Polygenic risk for coronary artery disease in the Scottish and English population

Abstract

Background Epidemiological studies have repeatedly observed a markedly higher risk for coronary artery disease (CAD) in Scotland as compared to England. Up to now, it is unclear whether environmental or genetic factors might explain this phenomenon. Methods Using UK Biobank (UKB) data, we assessed CAD risk, based on the Framingham risk score (FRS) and common genetic variants, to explore the respective contribution to CAD prevalence in Scotland (n = 31,963) and England (n = 317,889). We calculated FRS based on sex, age, body mass index (BMI), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), antihypertensive medication, smoking status, and diabetes. We determined the allele frequency of published genome-wide significant risk CAD alleles and a weighted genetic risk score (wGRS) for quantifying genetic CAD risk. Results Prevalence of CAD was 16% higher in Scotland as compared to England (8.98% vs. 7.68%, P < 0.001). However, the FRS only predicted a marginally higher CAD risk (less than 1%) in Scotland (12.5 ± 10.5 vs.12.6 ± 10.6, P = 0.03). Likewise, the overall number of genome-wide significant variants affecting CAD risk (157.6 ± 7.7 and 157.5 ± 7.7; P = 0.12) and a wGRS for CAD (2.49 ± 0.25 in both populations, P = 0.14) were remarkably similar in the English and Scottish population. Interestingly, we observed substantial differences in the allele frequencies of individual risk variants. Of the previously described 163 genome-wide significant variants studied here, 35 variants had higher frequencies in Scotland, whereas 37 had higher frequencies in England (P < 0.001 each). Conclusions Neither the traditional risk factors included in the FRS nor a genetic risk score (GRS) based on established common risk alleles explained the higher CAD prevalence in Scotland. However, we observed marked differences in the distribution of individual risk alleles, which emphasizes that even geographically and ethnically closely related populations may display relevant differences in the genetic architecture of a common disease.

Reclassification of coronary artery disease risk using genetic risk score among subjects with borderline or intermediate clinical risk

“…Results from our study are generally consistent with previous studies on PRS of CAD [3] , [8] , including those utilizing the UKB data [9] , [10] , [11] , [12] . However, our study differs from published studies in the objective, design, and methodology.…”

An independent external validation of the QRISK3 cardiovascular risk prediction model applied to UK Biobank participants

“…The extent to which the authors of the identified studies address the discrimination and the calibration of QRISK3 applied to UK Biobank data varies, and the extent to which this may influence their conclusions depends on their study aim. In five of the identified studies, the authors do not address discrimination or calibration [11,12,13,14,15], three only address discrimination [16,17,18] and one quotes a measure of discrimination calculated in another study [19]. The authors of one study found that the mean 10-year QRISK3 predicted CVD risk was greater than the observed 10-year event rate of coronary artery disease (CAD) in UK Biobank [20] and recalibrated QRISK3 for their analyses [20].…”

Clinical Prediction Models in Epidemiological Studies: Lessons from the Application of QRISK3 to UK Biobank Data

“…In three studies the authors used QRISK3 to compare predicted risk between groups of UK Biobank participants; the authors of one study examined the association between the QRISK3 scores of participants and their educational attainment and statin use (Carter et al, 2021). QRISK3 scores were used in the analysis of another study to estimate the contribution of risk factors included in QRISK3 to the higher risk of CAD in the Scottish compared to the English populations in the UK Biobank (Yang et al, 2021). Authors of the last study calculated the QRISK3 score to estimate the risk scores of South Asian and European UK Biobank participants.…”

“…The extent to which the identified studies address the discrimination and the calibration of QRISK3 applied to UK Biobank data varies. Some studies do not address discrimination or calibration at all (Welsh et al, 2020;Carter et al, 2021;Yang et al, 2021;Patel et al, 2021;Berry et al, 2021) and some only address discrimination (Elliott et al, 2020;Trinder et al, 2021;Riveros-Mckay et al, 2021) (with one quoting a measure of discrimination calculated in another study (Dolezalova et al, 2021)). One study discusses both discrimination and calibration, finding that the mean 10-year QRISK3 predicted CVD risk was markedly greater than the observed 10year event rate of CAD (Agrawal et al, 2021).…”

“…One solution to avoid the pitfalls of using a model with poor discrimination and/or calibration is to use a different model. Researchers have compared QRISK3 to other models such as polygenic risk scores (Elliott et al, 2020;Riveros-Mckay et al, 2021;Yang et al, 2021), genetic risk scores (Trinder et al, 2021), machine learning methods (Agrawal et al, 2021;Dolezalova et al, 2021), or just using the covariates included in QRISK3 to create their own risk prediction models (Welsh et al, 2020). In some of these studies (Elliott et al, 2020;Trinder et al, 2021;Riveros-Mckay et al, 2021) the authors report that these models have markedly better performance in UK Biobank than QRISK3 applied crudely to this population.…”