Abstract
Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8–4.6] up to 6.2 [4.6–7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.
Highlights
Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed
We (1) show that polygenic risk scores (PRS) combined with simple and surveyable risk factors, including demographic and lifestyle factors, and comorbidities, provide a viable tool to identify high-risk individuals in coronary heart disease (CHD) and type 2 diabetes (T2D), (2) show that Genomics-enhanced RIsk Tools (GRIT) combining genome-wide risk and these simple risk factors for CHD and T2D (GRIT-CHD and GRIT-T2D) have at least comparable performance to risk scores advocated by clinical guidelines, and (3) show that adding standard lipid and blood pressure measurements to our GRIT scores leads to notable performance improvements over current clinical risk scores for CHD and T2D
We built genome-wide PRSs for CHD and T2D by obtaining weights from the largest genome-wide association studies (GWASs) on European-ancestry individuals that do not overlap with UK Biobank[12,13]
Summary
Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. As a growing number of individuals have genome data available, one potential approach for improving risk estimation is to utilize PRSs together with simple online questionnaires to preselect people from the population for further comprehensive clinical risk evaluation. We (1) show that PRSs combined with simple and surveyable risk factors, including demographic and lifestyle factors, and comorbidities, provide a viable tool to identify high-risk individuals in CHD and T2D, (2) show that Genomics-enhanced RIsk Tools (GRIT) combining genome-wide risk and these simple risk factors for CHD and T2D (GRIT-CHD and GRIT-T2D) have at least comparable performance to risk scores advocated by clinical guidelines, and (3) show that adding standard lipid and blood pressure measurements to our GRIT scores leads to notable performance improvements over current clinical risk scores for CHD and T2D. We derive our risk tools in the Finnish biobank study, FinnGen, and externally validate them in an independent cohort, the UK Biobank
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