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lassosum

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LASSO for GWAS with summary statistics

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lassosum [![Build Status](https://travis-ci.org/tshmak/lassosum.svg?branch=master)](https://travis-ci.org/tshmak/lassosum)
=======================
*New!! A standalone version of `lassosum` is now available. Please see [here](https://github.com/tshmak/lassosum/blob/master/lassosum_standalone.md#lassosum-standalone-version-for-linux) for details.*

### Description

`lassosum` is a method for computing LASSO/Elastic Net estimates of a linear regression problem given summary statistics from GWAS and Genome-wide meta-analyses, accounting for Linkage Disequilibrium (LD), via a reference panel.
The reference panel is assumed to be in PLINK 1 [format](https://www.cog-genomics.org/plink/1.9/input#bed).
Summary statistics are expected to be loaded into memory as a data.frame/data.table.

### Reference
[Mak _et al_ (2017) Polygenic scores via penalized regression on summary statistics. _Genetic Epidemiology_ 41(6) 469-480](https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22050)

### Installation

`lassosum` requires the following R packages: `RcppArmadillo`, `data.table`, `Matrix`. Install them by:

```r
install.packages(c("RcppArmadillo", "data.table", "Matrix"), dependencies=TRUE)
```
For Windows and Mac users, it would be easiest to download the following binaries ([Windows](https://github.com/tshmak/lassosum/releases/download/v0.4.5/lassosum_0.4.5.zip), [Mac](https://github.com/tshmak/lassosum/releases/download/v0.4.5/lassosum_0.4.5.tgz)) and install using: 
```r
install.packages("/path/to/downloaded_binary_file", repos=NULL)
```

If you are on Linux or you would like to compile from source, you can download the source codes [lassosum_0.4.5.tar.gz](https://github.com/tshmak/lassosum/releases/download/v0.4.5/lassosum_0.4.5.tar.gz). Mac users should refer to [this page](https://cran.r-project.org/bin/macosx/tools/) for the various dependencies required. Install then via: 
```r
install.packages("/path/to/downloaded_source.tar.gz", repos=NULL, type="source")
```

If you have `devtools`, you can also type: 
```r
install_github("tshmak/lassosum@v0.4.5")
```
or
```r
install_github("tshmak/lassosum")
```
for the latest development version. Or you can clone the latest development version here and install yourself using `devtools`.

### Warning!

Most functions in `lassosum` impute missing genotypes in PLINK bfiles with a homozygous A2 genotype, which is the same as using the `--fill-missing-a2` option in PLINK. It is the user's responsibility to filter out individuals and SNPs with too many missing genotypes beforehand.

### Tutorial

_If you are using lassosum for __cross prediction__, please refer to the manual [here](https://github.com/tshmak/crosspred)_

Otherwise, run the following: 
```r
library(lassosum)
setwd(system.file("data", package="lassosum")) # Directory where data and LD region files are stored
```

First we read the summary statistics into R, and provide the `bfile` names of the reference panel and the test data. If only the reference panel is provided then only the beta coefficients (no polygenic scores) are calculated. You can then apply these subsequently to a test dataset using `validate` or `pseudovalidate`. If no reference panel is provided, then the test data is taken as the reference panel.

```r
library(data.table)

### Read summary statistics file ###
ss <- fread("summarystats.txt")
head(ss)

### Specify the PLINK file stub of the reference panel ###
ref.bfile <- "refpanel"

### Specify the PLINK file stub of the test data ###
test.bfile <- "testsample"

### Read LD region file ###
LDblocks <- "EUR.hg19" # This will use LD regions as defined in Berisa and Pickrell (2015) for the European population and the hg19 genome.
# Other alternatives available. Type ?lassosum.pipeline for more details. 
```
Reference: [Berisa and Pickrell (2015)](https://academic.oup.com/bioinformatics/article/32/2/283/1743626/Approximately-independent-linkage-disequilibrium)

To run `lassosum`, we need to input SNP-wise correlations. This can be converted from p-values via the `p2cor` function. 
```r
library(lassosum)
cor <- p2cor(p = ss$P_val, n = 60000, sign=log(ss$OR_A1))
# n is the sample size
```

Running lassosum using standard pipeline: 
```r
out <- lassosum.pipeline(cor=cor, chr=ss$Chr, pos=ss$Position, 
                         A1=ss$A1, A2=ss$A2, # A2 is not required but advised
                         ref.bfile=ref.bfile, test.bfile=test.bfile, 
                         LDblocks = LDblocks)

### Validation with phenotype ### 
v <- validate(out) # Use the 6th column in .fam file in test dataset for test phenotype
v <- validate(out, pheno=pheno) # Alternatively, specify the phenotype in the argument

# pheno <- rnorm(nrow.bfile(out$test.bfile)) # If you need a dummy for testing

### pseudovalidation ###
# install.packages("fdrtool")
v <- pseudovalidate(out)
```
Since v0.4.2, the `pheno` argument in `validate` can also take a `data.frame` with the first 2 columns headed by FID and IID, and the third column being the phenotype, or alternatively, a file name for such a data.frame. Moreover, a `v$results.table` object is also returned in `validate` and `pseudovalidate`, giving a table with the best PGS and the phenotype tabulated with the FID and IID (family and individual ID). *Note that `lassosum` doesn't understand the Plink convention of `-9` being NA. Change -9 to NA before running `validate()`!*

