DESCRIPTION: This tutorial provides a step-by-step guide to performing basic polygenic risk score (PRS) analyses and accompanies our PRS Guide paper. The aim of this tutorial is to provide a simple introduction of 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.
CITATION: Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020). Tutorial: a guide to performing polygenic risk score analyses. Nature protocols, 15(9), 2759-2772.
PLINK2
SHORT NAME: PLINK2
FULL NAME: PLINK2
DESCRIPTION: The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility.
URL : https://www.cog-genomics.org/plink/2.0/
CITATION: Chang, C.C., Chow, C.C., Tellier, L.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaSci 4, 7 (2015). https://doi.org/10.1186/s13742-015-0047-8
USE: calculate PRS using genotype data.
PRSice-2
SHORT NAME: PRSice-2
FULL NAME: PRSice-2
DESCRIPTION: PRSice (pronounced 'precise') is a Polygenic Risk Score software for calculating, applying, evaluating and plotting the results of polygenic risk scores (PRS) analyses.
URL : https://www.prsice.info/
CITATION: Shing Wan Choi, Paul F O'Reilly, PRSice-2: Polygenic Risk Score software for biobank-scale data, GigaScience, Volume 8, Issue 7, July 2019, giz082, https://doi.org/10.1093/gigascience/giz082
PRSet
SHORT NAME: PRSet
FULL NAME: PRSet
DESCRIPTION: A new feature of PRSice is the ability to perform set base/pathway based analysis. This new feature is called PRSet.
URL : https://www.prsice.info/quick_start_prset/
CITATION: Choi SW, Garcia-Gonzalez J, Ruan Y, et al. The power of pathway-based polygenic risk scores. Research Square; 2021. DOI: 10.21203/rs.3.rs-643696/v1.
KEY WORDS: pathway-based
LDpred
SHORT NAME: LDpred
FULL NAME: LDpred
DESCRIPTION: LDpred is a Python based software package that adjusts GWAS summary statistics for the effects of linkage disequilibrium (LD).
CITATION: Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., ... & Marsal, S. (2015). Modeling linkage disequilibrium increases accuracy of polygenic risk scores. The american journal of human genetics, 97(4), 576-592.
DESCRIPTION: LDpred2 is a widely used Bayesian method for building polygenic scores (PGS). LDpred2-auto can infer the two parameters from the LDpred model, h^2 and p, so that it does not require an additional validation dataset to choose best-performing parameters. Here, we present a new version of LDpred2-auto, which adds a third parameter alpha to its model for modeling negative selection. Additional changes are also made to provide better sampling of these parameters.
CITATION: Márquez-Luna, C., Gazal, S., Loh, P. R., Kim, S. S., Furlotte, N., Auton, A., & Price, A. L. (2021). Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Nature Communications, 12(1), 1-11.
KEY WORDS: Bayesian, functional priors
PRS-CS
SHORT NAME: PRS-CS
FULL NAME: PRS-CS
YEAR: 2019
DESCRIPTION: PRS-CS is a Python based command line tool that infers posterior SNP effect sizes under continuous shrinkage (CS) priors using GWAS summary statistics and an external LD reference panel.
URL : https://github.com/getian107/PRScs
CITATION: Ge, T., Chen, CY., Ni, Y. et al. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun 10, 1776 (2019). https://doi.org/10.1038/s41467-019-09718-5
KEY WORDS: continuous shrinkage (CS) prior
PRS-CSx
SHORT NAME: PRS-CSx
FULL NAME: PRS-CSx
YEAR: 2022
DESCRIPTION: PRS-CSx is a Python based command line tool that integrates GWAS summary statistics and external LD reference panels from multiple populations to improve cross-population polygenic prediction. Posterior SNP effect sizes are inferred under coupled continuous shrinkage (CS) priors across populations.
URL : https://github.com/getian107/PRScsx
CITATION: Ruan, Y., Lin, YF., Feng, YC.A. et al. Improving polygenic prediction in ancestrally diverse populations. Nat Genet 54, 573–580 (2022). https://doi.org/10.1038/s41588-022-01054-7
DESCRIPTION: lassosum is a method for computing LASSO/Elastic Net estimates of a linear regression problem given summary statistics from GWAS and Genome-wide meta-analyses, accounting for Linkage Disequilibrium (LD), via a reference panel.
