Physicians' Academy for Cardiovascular Education

New genomic risk score improves prediction of coronary artery disease

Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults Implications for Primary Prevention

Literature - Inouye M, Abraham G, Nelson CP et al. - J Am Coll Cardiol 2018;72:1883–93

Introduction and methods

Family history is a known risk factor for coronary artery disease (CAD), and the heritability of CAD has been estimated to be 40% to 60%. Existing genomic risk scores (GRS) have limited use due to a number of reasons, including insufficient accuracy for CAD [1-3]. In this study, a novel GRS for CAD was developed, and its potential as a screening tool for primary prevention was evaluated.

For this purpose, a meta-analytic strategy was used that captures the totality of information from the largest previous genome-wide association studies (GWAS). Subsequently, the external performance of this meta score (meta-GRS) was assessed in stratifying CAD risk in >480,000 individuals (aged 40-69 years) from the UK Biobank (UKB) [4].

The meta-GRS was constructed based on 3 GRSs:

Main results

Conclusion

The meta-GRS developed and evaluated in the present study achieved greater risk discrimination than previously available genetic risk scores that were based on selected genetic variants. Meta-GRS provides the opportunity to stratify individuals for different trajectories of CAD risk in general populations and highlights the potential for genomic screening in early life to complement conventional risk prediction.

Editorial comment

In his editorial article, Natarajan [5] discusses the need for improvement of CAD risk prediction, and states that current polygenic risk scores are lacking the full spectrum of genetic variation that influences CAD risk, since only whole genome sequencing can identify genomic variation. He also points out the need for relevant prospective randomized controlled studies, which would trigger guidelines changes in this direction. He concludes that health systems currently insufficiently manage to identify those likely to sustain premature CHD. ‘Inouye et al. show that incorporation of CHD polygenic risk with clinical risk factors can improve risk prediction and may help identify individuals who are candidates for earlier preventive therapies. Additionally, this single genetic test (currently <$100) only needs to be performed once, and this framework can be applied to calculate polygenic risk for virtually any trait.’

References

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