A new feature since v0.4.2 is `split-validation`, where the test dataset (`test.bfile`) is split in half using one half for validation and the other half for calculating PGS. The PGS in the two halves are then standardised and stacked back together. This avoids overfitting due to the overlapping of the target and the validation dataset. See [this paper](https://www.biorxiv.org/content/early/2018/07/30/252270) for details. 
```r
### Split-validation ###
sv <- splitvalidate(out)
```

#### Parallel processing with the `parallel` package
Note that parallel processing is done by `LDblocks`. 
```r
library(parallel)
cl <- makeCluster(2, type="FORK") # Parallel over 2 nodes
out <- lassosum.pipeline(cor=cor, chr=ss$Chr, pos=ss$Position, 
                         A1=ss$A1, A2=ss$A2, # A2 is not required but advised
                         ref.bfile=ref.bfile, test.bfile=test.bfile, 
                         LDblocks = LDblocks, cluster=cl)
```
#### Including covariates in validation
It is possible to include covariates in validation or splitvalidation (though not in pseudovalidation). Simply pass the covariate matrix as an argument to `validate` or `splitvalidate`. 
```r 
v <- validate(out, covar=covar)
# covar <- rnorm(nrow.bfile(out$test.bfile)) # If you need a dummy for testing
```
Since v0.4.2, the `covar` argument in `validate` and `splitvalidate` can also take a `data.frame` with the first 2 columns headed by FID and IID, and the other columns being covariates (any headers). It can also be a file name for such a data.frame.

#### Apply validated betas to new data 
To apply the best lassosum predictor (indexed by `s` and `lambda`) to a new dataset, first subset the `lassosum.pipeline` object. Then `validate` again: 
```r 
out2 <- subset(out, s=v$best.s, lambda=v$best.lambda)
v2 <- validate(out2, covar=covar, test.bfile="Some_new_bfile")
```

#### lassosum in subsamples  
To use subsamples of either the test dataset or the reference panel, use the `keep.test` and `keep.ref` options respectively in `lassosum.pipeline`. Type `?lassosum.pipeline` to see what format these options can take. To use only a subset of SNPs for inference, remove the unnecessary SNPs from the summary statistics file.

#### Estimation by chromosome 
In large datasets, it is typical for the PLINK data to be organized by chromosomes. For example, say we have `chr1.bed` and `chr2.bed`. It is much faster to carry out lassosum by chromosome, and then combine them together. An example: 
```r 
out1 <- lassosum.pipeline(..., test.bfile="chr1")
out2 <- lassosum.pipeline(..., test.bfile="chr2")
out <- merge(out1, out2)
v <- validate(out)
```
#### For datasets with large sample size in `ref.bfile`
To guard against taking too long to run, `lassosum.pipeline` will complain if the size of the reference panel is too large. Currently "too large" is taken to mean greater than 20000. `lassosum.pipeline` will suggest that you take a sample of, say, 5000 as the reference panel using the `sample=` option. Note that if you run `lassosum.pipeline` across different chromosomes, you need to keep the sample the same. You can do so by running `set.seed()` before `lassosum.pipeline`. Alternatively, specify the exact sample with the `keep.ref=` option.

#### For datasets with large sample size in `test.bfile`
In general, it is a good idea to anticipate the test sample you will need in the `lassosum.pipeline` stage rather than specify them at the `validate` stage, using the `keep.test=` option. This is especially the case if you need to specify the `pheno` or the `covar` option in `validate`. The reason is that if a different test sample is used (or if a different `test.bfile` is specified) in `validate`, then the polygenic score is re-calculated from the betas, this can be very time consuming. In a future version, I may allow `lassosum.pipeline` to take `pheno` and `covar` as argument so as to identify the exact test sample to circumvent this issue.

#### Extracting the lassosum betas 
The exact SNPs used in calculating the PGS are given in the `out$sumstats` data.frame, where `out` is the output from `lassosum.pipeline`. The order of the SNPs is the same as that of `v$best.beta` where `v` is the output from `validate`/`splitvalidate`/`psuedovalidate`.

### Support
Further documentation for various functions can be obtained by running `help` from `R`, e.g. 
```r
help(lassosum.pipeline)
help(validate)
help(pgs) # A low-level command for calculating PGS from betas 
```
If there are any further questions or problems with running or installing `lassosum`, please do email me at <timmak@yahoo.com>.

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