URL : https://github.com/tshmak/lassosum
CITATION: Mak, T. S. H., Porsch, R. M., Choi, S. W., Zhou, X., & Sham, P. C. (2017). Polygenic scores via penalized regression on summary statistics. Genetic epidemiology, 41(6), 469-480.
KEY WORDS: penalized regression
BayesR
SHORT NAME: BayesR
FULL NAME: BayesR
YEAR: 2015
DESCRIPTION: Bayesian mixture model to dissect genetic variation for disease in human populations and to construct more powerful risk predictors
CITATION:Moser, G., Lee, S. H., Hayes, B. J., Goddard, M. E., Wray, N. R., & Visscher, P. M. (2015). Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS genetics, 11(4), e1004969.
CITATION:Zeng, J., De Vlaming, R., Wu, Y., Robinson, M. R., Lloyd-Jones, L. R., Yengo, L., ... & Yang, J. (2018). Signatures of negative selection in the genetic architecture of human complex traits. Nature genetics, 50(5), 746-753.
SBayesR
SHORT NAME: SBayesR
FULL NAME: SBayesR
YEAR: 2019
DESCRIPTION: extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies, SBayesR.
CITATION:Lloyd-Jones, L. R., Zeng, J., Sidorenko, J., Yengo, L., Moser, G., Kemper, K. E., ... & Visscher, P. M. (2019). Improved polygenic prediction by Bayesian multiple regression on summary statistics. Nature communications, 10(1), 1-11.
SBayesS
SHORT NAME: SBayesS
FULL NAME: SBayesS
YEAR: 2021
DESCRIPTION: estimate multiple genetic architecture parameters including selection signature using only GWAS summary statistics
CITATION:Zeng, J., Xue, A., Jiang, L., Lloyd-Jones, L. R., Wu, Y., Wang, H., ... & Yang, J. (2021). Widespread signatures of natural selection across human complex traits and functional genomic categories. Nature communications, 12(1), 1-12.
SBayesRC
SHORT NAME: SBayesRC
FULL NAME: SBayesRC
YEAR: 2022
DESCRIPTION: SBayesRC integrates GWAS summary statistics with functional genomic annotations to improve polygenic prediction of complex traits.
CITATION: Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Zhili Zheng, Shouye Liu, Julia Sidorenko, Loic Yengo, Patrick Turley, Alireza Ani, Rujia Wang, Ilja M Nolte, Harold Snieder, Lifelines Cohort Study, Jian Yang, Naomi R. Wray, Michael E Goddard, Peter M Visscher, Jian Zeng bioRxiv 2022.10.12.510418; doi: https://doi.org/10.1101/2022.10.12.510418
TL-PRS
SHORT NAME: TL-PRS
FULL NAME: transfer learning PRS
YEAR: 2022
URL: https://github.com/ZhangchenZhao/TLPRS
DESCRIPTION: This R package helps users to construct multi-ethnic polygenic risk score (PRS) using transfer learning. It can help predict PRS of minor ancestry using summary statistics from exsiting resources, such as UK Biobank.
CITATION: Zhao, Z., Fritsche, L. G., Smith, J. A., Mukherjee, B., & Lee, S. (2022). The Construction of Multi-ethnic Polygenic Risk Score using Transfer Learning. medRxiv.
Multi-PGS
SHORT NAME: Multi-PGS
YEAR: 2022
FULL NAME: Multi-PGS
DESCRIPTION: a framework to generate enriched PGS from a wealth of publicly available genome-wide association studies, combining thousands of studies focused on many different phenotypes, into a multi-PGS
CITATION: Albiñana, C., Zhu, Z., Schork, A. J., Ingason, A., Aschard, H., Brikell, I., ... & Vilhjálmsson, B. J. (2023). Multi-PGS enhances polygenic prediction by combining 937 polygenic scores. Nature Communications, 14(1), 4702.
wMT-SBLUP
SHORT NAME: wMT-SBLUP
YEAR: 2018
FULL NAME: weighted approximate multi-trait summary statistic BLUP
URL : https://github.com/uqrmaie1/smtpred
CITATION: Maier, R.M., Zhu, Z., Lee, S.H. et al. Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat Commun 9, 989 (2018). https://doi.org/10.1038/s41467-017-02769-6
PGSCatalog
SHORT NAME: PGS Catalog
FULL NAME: PGS Catalog
DESCRIPTION: The PGS Catalog is an open database of published polygenic scores (PGS). Each PGS in the Catalog is consistently annotated with relevant metadata; including scoring files (variants, effect alleles/weights), annotations of how the PGS was developed and applied, and evaluations of their predictive performance.
