New!! A standalone version of lassosum is now available. Please see here for details.
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.
Summary statistics are expected to be loaded into memory as a data.frame/data.table.
lassosum requires the following R packages: RcppArmadillo, data.table, Matrix. Install them by:
install.packages(c("RcppArmadillo", "data.table", "Matrix"), dependencies=TRUE)For Windows and Mac users, it would be easiest to download the following binaries (Windows, Mac) and install using:
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. Mac users should refer to this page for the various dependencies required. Install then via:
install.packages("/path/to/downloaded_source.tar.gz", repos=NULL, type="source")If you have devtools, you can also type:
install_github("tshmak/lassosum@v0.4.5")or
install_github("tshmak/lassosum")for the latest development version. Or you can clone the latest development version here and install yourself using devtools.
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.
If you are using lassosum for cross prediction, please refer to the manual here
Otherwise, run the following:
library(lassosum)
setwd(system.file("data", package="lassosum")) # Directory where data and LD region files are storedFirst 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.
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)
To run lassosum, we need to input SNP-wise correlations. This can be converted from p-values via the p2cor function.
library(lassosum)
cor <- p2cor(p = ss$P_val, n = 60000, sign=log(ss$OR_A1))
# n is the sample sizeRunning lassosum using standard pipeline:
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 for details.
### Split-validation ###
sv <- splitvalidate(out)Note that parallel processing is done by LDblocks.
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)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.
v <- validate(out, covar=covar)
# covar <- rnorm(nrow.bfile(out$test.bfile)) # If you need a dummy for testingSince v0.4.2, the covar argument in validate and splitvalidate can also take a data.frame with the first 2 columns headed by FID and IID, and the other columns being covariates (any headers). It can also be a file name for such a data.frame.
To apply the best lassosum predictor (indexed by s and lambda) to a new dataset, first subset the lassosum.pipeline object. Then validate again:
out2 <- subset(out, s=v$best.s, lambda=v$best.lambda)
v2 <- validate(out2, covar=covar, test.bfile="Some_new_bfile")To use subsamples of either the test dataset or the reference panel, use the keep.test and keep.ref options respectively in lassosum.pipeline. Type ?lassosum.pipeline to see what format these options can take. To use only a subset of SNPs for inference, remove the unnecessary SNPs from the summary statistics file.
In large datasets, it is typical for the PLINK data to be organized by chromosomes. For example, say we have chr1.bed and chr2.bed. It is much faster to carry out lassosum by chromosome, and then combine them together. An example:
out1 <- lassosum.pipeline(..., test.bfile="chr1")
out2 <- lassosum.pipeline(..., test.bfile="chr2")
out <- merge(out1, out2)
v <- validate(out)To guard against taking too long to run, lassosum.pipeline will complain if the size of the reference panel is too large. Currently "too large" is taken to mean greater than 20000. lassosum.pipeline will suggest that you take a sample of, say, 5000 as the reference panel using the sample= option. Note that if you run lassosum.pipeline across different chromosomes, you need to keep the sample the same. You can do so by running set.seed() before lassosum.pipeline. Alternatively, specify the exact sample with the keep.ref= option.
In general, it is a good idea to anticipate the test sample you will need in the lassosum.pipeline stage rather than specify them at the validate stage, using the keep.test= option. This is especially the case if you need to specify the pheno or the covar option in validate. The reason is that if a different test sample is used (or if a different test.bfile is specified) in validate, then the polygenic score is re-calculated from the betas, this can be very time consuming. In a future version, I may allow lassosum.pipeline to take pheno and covar as argument so as to identify the exact test sample to circumvent this issue.
The exact SNPs used in calculating the PGS are given in the out$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.
Further documentation for various functions can be obtained by running help from R, e.g.
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.