An integrated polygenic and clinical risk tool enhances coronary artery disease prediction

Abstract

Background There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. Methods This research has been conducted using the UK Biobank (UKB) resource. We developed our own polygenic risk score (PRS) for coronary artery disease (CAD), using novel and established methods to combine published genomewide association study (GWAS) data with data from 114,196 UK Biobank individuals, also leveraging a large resource of other GWAS datasets along with functional information, to aid in the identification of causal variants, and thence define weights for > 8M genetic variants. We utilised a further 60,000 UKB individuals to develop an integrated risk tool (IRT) that combined our PRS with established risk tools (either the American Heart Association/American College of Cardiology's pooled cohort equations (PCE) or the UK's QRISK3) which was then tested in an additional, independent, set of 212,563 UKB individuals. We evaluated prediction performance in individuals of European ancestry, both as a whole and stratified by age and sex. Findings The novel CAD PRS showed superior predictive power for CAD events, compared to other published PRSs. As an individual risk factor, it has similar predictive power to each of systolic blood pressure, HDL cholesterol, and LDL cholesterol, but is more predictive than total cholesterol and smoking history. Our novel CAD PRS is largely uncorrelated with PCE, QRISK3, and family history, and, when combined with PCE into an integrated risk tool, had superior predictive accuracy. In individuals reclassified as high risk, CAD event rates were markedly and significantly higher compared to those reclassified as low risk. Overall, 9.7% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, in contrast to 3.7% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.7% (95% CI 4.4-7.0), but when individuals were stratified into four age-by-sex subgroups the improvement was larger for all subgroups (range 7.7%-17.3%), with best performance in younger middle-aged men aged 40-54yo (17.3%, 95% CI 13.0-21.5). Broadly similar results were found using a different risk tool (QRISK3), and also for cardiovascular disease events defined more broadly. Interpretation An integrated risk tool that includes polygenic risk outperforms current, clinical risk stratification tools, and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk. Funding Genomics plc

Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries

“…We next proceeded to investigate relative performance patterns in 4 sex-by-age subgroups (Figure 3, Supplementary Table 5). This analysis reiterates patterns previously reported for individuals of European ancestries in UKB and using a different IRT built from a coronary artery disease PRS 5 . The overall NRI performance in 3 of the 4 subgroups is significantly positive, with the strongest performance seen in younger middle-aged men (40-54yo) (NRI = 10.3% (5.7 -15.0)).…”

“…While these cohorts lack the necessary longitudinal and covariate information to calculate ASCVD-PCE at baseline, and therefore could not be used for IRT testing, these results permit the inference that the strong predictive performance seen at the PRS level should transfer to the IRT level. Second, in line with previous work 5,8 , we observe a low (and statistically non-significant) correlation between PRS values and ASCVD-PCE scores in the IRT testing cohorts (ARIC: r=0.026, 95% CI -0.018-0.070 (Fisher's z-method); MESA: r=0.002, 95% CI -0.043-0.047; UKB: r=0.002, 95% CI -0.005-0.008). This increases our confidence that the ASCVD PRS acts largely independently of ASCVD-PCE, and strengthens the inference that PRS results should therefore transfer to the IRT level.…”

“…But for clinical utility, the ASCVD-IRT tool requires gains in performance that are not only statistically significant but also clinically meaningful 30 . On this latter point, we note that large gains are seen especially in younger middle-aged men (40-54yo), not only in this study (overall NRI 10.3% (95% CI 5.7 -15.0), but also in a previous study on individuals of European ancestry in UK Biobank, where an NRI of 15.4% (95% CI 11.6 -19.3) was observed for coronary artery disease outcomes 5 . Furthermore, a large effect is also seen in younger middle-aged ARIC men of Black ethnicity in the current study (NRI = 8.5%, 95% CI 0.4 -16.7)), suggesting that this effect generalises to other ethnicities and ancestries.…”

“…Although genetics is a known risk factor 4 , it is not directly included in ASCVD-PCE (family history of ASCVD is assessed separately, outside of the tool). Polygenic risk scores (PRSs), which combine information across thousands of common genetic variants in the human genome, can be added to the ASCVD-PCE tool, but previous studies have reported variable predictive performance [5][6][7][8] . Additionally, these studies focused primarily on individuals with European ancestries, but it is known that the predictive accuracy of a PRS tends to attenuate in individuals with non-European ancestries [9][10][11] .…”

Integration of rare large-effect expression variants improves polygenic risk prediction

“…A major goal of complex disease genetics is predicting an individual's disease risk. Recent efforts have aimed at summarizing genome-wide risk for multiple traits and diseases using polygenic risk scores (PRS) [1][2][3][4][5][6] , which are derived by summing genome-wide common genetic variants associated with a given phenotype. PRS have demonstrated stratification of genetic disease risk, but there remains substantial unexplained variability in these predictions.…”