LDpred is a Python based software package that adjusts GWAS summary statistics for the effects of linkage disequilibrium (LD). The details of the method is described in Vilhjalmsson et al. (AJHG 2015) [http://www.cell.com/ajhg/abstract/S0002-9297(15)00365-1]
Recent improvements have focused on making LDpred more robust, addressing issues highlighted by recent publications (Ge et al., Nat Comm 2019; Choi and O'Reilly, GigaScience 2019; Privé et al., AJHG 2019).
Nov 20th, 2019, v. 1.0.11: Implemented LDpred-fast Joel Mefford's sparsified BLUP prediction (Mefford, thesis 2018). LDpred-fast is suitable for polygenic diseases/traits when LDpred-gibbs fails to converge or is too slow.
Oct 21st, 2019, v. 1.0.10: LDpred-gibbs now reports LDpred-inf effects for SNPs in long-range LD regions (Price et al., AJHG 2008). This improves convergence of the algorithm substantially when applied to large datasets.
Oct 17st, 2019, v. 1.0.8: Fixed a bug in LDpred that could improve convergence for gibbs.
Oct 11th, 2019, v. 1.0.7: Improved accuracy and robustness.
LDpred can be installed using pip on most systems by typing
pip install ldpred
LDpred currently requires three Python packages to be installed and in path. These are h5py http://www.h5py.org/, scipy http://www.scipy.org/ and libplinkio https://github.com/mfranberg/libplinkio. Lastly, LDpred has currently only been tested with Python 3.6+.
The first two packages h5py and scipy are commonly used Python packages, and pre-installed on many computer systems. The last libplinkio package can be installed using pip (https://pip.pypa.io/en/latest/quickstart.html), which is also pre-installed on many systems.
With pip, one can install libplinkio using the following command:
pip install plinkio
or if you need to install it locally you can try
pip install --user plinkio
With these three packages in place, you should be all set to install and use LDpred.
As with most Python packages, configurating LDpred is simple. You can use pip to install it by typing
pip install ldpred
This should automatically take care of dependencies. The examples below assume ldpred has been installed using pip.
Alternatively you can use git (which is installed on most systems) and clone this repository using the following git command:
git clone https://github.com/bvilhjal/ldpred.git
Finally, you can also download the source files and place them somewhere.
With the Python source code in place and the three packages h5py, scipy, and libplinkio installed, then you should be ready to use LDpred.
A couple of simulated data examples can be found in the test_data directory. These datasets were simulated using two different values of p (fraction of causal markers) and with heritability set to 0.1. The sample size used when simulating the summary statistics is 10,000.
I encourage users to extend the code, and adapt it too their needs. Currently there are no formal guidelines set for contributions, and pull requests will be reviewed on a case by case basis.
If you have any questions or trouble getting the method to work, try first to look at issues, to see if it is reported there. Also, you can check if some of the cloned LDpred repos have addressed your issue.
In emergencies, please contact Bjarni Vilhjalmsson (bjarni.vilhjalmsson@gmail.com), but expect slow replies.
A typical LDpred workflow consists of 3 steps:
The first step is a data synchronization step, where two or three data sets, genotypes and summary statistics are synchronized. This generates a HDF5 file which contains the synchronized genotypes. This step can be done by running
ldpred coord
use --help for detailed options. This step requires at least one genotype file (the LD reference genotypes), where we recommend at least 1000 unrelated individuals with the same ancestry make-up as the individuals for which summary statistics datasets are obtained from. Another genotype file can also be given if the user intends to validate the predictions using a separate set of genotypes.
After generating the coordinated data file then the one can apply LDpred and run it on the synchronized dataset. This step can be done by running
ldpred gibbs
use --help for detailed options. This step generates two files, a LD file with LD information for the given LD radius, and the re-weighted effect estimates. The LD file enables the user to not have to generate the LD file again when trying, e.g., different values of p (the fraction of causal variants). However, it is re-generated if a different LD radius is given. The other file that LDpred generates contains the LDpred-adjusted effect estimates.
Individual risk scores can be generated using the following command
ldpred score
use --help for detailed options. It calculates polygenic risk scores for the individuals in the validation data if given, otherwise it treats the LD reference genotypes as validation genotypes. A phenotype file can be provided, covariate file, as well as plink-formatted principal components file.
In addition to the LDpred gibbs sampler and infinitesimal model methods, the package also implements LD-pruning + Thresholding as an alternative method. You can run this using the following command
ldpred p+t
This method often yields better predictions than LDpred when the LD reference panel is small, or when the training data is very large (due to problems with gibbs sampler convergence).
As of v. 1.0.11 you can run tests to see if LDpred work on your system after installtion by running the following commands
ldpred-unittest
ldpred-inttest
Note that passing these tests does not guarantee that LDpred work in all situations.
Please cite this paper
Thanks to all who provided bug reports and contributed code.