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Review
. 2020 Nov;57(11):725-732.
doi: 10.1136/jmedgenet-2019-106763. Epub 2020 May 6.

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare

Chantal Babb de Villiers  1 , Mark Kroese  2 , Sowmiya Moorthie  2
Affiliations
Review

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare

Chantal Babb de Villiers et al. J Med Genet. 2020 Nov.

Abstract

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.

Keywords: clinical genetics; genetic epidemiology; genome-wide; getting research into practice; prevention.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Polygenic score calculation. This calculation aggregates the SNPs and their weights selected for a polygenic score. Common diseases are thought to be influenced by many genetic variants with small individual effect sizes, such that meaningful risk prediction necessitates examining the aggregated impact of these multiple variants including their weightings. PGS, polygenic score.
Figure 2
Figure 2
Construction of a polygenic score. In the process of developing a polygenic score, numerous models are tested and then compared. The model that performs best (as determined by one or more measures) is then selected for validation in the external data set. GWAS, genome-wide association studies.
Figure 3
Figure 3
Example distribution of polygenic scores across a population. Thresholds can be set to stratify risk as low (some), average (most) and high (some).
See this image and copyright information in PMC

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