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PLoS One. 2013 Dec 20;8(12):e83093. doi: 10.1371/journal.pone.0083093. eCollection 2013.

Use of net reclassification improvement (NRI) method confirms the utility of combined genetic risk score to predict type 2 diabetes.

Author information

  • 1Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • 2Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China ; Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China ; Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • 3Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • 4School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • 5Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America.

Abstract

BACKGROUND:

Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.

METHODOLOGY:

We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).

RESULTS:

We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10(-18)<P<8.5×10(-3)), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05<P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach (P<0.001).

CONCLUSION:

In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).

PMID:
24376643
[PubMed - indexed for MEDLINE]
PMCID:
PMC3869744
Free PMC Article
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