Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores

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 Med 19 3, 322–329.

[1]  D. Cox.  Tests of Separate Families of Hypotheses , 1961 .

[2]  P. Poulsen,et al.  Heritability of Type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance – a population-based twin study , 1999, Diabetologia.

[3]  T. Valle,et al.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. , 2001, The New England journal of medicine.

[4]  Jaakko Tuomilehto,et al.  The diabetes risk score: a practical tool to predict type 2 diabetes risk. , 2003, Diabetes care.

[5]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[6]  P. Visscher,et al.  Common SNPs explain a large proportion of heritability for human height , 2011 .

[7]  Elias Mossialos,et al.  Health in the European Union: Trends and Analysis , 2010 .

[8]  L. Groop,et al.  Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study , 2011, Diabetologia.

[9]  Ewout W Steyerberg,et al.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers , 2011, Statistics in medicine.

[10]  Nicholas Eriksson,et al.  Comparison of Family History and SNPs for Predicting Risk of Complex Disease , 2012, PLoS genetics.

[11]  Tanya M. Teslovich,et al.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes , 2012, Nature Genetics.

[12]  M. Laakso,et al.  Genetic Screening for the Risk of Type 2 Diabetes , 2013, Diabetes Care.

[13]  P. Visscher,et al.  Pitfalls of predicting complex traits from SNPs , 2013, Nature Reviews Genetics.

[14]  S. Rosset,et al.  Measuring missing heritability: Inferring the contribution of common variants , 2014, Proceedings of the National Academy of Sciences.

[15]  Tanya M. Teslovich,et al.  Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility , 2014, Nature Genetics.

[16]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[17]  Tom R. Gaunt,et al.  Sixty-Five Common Genetic Variants and Prediction of Type 2 Diabetes , 2014, Diabetes.

[18]  Yang Zhao,et al.  Statistical analysis for genome-wide association study , 2014, Journal of biomedical research.

[19]  Helena Gandra.  Health in the European Union , 2019 .