URL : https://www.pgscatalog.org/
CITATION: Lambert, S. A., Gil, L., Jupp, S., Ritchie, S. C., Xu, Y., Buniello, A., ... & Inouye, M. (2021). The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics, 53(4), 420-425.
KEYWORDS: PGS database
pgsc_calc
SHORT NAME: pgsc_calc
FULL NAME: The Polygenic Score Catalog Calculator
YEAR: 2022
DESCRIPTION: pgsc_calc is a bioinformatics best-practice analysis pipeline for calculating polygenic [risk] scores on samples with imputed genotypes using existing scoring files from the Polygenic Score (PGS) Catalog and/or user-defined PGS/PRS.
URL : https://github.com/PGScatalog/pgsc_calc
CITATION: https://github.com/PGScatalog/pgsc_calc
KEYWORDS: PRS calculation pipeline
VIPRS
SHORT NAME: VIPRS
FULL NAME: Variational inference of polygenic risk scores
YEAR: 2022
DESCRIPTION: viprs is a python package that implements scripts and utilities for running variational inference algorithms on genome-wide association study (GWAS) data for the purposes polygenic risk estimation.
URL : https://github.com/shz9/viprs
CITATION: Zabad, S., Gravel, S., & Li, Y. (2022). Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference. bioRxiv.
KEYWORDS: Variational Inference (VI)
ExPRSweb
SHORT NAME: ExPRSweb
FULL NAME: exposure polygenic risk scores (ExPRSs)
YEAR: 2022
DESCRIPTION: Integrating published and freely available genome-wide association studies (GWAS) summary statistics from multiple sources (published GWAS, the NHGRI-EBI GWAS Catalog, FinnGen- or UKB-based GWAS), we created an online repository for exposure polygenic risk scores (ExPRS) for health-related exposure traits. Our framework condenses these summary statistics into ExPRS using linkage disequilibrium pruning and p-value thresholding (P&T) or penalized, genome-wide effect size weighting. We evaluate them in the cohort of the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and in the population-based UK Biobank Study (UKB). For each ExPRS construct, measures on performance, accuracy, and discrimination are provided. Beyond the ExPRS evaluation in MGI and UKB, the ExPRSweb platform features construct downloads, evaluation in the top percentiles, and phenome-wide ExPRS association studies (ExPRS-PheWAS) for a subset of ExPRS that are predictive for the corresponding exposure.
URL : https://exprsweb.sph.umich.edu:8443/
CITATION: Ma, Y., Patil, S., Zhou, X., Mukherjee, B., & Fritsche, L. G. (2022). ExPRSweb-An Online Repository with Polygenic Risk Scores for Common Health-related Exposures. medRxiv.
KEY WORDS: exposure PRS
Cancer PRSweb
SHORT NAME: Cancer PRSweb
FULL NAME: Cancer PRSweb
YEAR: 2020
DESCRIPTION: Our framework condenses these summary statistics into PRS using linkage disequilibrium pruning and p-value thresholding (fixed or data-adaptively optimized thresholds) or penalized, genome-wide effect size weighting. We evaluate them in the cancer-enriched cohort of the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and in the population-based UK Biobank Study (UKB). For each PRS construct, measures on performance, calibration, and discrimination are provided. Beyond the cancer PRS evaluation in MGI and UKB, the PRSweb platform features construct downloads, risk evaluation in the top percentiles, and phenome-wide PRS association studies (PRS-PheWAS) for a subset of PRS that are predictive for the primary cancer.
URL : https://prsweb.sph.umich.edu:8443/
CITATION: Fritsche, L. G., Patil, S., Beesley, L. J., VandeHaar, P., Salvatore, M., Ma, Y., ... & Mukherjee, B. (2020). Cancer PRSweb: an online repository with polygenic risk scores for major cancer traits and their evaluation in two independent biobanks. The American Journal of Human Genetics, 107(5), 815-836.
KEY WORDS: Cancer PRS
PRS atlas
SHORT NAME: PRS atlas
FULL NAME: PRS atlas
DESCRIPTION: This web application can be used to query findings from an analysis of 162 polygenic risk scores and 551 complex traits using data from the UK Biobank study1. Traits were selected based on the heritability analysis conducted by the Neale Lab2 (P<0.05). We encourage users of this resource to conduct follow-up analyses of associations to robustly identify causal relationships between complex traits.
