Privé, F., Arbel, J., & Vilhjálmsson, B. J. length.out = 30). Note that we now recommend to run LDpred2 genome-wide, contrary to what was shown in the first versions of this tutorial.

Installing LDpred-2¶ Note. You can look at the path of the chains, as shown below. Here, these are simulated data so all variants use the same strand and the same reference. # 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. Please look at the code linked at the beginning. BioRxiv. 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. lassosum is one of the dedicated PRS programs which is an R package that uses penalised regression (LASSO) in its approach to PRS calculation. The tutorial is separated into four main sections and reflects the structure of our guide paper: the first two sections on QC corres… Basic Tutorial for Polygenic Risk Score Analyses. In the paper, we propose an automatic way to filter bad chains by comparing the scale of the resulting predictions (see this code, reproduced below). Here we show how to compute polygenic risk scores using LDpred2. You can install lassosum and its dependencies in R with the following command: Again, we assume that we have the following files (or you can download it from here): # Prefer to work with data.table as it speeds up file reading, # For multi-threading, you can use the parallel package and, # invoke cl which is then passed to lassosum.pipeline, # Need as.data.frame here as lassosum doesn't handle data.table, # We will need the EUR.hg19 file provided by lassosum. 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. This tutorial only uses fake data for educational purposes. In addition, Nagelkerke \(R^2\) is biased when there are ascertainment of samples. # which are LD regions defined in Berisa and Pickrell (2015) for the European population and the hg19 genome. The script used here is based on lassosum version 0.4.4, For more details, please refer to lassosum's homepage. 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. 60 We define polygenic risk scores, or polygenic scores, as a single value estimate of an 61 individual’s propensity to a phenotype, calculated as a sum of their genome-wide genotypes 62 weighted by corresponding genotype effect sizes – potentially scaled or shrunk – from 63 summary statistic GWAS data. Basic Tutorial for Polygenic Risk Score Analyses. This tutorial only uses fake data for educational purposes. thus one must rename the columns according to their actual ordering, Scripts for binary trait analysis only serve as a reference as we have not simulate any binary traits. In practice, until we find a better set of variants, we recommend using the HapMap3 variants used in PRS-CS and the LDpred2 papers. 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).

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.

Here, we have built the LD matrix using variants from one chromosome only. The other 159 individuals are used as test set to evaluate the final models. This tutorial provides a step-by-step guide to performing basic polygenic risk score (PRS) analyses and accompanies our PRS Guide paper. For more details, please refer to lassosum's homepage. If no or few variants are actually flipped, you might want to disable the strand flipping option. This is not the case here, which is probably because the data is so small. The script used here is based on LDpred 2 implemented under bigsnpr version 1.4.7. Here we consider that there are 400 individuals to be used as validation set to tune hyper-parameters for LDpred2-grid. The script used here is based on lassosum version 0.4.4.

# Read from bed/bim/fam, it generates .bk and .rds files. # 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. 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. LDpred2: better, faster, stronger. In practice, you need to build it for variants from all chromosomes.

"), # Extract SNPs that are included in the chromosome, # We assume the fam order is the same across different chromosomes, # Assuming the file naming is EUR_chr#.bed, # Reformat the phenotype file such that y is of the same order as the, # (will also need the fmsb package to calculate the pseudo R2), Basic Tutorial for Polygenic Risk Score Analyses, 1.