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Review
. 2022 Feb 14;1(2):129-149.
doi: 10.1515/mr-2021-0025. eCollection 2021 Dec.

Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention

Yuzhuo Wang  1   2 , Meng Zhu  1   3 , Hongxia Ma  1   3   4 , Hongbing Shen  1   3   4
Affiliations
Review

Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention

Yuzhuo Wang et al. Med Rev (2021). .

Abstract

Genome-wide association studies (GWASs) have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers. The genetic variants associated with a cancer can be combined into a polygenic risk score (PRS), which captures part of an individual's genetic susceptibility to cancer. Recently, PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer, which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk. In this context, we provide an overview of the major discoveries from cancer GWASs. We then review the methodologies used for PRS construction, and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors. Potential utility of PRSs in cancer risk prediction, screening, and precision prevention are illustrated. Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.

Keywords: cancer screening; genome-wide association study (GWAS); polygenic risk score (PRS); precision prevention; risk prediction model.

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Conflict of interest statement

Competing interests: All the authors declare that there is no conflict of interest.

Figures

Figure 1:
Figure 1:
Development and validation of polygenic risk prediction models. The recommended steps for PRS construction, risk model development and validation are displayed. During PRS construction, genetic variants associated with an outcome of interest in a GWAS dataset are combined as a weighted sum of risk allele counts. Commonly used methods for “SNP selection” and “SNP-weight calculation” during the PRS construction procedure are shown. Performance of PRSs are evaluated in the training sample to select the optimal PRS. This optimal PRS is then added to a risk prediction model and may be combined with demographics (e.g., age, sex, and ancestry) and conventional risk factors (e.g., lifestyle factors or environmental exposures, clinical risk factors, inherited mutations leading to a moderate-to-high risk of cancer, and family history) to predict the outcome of interest. After model building procedure to select the best risk prediction model, this model is validated in an independent sample. For the evaluation of risk prediction model, the distribution of the PRS, the proportion of variance explained (R2) and effect size estimates (e.g., ORs, HRs) of the PRS and/or risk models should be described. Performance of the risk prediction model in terms of discrimination, calibration, risk stratification, and NRI should also be assessed. Results from risk model evaluation should be reported for both the training and validation samples for comparison. PRS, polygenic risk score; GWAS, genome-wide association study; OR, odds ratio; HR, hazard ratio; R2, the proportion of variance explained; AUC, area under the receiver operating characteristic curve; NRI, net reclassification index.
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