Polygenic Risk Scores in Alzheimer's Disease: Current Applications and Future Directions

Abstract

Genome-wide association studies have identified nearly 40 genome-wide significant single nucleotide polymorphisms (SNPs) which are associated with Alzheimer's Disease (AD). Due to the polygenicity of AD, polygenic risk scores (PRS) have shown high potential for AD risk prediction. PRSs have been shown to successfully discriminate between AD cases and controls achieving a prediction accuracy of up to 84% based on area under the receiver operating curve. The prediction accuracy in AD is higher compared with other complex genetic disorders. PRS can be restricted to SNPs which reside in biologically relevant gene-sets; the predictive value of these gene-sets in the general population is not as high as genome-wide PRS, but they may play an important role to identify mechanisms of disease development and inform biological experiments. Multiple methods are available to derive PRSs, such as selecting SNPs based on statistical evidence of association with the disease or using prior evidence for SNP selection. All methods have advantages, but PRS produced using different methodologies are often not comparable, and results should be interpreted with care. Similarly, this is true when PRS is based on different background populations. With the exponential growth in development of digital electronic devices it is easy to calculate an individual's disease risk using public databases. A major limitation for the utility of PRSs is that the risk score is sample and method dependent. Therefore, replicability and interpretability of PRS is an important issue. PRS can be used to determine the probability of developing disease which incorporates information about disease risk in the general population or in a specific AD risk group. It is essential to consult with genetic counselors to ensure genetic risk is communicated appropriately.

Keywords: LDpred; PRS; PRS-CS; PRSice; alzheimer; polygenic; risk.

Figure 1

Comparison of PRS using Different Methodologies; PRS(P+T), PRSice, PRS-CS and LDpred. The PRS is computed for all individuals in HipSci (19) open access data merged with the 1,000 Genomes data (18), scores are weighted using AD GWAS (5) summary statistics. Only SNPs with p ≤ 0.5 are included and the APOE region (chr19: 44.4-46.5Mb) is excluded for all methods.

Figure 2

(A) Histogram of PRS Scores in HipSci (19) open access data + 1,000 Genomes data (18) for both PRS(P+T) and PRSice methods. Only SNPs with p ≤ 0.5 are included in the score, and a clumping threshold of 0.1 was used. (B) Histogram displaying PRS extremes using PRS(P+T) estimating LD in HipSci (19) data. Additional vertical lines display the PRS of the same two individuals when LD is estimated from a different sample, or PRS is computed using a different method.