Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Nov 21;28(R2):R143-R150.
doi: 10.1093/hmg/ddz205.

Validity of polygenic risk scores: are we measuring what we think we are?

A Cecile J W Janssens  1
Affiliations
Review

Validity of polygenic risk scores: are we measuring what we think we are?

A Cecile J W Janssens. Hum Mol Genet. .

Abstract

Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Three types of validity applied to the measurement of polygenic risk scores. Legend: * In the context of the specific application of the measurement.
Figure 2
Figure 2
Independent effects between single-nucleotide polymorphisms, polygenic risk scores and clinical risk factors. Legend: PRS, polygenic risk score; SNP, single-nucleotide polymorphism; CAD, coronary artery disease. For illustration purposes, other possible associations between variables are omitted.
Figure 3
Figure 3
Per allele effect sizes for single-nucleotide polymorphisms in type 2 diabetes. Legend: Picture provided by 23andMe, reproduced from (55). The dots represent the genome-wide significant polymorphisms in the study of Scott et al. (56).
See this image and copyright information in PMC

References

    1. De La Vega F.M. and Bustamante C.D. (2018) Polygenic risk scores: a biased prediction? Genome Med., 10, 3. - PMC - PubMed
    1. Torkamani A., Wineinger N.E. and Topol E.J. (2018) The personal and clinical utility of polygenic risk scores. Nat Rev Genet., 19, 581–590. - PubMed
    1. Nelson R.M., Pettersson M.E. and Carlborg O. (2013) A century after Fisher: time for a new paradigm in quantitative genetics. Trend Genet., 29, 669–676. - PubMed
    1. Lyssenko V., Almgren P., Anevski D., Orho-Melander M., Sjogren M., Saloranta C., Tuomi T., Groop L. and Botnia Study G. (2005) Genetic prediction of future type 2 diabetes. PLoS Med., 2, e345. - PMC - PubMed
    1. Seddon J.M., George S., Rosner B. and Klein M.L. (2006) CFH gene variant, Y402H, and smoking, body mass index, environmental associations with advanced age-related macular degeneration. Hum Hered., 61, 157–165. - PubMed