RápidoPGS is a tool to quickly compute polygenic scores (PGS) from GWAS summary statistics datasets from either case-control (eg. asthma) or quantitative traits (eg. height and BMI).
Input GWAS summary statistics datasets should have a minimum set of columns, with these names, but in any order:
Also, if doing a PGS on a quantitative trait GWAS dataset, your file must include this:
Current RápidoPGS version (v2.1.0) is available on CRAN, so we can install it using the following code
Alternatively, you can download the development version from Github
(Note: If you don’t have remotes installed, please install
it first:
Once installed, let’s load it. This will automatically load all required dependencies.
GWAS catalog is an outstanding GWAS summary statistics data source, as it not only puts together tons of public datasets, but it also uses a semi-automatic pipeline to apply quality control and liftover (a.k.a. harmonise) those datasets, thus helping overcome the problem of having GWAS sumstats in so many different formats and builds.
In this vignette, we’ll use GWAS catalog to obtain an example dataset. For this vignette we’ll use a Breast cancer (BRCA) dataset from Michailidou et al., 2017, which is one that we used in our manuscript. This GWAS was performed on 119078 controls and 137045 cases of Breast cancer.
It’s a big file, and may take a while to download, so here we will show the command to download, but actually cheat and load a subset of data already loaded.
Note that in this particular case we chose a study that contained a
single dataset. In other cases there may be more than one. In that case
gwascat.download will prompt you with the list of available
options, prompting their accession numbers, and asking you to choose a
file by its number in the list, if running interactively.
We use it’s PubMed ID (29059683). When running
gwascat.download without any other arguments, it will try
to get the harmonised files associated with the ID. Harmonised files
have been processed by GWAS catalog and are formatted and aligned to the
lastest genome build. See here
for more information.
Once we download the file, we’ll need to prepare it for RápidoPGS. That will involve renaming columns to something RápidoPGS can understand, and this is easy to do with data.table. Also, RápidoPGS use only autosomes, so remove X or Y chromosomes at this step.
ds <- gwascat.download(29059683)
# Select the harmonised hg38 file
# This is equivalent to:
# ds <- fread("ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST004001-GCST005000/GCST004988/harmonised/29059683-GCST004988-EFO_0000305.h.tsv.gz")
# Then apply some reformatting
setnames(ds, old = c("hm_rsid","hm_chrom","hm_pos", "hm_other_allele", "hm_effect_allele", "hm_effect_allele_frequency", "hm_beta", "standard_error", "p_value"), new = c("SNPID","CHR", "BP", "REF","ALT","ALT_FREQ", "BETA", "SE", "P"))
ds <- ds[,.(SNPID, CHR, BP, REF, ALT, ALT_FREQ, BETA, SE, P)]
ds <- ds[CHR !="X"]
ds$CHR <- as.numeric(ds$CHR)
ds <- ds[order(CHR, BP)]
ds <- na.omit(ds, cols = c("BETA", "ALT_FREQ"))For illustrative purposes, I took a random subset from this file including 100,000 SNPs from this file, which we’ll use in this tutorial. Bear in mind that the final PGS won’t be a valid model since we randomly removed most SNPs. It will serve for our teaching purposes, though! ;) We can load this file straight away.
The first thing to do is to check what our file looks like. RápidoPGS can handle NAs in crucial columns, so don’t worry too much about that (unless you have all NA for crucial columns, of course!).
A note of caution when dealing with GWAS catalog files, though: since they use a semi-automated pipeline, it is possible that even some columns are present, they are empty, so be careful with that!
Also, RápidoPGS requires allele frequencies, so it’s possible that you need to compute it from an external reference panel (eg. 1000 Genomes Phase III). We don’t cover that step in this vignette, but we might write instructions explaining how to do it in the future.
Lastly, we applied a number of QC steps in our paper, which we won’t apply here, but encourage you to try when using real data. The details of this procedure are described in the paper. You can also take a look at the code here.
Let’s now look at the file.
## SNPID CHR BP REF
## Length:100000 Min. : 1.000 Min. : 19484 Length:100000
## Class :character 1st Qu.: 4.000 1st Qu.: 32314220 Class :character
## Mode :character Median : 7.000 Median : 69249390 Mode :character
## Mean : 8.612 Mean : 78711220
## 3rd Qu.:13.000 3rd Qu.:114464115
## Max. :22.000 Max. :248922216
## ALT ALT_FREQ BETA SE
## Length:100000 Min. :0.0051 Min. :-2.1291000 Min. :0.00620
## Class :character 1st Qu.:0.0205 1st Qu.:-0.0100000 1st Qu.:0.00740
## Mode :character Median :0.1037 Median :-0.0003000 Median :0.01140
## Mean :0.2245 Mean :-0.0009533 Mean :0.01949
## 3rd Qu.:0.3571 3rd Qu.: 0.0094000 3rd Qu.:0.02540
## Max. :0.9949 Max. : 0.6442000 Max. :0.83960
## P N
## Min. :0.0000 Min. :256123
## 1st Qu.:0.1922 1st Qu.:256123
## Median :0.4470 Median :256123
## Mean :0.4597 Mean :256123
## 3rd Qu.:0.7174 3rd Qu.:256123
## Max. :0.9991 Max. :256123In this case, we don’t have any missing data, which is fantastic.
Let’s now compute our PGS! The build of this example file is hg38, so we must tell RápidoPGS about that (default is hg19).
