SLDP (Signed LD Profile) regression
SLDP regression is a method for looking for a directional effect of a signed functional annotation on a heritable trait using GWAS summary statistics. This repository contains code for the SLDP regression method as well as tools required for preprocessing data for use with SLDP regression.
Installation
First, make sure you have a python distribution installed that includes scientific computing packages like numpy/scipy/pandas as well as the package manager pip; we recommend Anaconda.
To install sldp, type the following command.
pip install sldp
This should install both sldp as well as any required packages, such as gprim and ypy.
If you prefer to install sldp without pip, just clone this repository, together with gprim and ypy, and add an entry for each into your python path.
Getting started
To verify that the installation went okay, run
sldp -h
to print a list of all command-line options. If this command fails, there was a problem with the installation.
Once this works, take a look at our wiki for a short tutorial on how to use sldp.
Where can I get signed LD profiles?
You can download signed LD profiles (as well as raw signed functional annotations) for ENCODE ChIP-seq experiments from the sldp data page. These signed LD profiles were created using 1000 Genomes Phase 3 Europeans as the reference panel.
Where can I get reference panel information such as SVDs of LD blocks and LD scores?
You can download all required reference panel information, computed using 1000 Genomes Phase 3 Europeans, from the sldp data page.
Citation
If you use sldp, please cite
