The necessity of incorporating non-genetic risk factors into polygenic risk score models

Abstract

The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests.

How group structure impacts the numbers at risk for coronary artery disease: polygenic risk scores and non-genetic risk factors in the UK Biobank cohort

“…Commercial genetic testing services have been sold more than 27 million times, but the ability of genetic factors to assess risk did not outperform common methods for CAD (van Dam et al 2023). They also pointed out that risk assessment for CAD based on simple questionnaires or variables from electronic health records is as good or better than risk prediction based on genetics alone.…”

The Early‐Onset Alzheimer's Disease Whole‐Genome Sequencing Project: Study design and methodology

“…To test these polygenic hypotheses in EOAD, we will perform in‐depth network and pathway‐based tests of association, and construct and assess the utility of genetic risk scores (calculated by summing an individual's genome‐wide genotypes weighted by their corresponding z‐scores) employing state‐of‐the‐art methods specifically developed for these analyses. Risk score sub–analyses will include non‐genetic factors as this can improve predictive power of polygenic scores significantly 74 . Third, we will comprehensively assess the role of ancestry in EOAD and its subtypes, capitalizing on the rich diversity of this dataset.…”

“…Risk score sub-analyses will include non-genetic factors as this can improve predictive power of polygenic scores significantly. 74 Third, we will comprehensively assess the role of ancestry in EOAD and its subtypes, capitalizing on the rich diversity of this dataset. All analyses will be conducted within and across ancestry groups, and we will utilize a wide array of tools to assess global ancestry, local ancestry, admixture, and the evolutionary history of identified risk and protective alleles.…”

Pharmacogenomic-guided dosing of fluoropyrimidines beyond DPYD: time for a polygenic algorithm?

“…Pre-existing diseases, such as cardiovascular diseases (CVD), hepatic impairment, and renal insufficiency, as well as CVD risk factors (hypertension, hyperlipidemia and smoking), have been studied as for their association with increased risk for 5-FU-induced cardiotoxicity ( Brutcher et al, 2018 ; Sara et al, 2018 ). The perception of incorporating non-genetic factors into polygenic risk score models is currently being evaluated for disease risk assessment ( van Dam et al, 2023 ). This approach holds promise to increase the discriminative power of the often low variance explained solely by the genetic factors for the predictive outcome(s).…”