Polygenic risk scores for complex diseases: where are we now?

Polygenic risk scores (PRS), commonly referred to as genetic or genomic risk scores, aggregate the effects of multiple genetic variants into a single composite estimate of genetic risk. PRS scores are typically used to predict the risk of developing a disease or to explain the phenotypic variation,...

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Bibliographic Details
Main Authors: Loh, Marie, Chambers, John Campbell
Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
Format: Article
Language:English
Published: 2023
Subjects:
Online Access:https://hdl.handle.net/10356/169414
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Institution: Nanyang Technological University
Language: English
Description
Summary:Polygenic risk scores (PRS), commonly referred to as genetic or genomic risk scores, aggregate the effects of multiple genetic variants into a single composite estimate of genetic risk. PRS scores are typically used to predict the risk of developing a disease or to explain the phenotypic variation, and are derived from the effect sizes observed in large-scale genome-wide association studies (GWAS). Unlike rare monogenic diseases such as cystic fibrosis, which are attributable to genetic variants in single genes, with large effects on disease status, common complex diseases such as type 2 diabetes mellitus (T2DM) are polygenic, with risk contributed by a panel of genetic variants present throughout the genome. The concept of integrating information from these multiple genetic variants into a single metric of genetic risk was initially proposed in the shape of genetic risk scores, which generally limited the score to include single-nucleotide polymorphisms (SNPs) that were common and reached genome-wide significance in the initial GWASs. In contrast, PRS incorporates information from a much larger set of genetic variants, typically hundreds of thousands, including SNPs below the threshold for genome-wide statistical significance, and often with much more modest effect sizes. Indeed, recent findings have pointed to how polygenic background could also increase the accuracy of risk estimation for individuals with monogenic risk variant in conditions such as familial hypercholesterolaemia, hereditary breast and ovarian cancer, and Lynch syndrome.