PRScope

Contents

• Outline
• Repo Contents
• System Requirements
• Setup
• Installation Instructions
• Running PRScope
• PRScope tested with
• Pipeline Description
• Demo

Outline

PRScope automatically generates all the polygenic scores (PGS) associated with selected ontology IDs (e.g., Experimental Factor Ontology, EFO) for a given genotype dataset.

Inputs

• Ontology IDs defining the PGS of interest
• Genotype data to calculate the PGS

Output

• A dataset (multi-PGS matrix) containing:
• Subject IDs from the genotype data
• Values of all selected PGS

The setup is optimized for a minimal-effort "vanilla use case" but supports advanced configurations.

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Repo Contents

• config: config files for parameter specification.
• input: genotype-, reference files and PRS trait specification.
• main: contains pipeline and scripts for PRS calculation, biotype identification and tools.
• output: intermediate and final results are saved here.

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System Requirements

Hardware Requirements

PRScope requires only a standard computer with enough RAM (4GB) to support the in-memory operations, but for best performance, we suggest a computer with higher specifications:

RAM: 16+ GB
CPU: 4+ cores, 3.3+ GHz/core

The runtimes below are generated using a computer with the recommended specs (16 GB RAM, 4 cores each 3.3 GHz) and internet of speed 100 Mbps.

Software Requirements

PRScope requires the following:

• conda
• Python
• R
• Snakemake
• PLINK^[1]
• PRSice^[2] or LDpred-2^[3]
• liftOverPlink^[4]

Only conda must be installed manually. All other dependencies are managed via the Conda environment.

PRScope has been tested on the Ubuntu 22.04.5 and requires a Linux system.

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Setup

1. Setting Up

a. Cloning the Repository

git clone https://github.com/transbioZI/PRScope

b. Download the required files from the provided link

Reference files for PRScope

tar -xf reference.tar.gz

Place them into this folder input/reference/:

• The folder should now include:

  • ldpred2_ref/
  • eur_hg38.phase3.bed
  • eur_hg38.phase3.bim
  • eur_hg38.phase3.fam
  • eur_hg38.phase3.frq

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Installation Instructions

1. Install Conda

Conda Installation Guide

2. Create the Conda Environment

conda create -c conda-forge -c bioconda -n snakemake snakemake python=3.12.1

• Environment name: snakemake
• Wait for installation to complete (~15 minutes)

3. Activate the Environment

conda activate snakemake

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Running PRScope (please see Demo below)

cd PRScope
./run.sh

This command initiates the PRScope pipeline.

• Wait for installation to complete conda environment
• May take up to an hour

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PRScope tested with

conda version : 23.1.0
snakemake version : 8.4.8
python : 3.12.1

R-sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0

locale:
LC_CTYPE=C.UTF-8, LC_NUMERIC=C, LC_TIME=C.UTF-8, LC_COLLATE=C.UTF-8, LC_MONETARY=C.UTF-8, LC_MESSAGES=C.UTF-8, LC_PAPER=C.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=C.UTF-8, LC_IDENTIFICATION=C

time zone: Europe/Berlin
tzcode source: system (glibc)

attached base packages:
parallel, stats, graphics, grDevices, utils, datasets, methods, base

