Abstract
Aims
Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated.
Methods
Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053).
Results
The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs.
Conclusions
The metaGRS significantly improves T2D prediction ability.
1 Application
Application ID | Title |
51470 | Physical measurement, blood biochemistry, lifestyle, environmental exposure: causality, gene-environment interaction in relation to metabolic diseases and cancer risk. |
1 Return
Return ID | App ID | Description | Archive Date |
3789 | 51470 | Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank | 6 Sep 2021 |