Has the time come to integrate genetic risk scores into clinical practice?

Abstract

Abstract Background The risk for Coronary Artery Disease (CAD) is determined by both genetic and environmental factors, as well as by the interaction between them. It is estimated that genetic factors could account for 40% to 55% of the existing variability among the population (inheritability). Therefore, some authors have advised that it is time we integrated genetic risk scores into clinical practice. Aim The aim of this study was to evaluate the magnitude of the association between an additive genetic risk score (aGRS) and CAD based on the cumulative number of risk alleles in these variants, and to estimate whether their use is valuable in clinical practice. Methods A case-control study was performed in a Portuguese population. We enrolled 3120 participants, of whom 1687 were CAD patients and 1433 were normal controls. Controls were paired to cases with respect to gender and age. 33 genetic variants known to be associated with CAD were selected, and an aGRS was calculated for each individual. The aGRS was further subdivided into deciles groups, in order to estimate the CAD risk in each decile, defined by the number of risk alleles. The magnitude of the risk (odds ratio) was calculated for each group by multiple logistic regression using the 5th decile as the reference group (median). In order to evaluate the ability of the aGRS to discriminate susceptibility to CAD, two genetic models were performed, the first with traditional risk factors (TRF) and second with TRF plus aGRS. The AUC of the two ROC curves was calculated. Results A higher prevalence of cases over controls became apparent from the 6th decile of the aGRS, reflecting the higher number of risk alleles present (see figure). The difference in CAD risk was only significant from the 6th decile, increasing gradually until the 10th decile. The odds ratio (OR) for the last decile related to 5th decile (median) was 1.87 (95% CI:1.36–2.56; p<0.0001). The first model yielded an AUC=0.738 (95% CI:0.720–0.755) and the second model was slightly more discriminative for CAD risk (AUC=0.748; 95% CI:0.730–0.765). The DeLong test was significant (p=0.0002). Conclusion Adding an aGRS to the non-genetic risk factors resulted in a modest improvement in the ability to discriminate the risk of CAD. Such improvement, even if statistically significant, does not appear to be of real value in clinical practice yet. We anticipate that with the development of further knowledge about different SNPs and their complex interactions, and with the inclusion of rare genetic variants, genetic risk scores will be better suited for use in a clinical setting. Funding Acknowledgement Type of funding source: None