PLoS ONE (Jan 2015)

Cumulative effect and predictive value of genetic variants associated with type 2 diabetes in Han Chinese: a case-control study.

  • Yun Qian,
  • Feng Lu,
  • Meihua Dong,
  • Yudi Lin,
  • Huizhang Li,
  • Juncheng Dai,
  • Guangfu Jin,
  • Zhibin Hu,
  • Hongbing Shen

DOI
https://doi.org/10.1371/journal.pone.0116537
Journal volume & issue
Vol. 10, no. 1
p. e0116537

Abstract

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Genome-wide association studies (GWAS) have identified dozens of single nucleotide polymorphisms (SNPs) associated with type 2 diabetes risk. We have previously confirmed the associations of genetic variants in HHEX, CDKAL1, VEGFA and FTO with type 2 diabetes in Han Chinese. However, the cumulative effect and predictive value of these GWAS identified SNPs on the risk of type 2 diabetes in Han Chinese are largely unknown.We conducted a two-stage case-control study consisting of 2,925 cases and 3,281 controls to examine the association of 30 SNPs identified by GWAS with type 2 diabetes in Han Chinese. Significant associations were found for proxy SNPs at KCNQ1 [odds ratio (OR) = 1.41, P = 9.91 × 10-16 for rs2237897], CDKN2A/CDKN2B (OR = 1.30, P = 1.34 × 10-10 for rs10811661), CENTD2 (OR = 1.28, P = 9.88 × 10-4 for rs1552224) and SLC30A8 (OR = 1.19, P = 1.43 × 10-5 for rs13266634). We further evaluated the cumulative effect on type 2 diabetes of these 4 SNPs, in combination with 5 SNPs at HHEX, CDKAL1, VEGFA and FTO reported previously. Individuals carrying 12 or more risk alleles had a nearly 4-fold increased risk for developing type 2 diabetes compared with those carrying less than 6 risk alleles [adjusted OR = 3.68, 95% confidence interval (CI): 2.76-4.91]. Adding the genetic factors to clinical factors slightly improved the prediction of type 2 diabetes, with the area under the receiver operating characteristic curve increasing from 0.76 to 0.78. However, the difference was statistically significant (P < 0.0001).We confirmed associations of SNPs in KCNQ1, CDKN2A/CDKN2B, CENTD2 and SLC30A8 with type 2 diabetes in Han Chinese. The utilization of genetic information may improve the accuracy of risk prediction in combination with clinical characteristics for type 2 diabetes.