| Title: | Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists |
| Journal: | Frontiers in Genetics |
| Published: | 18 Feb 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/35251129/ |
| DOI: | https://doi.org/10.3389/fgene.2022.818574 |
| URL: | https://www.frontiersin.org/articles/10.3389/fgene.2022.818574/pdf |
| Citations: | 61 (52 in last 2 years) as of 8 Aug 2024 |
Publication 7188
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Abstract
A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.3 Authors
- Jennifer A. Collister
- Xiaonan Liu
- Lei Clifton
1 Application
| Application ID | Title |
|---|---|
| 33952 | Risk profiles for cancers; polygenic risk scores with and without non-genetic risk factors |
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