A guide to performing Polygenic Risk Score analyses

Abstract

6The application of polygenic risk scores (PRS) has become routine across genetic 7 research. Among a range of applications, PRS are exploited to assess shared aetiology 8 between phenotypes, to evaluate the predictive power of genetic data for use in clinical 9 settings, and as part of experimental studies in which, for example, experiments are 10 performed on individuals, or their biological samples (eg. tissues, cells), at the tails of 11 the PRS distribution and contrasted. As GWAS sample sizes increase and PRS become 12 more powerful, they are set to play a key role in personalised medicine. However, 13 despite the growing application and importance of PRS, there are limited guidelines for 14 performing PRS analyses, which can lead to inconsistency between studies and 15 misinterpretation of results. Here we provide detailed guidelines for performing 16 polygenic risk score analyses relevant to different methods for their calculation, 17 outlining standard quality control steps and offering recommendations for best-18 practice. We also discuss different methods for the calculation of PRS, common 19 misconceptions regarding the interpretation of results and future challenges. 20 21 Genome-wide association studies (GWAS) have identified a large number of genetic variants, 22 typically single nucleotide polymorphisms (SNP), associated with a wide range of complex 23 traits [1-3]. However, the majority of these variants have a small effect and typically 24 correspond to a small fraction of truly associated variants, meaning that they have limited 25 predictive power [4-6]. Using a linear mixed model in the Genome-wide Complex Trait 26 Analysis software (GCTA) [7], Yang et al (2010) demonstrated that much of the heritability of 27 height can be explained by evaluating the effects of all SNPs simultaneously [6]. Subsequently, 28statistical techniques such as LD score regression (LDSC) [8,9] and the polygenic risk score 29 (PRS) method [4,10] have also aggregated the effects of variants across the genome to 30 estimate heritability, to infer genetic overlap between traits and to predict phenotypes based 31 on genetic profile or that of other phenotypes [4,5,[8][9][10]. 32 33 While GCTA, LDSC and PRS can all be exploited to infer heritability and shared aetiology 34 among complex traits, PRS is the only approach that provides an estimate of genetic 35 propensity to a trait at the individual-level. In the standard approach [4,[11][12][13], polygenic risk 36 scores are calculated by computing the sum of risk alleles corresponding to a phenotype of 37 interest in each individual, weighted by the effect size estimate of the most powerful GWAS 38 on the phenotype. Studies have shown that substantially greater predictive power can usually 39 be achieved by using PRS rather than a small number of genome-wide significant SNPs 40 [11,14,15]. As an individual-level genome-wide genetic proxy of a trait, PRS are suitable for a 41 range of applications. For example, as well as identifying shared aetiology among traits, PRS 42h...

Genetic Risk Scores

“…Calculating and testing PRSs is computationally demanding but in recent years has become routine (reviewed in Choi et al, 2018;Maier, Visscher, Robinson, & Wray, 2018). The major obstacle for PRSs is correlation among neighboring markers in high-density panels due to linkage disequilibrium (LD).…”

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Many PRS construction strategies and software packages exist, and we will not detail these various methods here. 124,[132][133][134][135][136][137][138][139][140][141][142][143] For a recent exploration of PRS construction, we refer the reader to Choi et al 144 Recently, statistical methods have been developed to leverage published GWAS and other omics summary statistics to improve the performance of prediction algorithms and perform analyses adjusting for many genetic loci simultaneously. [145][146][147][148][149] Researchers may also be interested in studying relationships between phenotypes or joint relationships between phenotypes and other patient-level factors such as treatments or genotypes.…”

Mitonuclear interactions influence Alzheimer’s disease risk

“…As such the findings of this study need to be replicated in a larger cohort. Second, when constructing PRS, sample overlap between the base dataset (i.e., IGAP), and the target datasets (i.e., ADNI) can result in inflation of the association between the PRS and trait tested in the target dataset (Choi et al, 2018).…”