Dataset Information

A Replication Study Identified Seven SNPs Associated with Quantitative Traits of Type 2 Diabetes Among Chinese Population (P18-079-19)

ABSTRACT: Abstract

Objectives

To explore genetic variants for quantitative traits of type 2 diabetes (T2D) such as insulin resistance and insulin release among Chinese adults.

Methods

A total of 2216 subjects were selected from cross-sectional 2010–2012 China National Nutrition and Health Surveillance. Fasting plasma glucose(FPG) and fasting serum insulin(FSIN) were tested and 81 T2D single nucleotide polymorphisms (SNPs) were genotyped. Insulin resistance was defined by exceeding top quartile of insulin resistance of homeostasis model assessment(HOMA-IR) index value and insulin release was defined under lower quartile of insulin release of homeostasis model assessment(HOMA-B) index value in nondiabetic individuals, respectively. Logisticregression model was utilized to explore association between each SNP and quantitative traits.

Results

The risk C allele of GRK5 rs10886471, the risk C allele of IGF2BP2 rs1470579 and the risk A allele of GCK rs4607517 was associated with increased HOMA-IR(P = 0.043, P = 0.014, P = 0.050), and the risk T allele of SEC16B rs574367 was associated with decreased HOMA-IR(P = 0.028). The risk A allele of SCAP rs4858889 associated with decreased HOMA-B(P = 0.004), while FTO rs1558902 and MAEA rs6815464 both had inverse effect on HOMA-B(P = 0.018, P = 0.048).

Conclusions

The rs10886471, rs1470579 and rs4607517 might be risk loci for T2D by mediating insulin receptors or insulin signal transduction to reduce insulin sensitivity. Individuals carrying the risk A allele of rs4858889 predisposed to T2D may by decreasing insulin secretion. Our findings will provide clue for casual relationship between SNPs and T2D-related traits.

Funding Sources

The Ministry of Healthand the Ministry of Science and Technology in China(2001-DEA30035, 2003-DIA6N008).

SUBMITTER: Liu A

PROVIDER: S-EPMC6574642 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Project description:BackgroundSeveral genome-wide association studies (GWAS) involving European populations have successfully identified risk genetic variants associated with type 2 diabetes mellitus (T2DM). However, the effects conferred by these variants in Han Chinese population have not yet been fully elucidated.MethodsWe analyzed the effects of 24 risk genetic variants with reported associations from European GWAS in 3,040 Han Chinese subjects in Taiwan (including 1,520 T2DM cases and 1,520 controls). The discriminative power of the prediction models with and without genotype scores was compared. We further meta-analyzed the association of these variants with T2DM by pooling all candidate-gene association studies conducted in Han Chinese.ResultsFive risk variants in IGF2BP2 (rs4402960, rs1470579), CDKAL1 (rs10946398), SLC30A8 (rs13266634), and HHEX (rs1111875) genes were nominally associated with T2DM in our samples. The odds ratio was 2.22 (95% confidence interval, 1.81-2.73, P<0.0001) for subjects with the highest genetic score quartile (score>34) as compared with subjects with the lowest quartile (score<29). The incoporation of genotype score into the predictive model increased the C-statistics from 0.627 to 0.657 (P<0.0001). These estimates are very close to those observed in European populations. Gene-environment interaction analysis showed a significant interaction between rs13266634 in SLC30A8 gene and age on T2DM risk (P<0.0001). Further meta-analysis pooling 20 studies in Han Chinese confirmed the association of 10 genetic variants in IGF2BP2, CDKAL1, JAZF1, SCL30A8, HHEX, TCF7L2, EXT2, and FTO genes with T2DM. The effect sizes conferred by these risk variants in Han Chinese were similar to those observed in Europeans but the allele frequencies differ substantially between two populations.ConclusionWe confirmed the association of 10 variants identified by European GWAS with T2DM in Han Chinese population. The incorporation of genotype scores into the prediction model led to a small but significant improvement in T2DM prediction.