In practice, you need to build it for variants from all chromosomes. # Attach the "bigSNP" object in R session, # takes several minutes if you do not have many cores, Why clumping should be preferred over pruning, How to capture Population Structure with PCA (LD problem explained), How to capture Population Structure with PCA (directly on PLINK bed files), Computing polygenic scores using Stacked Clumping and Thresholding (SCT), the code to prepare summary statistics (including performing the quality control presented in the Methods section “Quality control of summary statistics” of the paper), the code to read BGEN files into the data format used by bigsnpr, https://doi.org/10.6084/m9.figshare.13034123. We split genotype data using part of the data to choose hyper-parameters and another part of the data to evaluate statistical properties of polygenic risk score such as AUC. The tutorial is separated into four main sections and reflects the structure of our guide paper: the first two sections on QC corres… We recommend to use a window size of 3 cM (see ref). You can download data and unzip files in R. We store those files in a directory called "tmp-data" here. Please look at the code linked at the beginning. length.out = 30). The only difference it makes is when building the SFBM (the sparse LD matrix on disk), you need to build it so that it contains all variants genome-wide (see e.g. You can find the accompany tutorial here PRSice-2: Polygenic Risk Score software PRSice (pronounced 'precise') is a Polygenic Risk Score software for calculating, applying, evaluating and plotting the results of polygenic risk scores (PRS) analyses. We assume that you have the following files (or you can download it from here): While we do provide a rough guide on how to perform LDpred on bed files separated into individual chromosomes, this script is untested and extra caution is required, On some server, you might need to first use the following code in order to run LDpred with multi-thread, LDpred2 authors recommend restricting the analysis to only the HapMap3 SNPs, Here, we know the exact ordering of the summary statistics file. Note that these data are for educational purposes only, not for use as a reference panel. Here, these are simulated data so all variants use the same strand and the same reference. You can see there how we generated these data from the 1000 Genomes project. Installing LDpred-2¶ Note. Some quality control on summary statistics is highly recommended (see paper). You should also probably look at the code of the paper, particularly at the code to prepare summary statistics (including performing the quality control presented in the Methods section “Quality control of summary statistics” of the paper), at the code to read BGEN files into the data format used by bigsnpr, at the code to prepare LD matrices and at the code to run LDpred2 (genome-wide). If no or few variants are actually flipped, you might want to disable the strand flipping option. The other 159 individuals are used as test set to evaluate the final models. Docs » lassosum; Edit on GitHub; Background¶ lassosum is one of the dedicated PRS programs which is an R package that uses penalised regression (LASSO) in its approach to PRS calculation. max AUC). In practice, if you do not really care about sparsity, you could choose the best LDpred2-grid model among all sparse and non-sparse models. # Read from bed/bim/fam, it generates .bk and .rds files. Alternatively, we also provide an LD reference to be used direcly, along with an example script on how to use it at https://doi.org/10.6084/m9.figshare.13034123. First, you need to read genotype data from the PLINK files (or BGEN files) as well as the text file containing summary statistics. You can install LDpred and its dependencies in R with the following command: For mac users, you might need to follow the guide here to be able to install LDpred2. Note. Here, we use the Z-Score from the regression of the phenotype by the PRS since we have found it more robust than using the AUC. Docs » LDpred-2; Edit on GitHub; Background¶ LDpred-2 is one of the dedicated PRS programs which is an R package that uses a Bayesian approach to polygenic risk scoring. LDpred2: better, faster, stronger. (2020). Basic Tutorial for Polygenic Risk Score Analyses. this code). Here, we have built the LD matrix using variants from one chromosome only. The aim of this tutorial is to provide a simple introduction to PRS analyses to those new to PRS, while equipping existing users with a better understanding of the processes and implementation "underneath the hood" of popular PRS software. Basic Tutorial for Polygenic Risk Score Analyses. We recommend to run many of them in parallel with different initial values for p (e.g. Docs » PLINK; Edit on GitHub; Background¶ On this page, you will compute PRS using the popular genetic analyses tool plink - while plink is not a dedicated PRS software, you can perform every required steps of the C+T approach with plink. Privé, F., Arbel, J., & Vilhjálmsson, B. J. # which are LD regions defined in Berisa and Pickrell (2015) for the European population and the hg19 genome. This is not the case here, which is probably because the data is so small. Get the final performance of the LDpred models, The genotype file after performing some basic filtering, This file contains the SNPs that passed the basic filtering, This file contains the samples that passed the basic filtering, This file contains the phenotype of the samples, This file contains the covariates of the samples, This file contains the PCs of the samples. Read in the phenotype and covariate files, 3. The script used here is based on lassosum version 0.4.4, For more details, please refer to lassosum's homepage. The script used here is based on LDpred 2 implemented under bigsnpr version 1.4.7, For more details, please refer to LDpred 2's homepage. # Remove P-value = 0, which causes problem in the transformation, # Transform the P-values into correlation, # The cluster parameter is used for multi-threading, # You can ignore that if you do not wish to perform multi-threaded processing, Basic Tutorial for Polygenic Risk Score Analyses, The genotype file after performing some basic filtering, This file contains the SNPs that passed the basic filtering, This file contains the samples that passed the basic filtering, This file contains the phenotype of the samples, This file contains the covariates of the samples, This file contains the PCs of the samples.