PLoS ONE (Jan 2016)

Replication and Relevance of Multiple Susceptibility Loci Discovered from Genome Wide Association Studies for Type 2 Diabetes in an Indian Population.

  • Nagaraja M Phani,
  • Prabha Adhikari,
  • Shivashankara K Nagri,
  • Sydney C D'Souza,
  • Kapaettu Satyamoorthy,
  • Padmalatha S Rai

DOI
https://doi.org/10.1371/journal.pone.0157364
Journal volume & issue
Vol. 11, no. 6
p. e0157364

Abstract

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AIM:Several genetic variants for type 2 diabetes (T2D) have been identified through genome wide association studies (GWAS) from Caucasian population; however replication studies were not consistent across various ethnicities. Objective of the current study is to examine the possible correlation of 9 most significant GWAS single nucleotide polymorphisms (SNPs) for T2D susceptibility as well as the interactive effect of these variants on the risk of T2D in an Indian population. METHODS:Case-control cohorts of 1156 individuals were genotyped for 9 SNPs from an Indian population. Association analyses were performed using logistic regression after adjusting for covariates. Multifactor dimensionality reduction (MDR) analysis was adopted to determine gene-gene interactions and discriminatory power of combined SNP effect was assessed by grouping individuals based on the number of risk alleles and by calculating area under the receiver-operator characteristic curve (AUC). RESULTS:We confirm the association of TCF7L2 (rs7903146) and SLC30A8 (rs13266634) with T2D. MDR analysis showed statistically significant interactions among four SNPs of SLC30A8 (rs13266634), IGF2BP2 (rs4402960), HHEX (rs1111875) and CDKN2A (rs10811661) genes. Cumulative analysis showed an increase in odds ratio against the baseline group of individuals carrying 5 to 6 risk alleles and discriminatory power of genetic test based on 9 variants showed higher AUC value when analyzed along with body mass index (BMI). CONCLUSION:These results provide a strong evidence for independent association between T2D and SNPs for in TCF7L2 and SLC30A8. MDR analysis demonstrates that independently non-significant variants may interact with one another resulting in increased disease susceptibility in the population tested.