other attached packages:
reshape2_1.4.4, cluster_2.1.7, xgboost_1.7.8.1, bigutilsr_0.3.4, reshape_0.8.9, ggpubr_0.6.0,doParallel_1.0.17, iterators_1.0.14, foreach_1.5.2, glmnet_4.1-8, Matrix_1.7-1, lubridate_1.9.4, forcats_1.0.0, purrr_1.0.2, readr_2.1.5, tidyr_1.3.1, tibble_3.2.1, tidyverse_2.0.0, data.table_1.16.4, dplyr_1.1.4, gwasrapidd_0.99.17, caret_7.0-1, lattice_0.22-6, ranger_0.17.0, stringr_1.5.1, ggplot2_3.5.1, fmsb_0.7.6, optparse_1.7.5, tidyselect_1.2.1, timeDate_4041.110, bigassertr_0.1.6, pROC_1.18.5, digest_0.6.37, rpart_4.1.23,timechange_0.3.0, lifecycle_1.0.4, survival_3.7-0, magrittr_2.0.3, compiler_4.4.1, rlang_1.1.4, tools_4.4.1, utf8_1.2.4, ggsignif_0.6.4, plyr_1.8.9, abind_1.4-8, withr_3.0.2, nnet_7.3-19, grid_4.4.1, stats4_4.4.1, fansi_1.0.6, colorspace_2.1-1, future_1.34.0, globals_0.16.3, scales_1.3.0, MASS_7.3-61, cli_3.6.3, generics_0.1.3, RSpectra_0.16-2, rstudioapi_0.17.1, future.apply_1.11.3, tzdb_0.4.0, getopt_1.20.4, splines_4.4.1, vctrs_0.6.5, hardhat_1.4.0, jsonlite_1.8.9, carData_3.0-5, car_3.1-3, hms_1.1.3, rstatix_0.7.2, Formula_1.2-5, listenv_0.9.1, gower_1.0.1, recipes_1.1.0, glue_1.8.0,parallelly_1.40.1, codetools_0.2-20, stringi_1.8.4, gtable_0.3.6, shape_1.4.6.1, munsell_0.5.1, pillar_1.9.0, ipred_0.9-15, lava_1.8.0, R6_2.5.1, backports_1.5.0, broom_1.0.7, class_7.3-22, Rcpp_1.0.13-1, nlme_3.1-166, prodlim_2024.06.25, ModelMetrics_1.2.2.2, pkgconfig_2.0.3

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Pipeline Description

The following pipelines can be found in the main/snakefiles/ directory:

• find_sumstats.snakefile – Selection of summary statistics for specified EFO IDs
• qc_sumstats.snakefile – Quality control of the selected summary statistics
• qc_genotype_with_liftover.snakefile – Quality control of genotype data with liftover
• qc_genotype.snakefile – Quality control of genotype data
• prs_calculation_prsice.snakefile – For PRS calculation using PRSice
• prs_calculation_ldpred.snakefile – For PRS calculation using LDpred
• ldsc_heritability_calculation.snakefile – Heritability calculation

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Demo

a. Navigate to the repository path

cd PRScope

b. Folder Structure

• config/ – For advanced parameter customization
• input/ – The only folder requiring user modifications
• main/ – Contains the main pipeline
• output/ – Will contain output after pipeline execution

c. Preparing the Input

1. Navigate to input/

2. Edit efo_ids.txt:

   • Default content: EFO_0003898
   • Replace with your own EFO IDs as needed

3. In input/genotype/, you’ll find:

   • EUR.bed
   • EUR.bim
   • EUR.fam

   (Replace with your genotype data if desired)

4. Running PRScope

cd PRScope
./run.sh

5. Expected Outcome

• output/gwas_list/gwas_search.txt - GWAS list meeting the criteria for use in PGS calculation.
• output/qced_gwas/GCST* - GWAS in gwas_search.txt, downloaded and preprocessed, ready for PGS calculation.
• output/qced_genotype/corrected[hg19,hg38] - EUR.FINAL.* - preprocessed version of the simulated genotype data in input/genotype/.
• output/calculated_pgs_prsice/ all calculated PGS of GWAS in the list gwas_search.txt as data table pgs_datatable_prsice_100.tsv. The suffix _100 means, the min number of SNPs used to calculate a PGS.

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References

[1] HannahVMeyer. Meyer-Lab-cshl/plinkQC: plinkQC 0.3.2. (Zenodo, 2020). 976 doi:10.5281/ZENODO.3934294.

[2] Choi, S. W. & O’Reilly, P. F. PRSice-2: Polygenic Risk Score software for biobank-scale 906 data. GigaScience 8, (2019).

[3] Privé, F., Arbel, J. & Vilhjálmsson, B. J. LDpred2: better, faster, stronger. Bioinformatics 913 36, 5424–5431 (2021).

[4] https://github.com/sritchie73/liftOverPlink

Citation

For usage of the PRScope and associated manuscript, please cite according to the enclosed citation.bib.