Corrigendum: Simple, standardized incorporation of genetic risk into non-genetic risk prediction tools for complex traits: coronary heart disease as an example

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... A genetic risk score (GRS) provides a means to aggregate the health-related risk of a collection of genetic alleles into a single number [6][7][8]. A GRS for an individual is calculated by summing the products between the number of high-risk variants inherited at a susceptibility polymorphism identified through GWAS and the log odds ratio for the same variant [9]. GRSs currently represent the most practical way of incorporating genetic risk into clinic practice for common polygenic disorders. ...
... Incorporating GRS of CAD into clinical practice has been hampered by several factors including the high cost of genotyping, the more modest effects of genetic variants on the risk of CAD than originally anticipated, and the challenge of improving clinical risk scores for CAD that already perform quite well [8,9,45]. While the cost of genotyping has dropped dramatically making large scale genotyping in clinical practice feasible in the near term, it remains difficult to demonstrate substantial improvements to standard model performance metrics, such as the C-statistic, with the addition of a GRS, even though many studies have highlighted its ability to independently predict incident CAD events. ...
Purpose of review: Genome-wide association studies (GWAS) have identified ∼60 loci for coronary artery disease (CAD). Through genetic risk scores (GRSs), investigators are leveraging this genomic information to gain insights on both the fundamental mechanisms driving these associations as well as their utility in improving risk prediction. Recent findings: GRSs of CAD track with the earliest atherosclerosis lesions in the coronary including fatty streaks and uncomplicated raised lesions. In multiple cohort studies, they predict incident CAD events independent of all traditional and lifestyle risk factors. The incorporation of SNPs with suggestive but not genome-wide association in GWAS into GRSs often increases the strength of these associations. GRS may also predict recurrent events and identify patients most likely to respond to statins. The effect of the GRS on discrimination metrics remains modest but the minimal degree of improvement needed for clinical utility is unknown. Summary: Most novel loci for CAD identified through GWAS facilitate the formation of coronary atherosclerosis and stratify individuals based on their underlying burden of coronary atherosclerosis. GRSs may one day be routinely used in clinical practice to not only assess the risk of incident events but also to predict who will respond best to established prevention strategies.
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