Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores.
Genet Med 2016;
19:322-329. [PMID:
27513194 PMCID:
PMC5506454 DOI:
10.1038/gim.2016.103]
[Citation(s) in RCA: 104] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 06/20/2016] [Indexed: 12/16/2022] Open
Abstract
Purpose:
Using effect estimates from genome-wide association studies (GWAS), we identified
a genetic risk score (GRS) that has the strongest association with type 2 diabetes
(T2D) status in a population-based cohort and investigated its potential for
prospective T2D risk assessment.
Methods:
By varying the number of single-nucleotide polymorphisms (SNPs) and their
respective weights, alternative versions of GRS can be computed. They were tested
in 1,181 T2D cases and 9,092 controls of the Estonian Biobank cohort. The
best-fitting GRS was chosen for the subsequent analysis of incident T2D (386
cases).
Results:
The best fit was provided by a novel doubly weighted GRS that captures
the effect of 1,000 SNPs. The hazard for incident T2D was 3.45 times (95% CI:
2.31–5.17) higher in the highest GRS quintile compared with the lowest
quintile, after adjusting for body mass index and other known predictors. Adding
GRS to the prediction model for 5-year T2D risk resulted in continuous net
reclassification improvement of 0.324 (95% CI: 0.211–0.444). In
addition, a significant effect of the GRS on all-cause and cardiovascular
mortality was observed.
Conclusion:
The proposed GRS would improve the accuracy of T2D risk prediction when added to
the currently used set of predictors.
Genet Med19 3, 322–329.
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