In this new version, we don’t need sample size for case-control datasets. Note that if this was a quantitative trait dataset, we should inform total number of individuals (N parameter). Also, if our dataset had columns reporting the number of individuals for each SNP, we can replace N by a string specifying the column (eg. N = “sample_size”). By doing so, RápidoPGS-single will extract this information directly from the columns.
Let’s get our hands dirty! Let’s compute first a full RápidoPGS-single model.
Advanced use note: You may want to filter by and
align your dataset to an external reference. You can do that with
RápidoPGS, too, by specifying the path of your reference file at
reference argument. This reference file should have five
columns (CHR, BP, REF, ALT, and SNPID) and be in the same
build as our summary statistics dataset. RápidoPGS currently
supports hg19 and hg38 builds.
## Assigning LD blocks...## Done!## Computing a RapidoPGS-single model for a case-control dataset...Well, that was rápido! With increasingly big datasets, it will take a bit longer, of course.
Let’s take a look.
## SNPID CHR BP REF ALT ALT_FREQ BETA SE P
## <char> <num> <int> <char> <char> <num> <num> <num> <num>
## 1: rs76197520 2 49673981 C T 0.0603 0.0149 0.0131 0.2540
## 2: rs12232959 2 149575863 C G 0.7818 -0.0035 0.0076 0.6450
## 3: rs7621633 3 7330932 C T 0.7460 -0.0045 0.0089 0.6151
## 4: rs11129616 3 34773314 A G 0.6731 -0.0060 0.0069 0.3895
## 5: rs62282714 3 150895438 T C 0.1539 -0.0052 0.0113 0.6439
## 6: rs62375579 5 109299301 G A 0.1909 0.0080 0.0102 0.4347
## N ld.block ppi weight
## <num> <int> <num> <num>
## 1: 256123 135 1.244593e-05 1.854443e-07
## 2: 256123 178 4.221391e-06 -1.477487e-08
## 3: 256123 221 5.050474e-06 -2.272713e-08
## 4: 256123 235 5.029883e-06 -3.017930e-08
## 5: 256123 287 6.268896e-06 -3.259826e-08
## 6: 256123 457 6.922043e-06 5.537634e-08We see three new columns: ld.block corresponds to the ld-detect LD block number assigned to the SNP (see manuscript for more details of where this comes from), ppi is the posterior probability that the SNP is causal, and weight is the weighted effect size (BETA * ppi) - and the column we’re interested in.
Imagine that we want a small portable PGS. We could use a threshold (eg. keep only SNPs with ppi larger than 1e−4, a reasonable threshold) or keep the top SNPs with largest weights (in either way).
We can do that using RápidoPGS, let’s see how by using a 1e−4 threshold.
For accuracy reasons, we recommend recomputing the PGS on just these
SNPs after the threshold was applied, so recalc = TRUE by
default.
## Assigning LD blocks...## Done!## Computing a RapidoPGS-single model for a case-control dataset...## SNPID CHR BP REF ALT ALT_FREQ BETA SE P
## <char> <num> <int> <char> <char> <num> <num> <num> <num>
## 1: rs143863360 15 75910714 G T 0.0222 0.0611 0.0265 0.02123
## N ld.block ppi weight
## <num> <int> <num> <num>
## 1: 256123 1112 0.0001789831 1.093587e-05You can omit recalculation by setting that argument to
FALSE
PGS_1e4_norecalc <- rapidopgs_single(ds, trait ="cc", build = "hg38", filt_threshold = 1e-4, recalc = FALSE)## Assigning LD blocks...## Done!## Computing a RapidoPGS-single model for a case-control dataset...## SNPID CHR BP REF ALT ALT_FREQ BETA SE P
## <char> <num> <int> <char> <char> <num> <num> <num> <num>
## 1: rs143863360 15 75910714 G T 0.0222 0.0611 0.0265 0.02123
## N ld.block ppi weight
## <num> <int> <num> <num>
## 1: 256123 1112 0.0001789831 1.093587e-05And what if we want the top 10 SNPs? Just change the
filt_threshold argument. If it’s larger than 1, RápidoPGS
will understand that you want a top list, rather than a thresholded
result.
## Assigning LD blocks...## Done!## Computing a RapidoPGS-single model for a case-control dataset...## SNPID CHR BP REF ALT ALT_FREQ BETA SE P
## <char> <num> <int> <char> <char> <num> <num> <num> <num>
## 1: rs143863360 15 75910714 G T 0.0222 0.0611 0.0265 0.02123
## 2: rs55817933 19 36970383 T C 0.3584 -0.0154 0.0065 0.01691
## 3: rs149557496 9 35906658 G A 0.0152 0.0297 0.0313 0.34330
## 4: rs72867697 17 76595643 T A 0.1731 -0.0165 0.0095 0.08168
## 5: rs76197520 2 49673981 C T 0.0603 0.0149 0.0131 0.25400
## 6: rs61932677 12 98186323 G A 0.1930 0.0096 0.0093 0.30170
## N ld.block ppi weight
## <num> <int> <num> <num>
## 1: 256123 1112 1.789831e-04 1.093587e-05
## 2: 256123 1257 5.361020e-05 -8.255971e-07
## 3: 256123 757 2.399329e-05 7.126007e-07
## 4: 256123 1198 2.136836e-05 -3.525779e-07
## 5: 256123 135 1.244593e-05 1.854443e-07
## 6: 256123 978 7.904310e-06 7.588138e-08So those are examples of basic RápidoPGS-single usage!
Drop us a line if you encounter problems, and we’ll be happy to help.
Good luck!
Guillermo