URL : http://mrcieu.mrsoftware.org/PRS_atlas/
CITATION: Richardson, T. G., Harrison, S., Hemani, G., & Smith, G. D. (2019). An atlas of polygenic risk score associations to highlight putative causal relationships across the human phenome. Elife, 8, e43657.
metabolites PRS atlas
SHORT NAME: metabolites PRS atlas
FULL NAME: metabolites PRS atlas
DESCRIPTION: This web application can be used to query findings from a systematic analysis of 129 polygenic risk scores and 249 circulating metabolits using high-throughput nuclear magnetic resonance data from the UK Biobank study1,2. We encourage users of this resource to conduct follow-up analyses of associations to investigate potential causal and non-causal metabolic biomarkers. Age-stratified results can be used to investigate how potential sources of collider bias (e.g. statin therapy) may influence findings in the full sample
CITATION:Fang, S., Holmes, M. V., Gaunt, T. R., Smith, G. D., & Richardson, T. G. (2022). Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. eLife.
PUMA-CUBS
SHORT NAME:PUMA-CUBS
FULL NAME:PUMA-CUBS
DESCRIPTION:an ensemble learning strategy named PUMACUBS to combine multiple PRS models into an ensemble score without requiring external data
for model fitting.
URL :https://github.com/qlu-lab/PUMAS
CITATION:Zhao, Zijie, et al. "Optimizing and benchmarking polygenic risk scores with GWAS summary statistics." bioRxiv (2022).
MiXeR
SHORT NAME: MiXeR
FULL NAME: MiXeR
DESCRIPTION: Causal Mixture Model for GWAS summary statistics
URL :https://github.com/precimed/mixer
CITATION: （univariate） Holland, Dominic, et al. "Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model." PLoS Genetics 16.5 (2020): e1008612.
CITATION：（cross-trait analysis）Frei, Oleksandr, et al. "Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation." Nature communications 10.1 (2019): 1-11.
SDPRX
SHORT NAME: SDPRX
FULL NAME: SDPRX
DESCRIPTION: SDPRX is a statistical method for cross-population prediction of complex traits. It integrates GWAS summary statistics and LD matrices from two populations (EUR and non-EUR) to compuate polygenic risk scores.
URL : https://github.com/eldronzhou/SDPRX
CITATION:Zhou, Geyu, Tianqi Chen, and Hongyu Zhao. "SDPRX: A statistical method for cross-population prediction of complex traits." The American Journal of Human Genetics (2022).
PRStuning
SHORT NAME: PRStuning
FULL NAME: PRStuning
DESCRIPTION: Estimate Testing AUC for Binary Phenotype Using GWAS Summary Statistics from the Training Data
CITATION: Jiang, W., Chen, L., Girgenti, M. J., & Zhao, H. (2023). Tuning Parameters for Polygenic Risk Score Methods Using GWAS Summary Statistics from Training Data. Research Square.
BridgePRS
SHORT NAME: BridgePRS
FULL NAME: BridgePRS
DESCRIPTION: BridgePRS is a Bayesian-ridge (Bridge) approach, which "bridges" the PRS between two populations of different ancestry, developed to tackle the "PRS Portability Problem". The PRS Portability Problem causes lower accuracy PRS in underrepresented populations due to the biased sampling in GWAS data collection.
YEAR: 2023
URL: https://www.bridgeprs.net/
CITATION: Hoggart, C. J., Choi, S. W., García-González, J., Souaiaia, T., Preuss, M., & O’Reilly, P. F. (2023). BridgePRS leverages shared genetic effects across ancestries to increase polygenic risk score portability. Nature Genetics, 1-7.
Reviews&Tutorial
CITATION: Peter M. Visscher,Loic Yengo,Nancy J. Cox,Naomi R. Wray,Discovery and implications of polygenicity of common diseases, Science, 373, 6562, (1468-1473), (2021). /doi/10.1126/science.abi8206
CITATION: Wang, Y., Tsuo, K., Kanai, M., Neale, B. M., & Martin, A. R. (2022). Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annual Review of Biomedical Data Science, 5.
CITATION: Kachuri, L., Chatterjee, N., Hirbo, J. et al. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet (2023).
Benchmarks
CITATION: Wang, C., Zhang, J., Zhou, X., & Zhang, L. (2022). A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants. bioRxiv.