Open AccessGenetic markers of type 2 diabetes: Progress in genome‐wide association studies and clinical application for risk prediction
Author(s) -
Wang Xueyin,
Strizich Garrett,
Hu Yonghua,
Wang Tao,
Kaplan Robert C.,
Qi Qibin
Publication year - 2016
Publication title -
journal of diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.949
H-Index - 43
eISSN - 1753-0407
pISSN - 1753-0393
DOI - 10.1111/1753-0407.12323
Subject(s) - genome wide association study , genetic association , type 2 diabetes , imputation (statistics) , medicine , bioinformatics , genetics , computational biology , genotype , diabetes mellitus , biology , single nucleotide polymorphism , computer science , gene , machine learning , endocrinology , missing data
Type 2 diabetes ( T2D ) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome‐wide association studies ( GWAS ). Application of meta‐analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D , and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%–20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D .