The Polygenic Risk Score Knowledge Base offers a centralized online repository for calculating and contextualizing polygenic risk scores

Abstract

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.

Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations

“…While several tutorials and methods have been published to inform PRS development and optimization, they often require strict dataset requirements which are rarely available for most populations (Choi et al, 2020;Hao et al, 2022;Page et al, 2022;Patel and Khera, 2022). In this pragmatic multistep approach, we will present a framework that is flexible in accommodating variable data availability across populations.…”

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

“…The development and construction of PGS/PRS have been the focus of many methodological studies, and these studies have provided effective tools for constructing reliable PGS/PRS [ 15 , 17 – 19 ]. These tools allow PGS/PRS to be derived from very large data sets or meta-analyses [ 20 – 22 ], and open-source websites have been developed that provide the information needed to compute PRS for over 3200 traits and diseases [ 13 , 23 ]. The ubiquity of PGS/PRS methods and the availability of large data sets have motivated studies of the polygenic basis of many non-disease traits, such as height [ 24 ], as well as health-positive traits such as health span [ 25 ], beneficial disease treatment response [ 26 – 28 ], and resilience to disease and longevity [ 29 – 34 ].…”

Evaluation of optimal methods and ancestries for calculating polygenic risk scores in East Asian population

“…Choosing an appropriate GWAS is one of the most important considerations to optimize PRS performance 10 . When selecting a GWAS, the ancestry of the study population is a key factor, since the transferability of PRSs across populations is poor owing to differences in allele frequencies and LD patterns of genetic variants 11,12 .…”