Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists

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.

Polygenic Risk Score Comparative Analyses Reveals Risk Disparity of Genetic Predisposition to Chronic Kidney Disease- A Multi Ancestry Approach

“…Larger sample sets are required for PRS [27] to find out the severity of the disease in particular ethnic group for which GWAS are quite expensive an alternate to it can be small case control studies which are cost effective compared to GWAS can be conducted, it should be practiced to make individual data available online this will help in testing the models when sample sets are merged using small scale local datasets which will serve as a good hold for powerful statistical analysis [28]. The limitation with PRS is the poor performance of it in other than European population due to lack of data from other ancestries [29].…”

A polygenic score indexing a DRD2-related co-expression network is associated with striatal dopamine function

“…The effect of the PCI on whole striatal K i cer was tested using a linear model (lm) regression in R 79 with age, gender, PET scanner and the first 10 genetic principal components as covariates of no interest in view of their potential effect on dopamine synthesis capacity 80 , 81 . To facilitate the interpretation of the results, PCI values were standardised using the scale() function in R before being entered in the model 82 . Injected dose of radiotracer was not considered, as it is not associated with 18 F-DOPA K i cer estimates 74 .…”

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis

“…The formula for calculating PGS is as follows [ 39 ]: where Sj is the effect size (OR); Gij is the observed number of risk alleles; P is the sampling density (2 for humans); N is the total number of polymorphic variants; Mj is the number of missing genotypes in the analysed array.…”