/filestream/79037/field_highwire_adjunct_files/1/DB161253SupplementaryData2.xlsx"

# Supplementary Table 3. Results for established, novel and additional distinct signals from the main analysis.
ST3 <- read.xlsx(xlsx, sheet = 3, colNames=TRUE, skipEmptyRows = FALSE, cols = 1:20, rows = 2:130) %>%
       rename(P="p-value.in.stage.1") %>% within(
       {
          beta=log(OR)
          L <- as.numeric(substr(CI,1,4))
          U <- as.numeric(substr(CI,6,9))
          se=abs(log(L)-log(U))/3.92
       }) %>% select(
          SNP=rsid,
          A1=EA,
          A2=NEA,
          freqA1=EAF,
          beta,
          se,
          P,
          N=Sample.size,
          chr=Chr,
          pos=Position_b37
       )
write.table(ST3, file="ST3", row.names=FALSE, col.names=FALSE, quote=FALSE)

# Supplementary Table 4. BMI-unadjusted association analysis model
ST4 <- read.xlsx(xlsx, sheet = 4, colNames=TRUE, skipEmptyRows = FALSE, cols = 1:12, rows = 3:132) %>% rename(
          "CI"="CI.95%",
          "P"="P-value") %>% within(
       {
          beta=log(OR)
          L <- as.numeric(substr(CI,1,4))
          U <- as.numeric(substr(CI,6,9))
          se=abs(log(L)-log(U))/3.92
          P=2*(1-pnorm(abs(beta/se)))
       }) %>% select(
          SNP=rsid,
          A1=allele1, 
          A2=allele2,
          freqA1=freq1,
          beta,
          se,
          P,
          N,
          chr,
          pos=position_b37
       )
write.table(ST4, file="ST4", row.names=FALSE, col.names=FALSE, quote=FALSE)

END
```
where we generate data based on the paper's supplementary tables ST3 and ST4; the former is in line with the paper (by 
specifying  `_db`=depict and `p_threshold`=0.00001 when calling PW-pipeline).

### Plasma proteins

The data was reported by Sun, et al. (2018),
```bash
R -q --no-save <<END
  require(openxlsx)
  xlsx <- "https://static-content.springer.com/esm/art%3A10.1038%2Fs41586-018-0175-2/MediaObjects/41586_2018_175_MOESM4_ESM.xlsx"
# Supplementary Table 4
  ST4 <- read.xlsx(xlsx, sheet=4, colNames=TRUE, skipEmptyRows=FALSE, cols=c(6:8,11:13,23:25), rows=6:1986)
  names(ST4) <- c("SNP","Chr","Pos","A1","A2","EAF","b","se","p")
  write.table(ST4, file="plamsprotein", row.names=FALSE, col.names=FALSE, quote=FALSE)
END
```
and Supplementary Table 4 is fetched here.

## Relations to other resources

[lz.sh](lz.sh) is a script which extracts information on SNP and their positions from [LocusZoom](http://locuszoom.sph.umich.edu/) 1.4 database.

It might be worthwhile to check for options with the sumstats as defined in ldsc, <https://github.com/bulik/ldsc>, and particularly its `munge_sumstats.py` utility.

One may prefer to use the following repository to convert GWAS sumstats for downstream processing,

<https://github.com/MRCIEU/gwas2vcf>

The VCF files generated can be converted to the format here with command
```bash
bcftools query -f '%ID\t%ALT\t%REF\t%AF\t[%ES]\t[%SE]\t[%LP]\t[%SS]\t%CHROM\t%POS\n' my.vcf.gz | \
awk -vOFS="\t" '{$7=10^-$7};1'> my.tsv
```

The software suite now has its own liftover facility, as in <https://doi.org/10.1093/bioinformatics/btae038>.

A relatively earlier repository with specific focus on download of sumstats is as follows,

<https://github.com/mikegloudemans/gwas-download>

The OpenTargets harmoniser is available from here, <https://github.com/opentargets/genetics-sumstat-harmoniser>.

UCSC FAQ on data file formats, <https://genome.ucsc.edu/FAQ/FAQformat.html>

## References

**[GIANT](https://portals.broadinstitute.org/collaboration/giant/index.php/Main_Page)** (Genetic Investigation of ANthropometric Traits) data

Locke AE, et al. (2015) Genetic studies of body mass index yield new insights for obesity biology. *Nature* 518(7538):197-206. doi: 10.1038/nature14177

**[DIAGRAM](http://www.diagram-consortium.org/)** (DIAbetes Genetics Replication And Meta-analysis) data

Scott R, et al. (2017) An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. *Diabetes* 66:2888–2902.

**[Protein website](http://www.phpc.cam.ac.uk/ceu/proteins/) at Cardiovascular Epidemiology Unit (CEU)**

Sun BB, et al. (2018) Genomic atlas of the human plasma proteome. *Nature* 558: 73-79.

GWAS catalog summary statistics API, <https://www.ebi.ac.uk/gwas/summary-statistics/docs/>

---

[^hg18]: Build 36, <https://hgdownload.soe.ucsc.edu/goldenPath/hg18/database/> (snp[126--30].sql, snp[126--30].txt.gz)

[^tabix]: Alternatively, tabix can also be used as

```bash
    tabix -S38 -s1 -b2 -e2 -f snp154_hg19.vcf.gz
    tabix -S38 -s1 -b2 -e2 -f snp154_hg38.vcf.gz
    ```

[^hg38]: Robinson PN, Piro RM, Jager K (2018). [Computational Exome and Genome Analysis](https://www.crcpress.com/Computational-Exome-and-Genome-Analysis/Robinson-Piro-Jager/p/book/9781498775984), CRC.

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