title: Assessing the clinical utility of a genetic risk score constructed using 49 susceptibility alleles for type 2 diabetes in a Japanese population.
abstract: BACKGROUND
  Genome-wide association studies (GWASs) have identified over 60 susceptibility loci for type 2 diabetes (T2D). Although the ability of previous genetic information (∼40 loci) to discriminate between susceptible and nonsusceptible individuals is limited, the added benefit of updated genetic information has not been evaluated.
  
  
  OBJECTIVE
  We assessed the clinical utility of GWAS-derived T2D susceptibility variants in a Japanese population.
  
  
  METHODS
  We conducted a cross-sectional case-control study.
  
  
  METHODS
  T2D cases (n = 2613) and controls (n = 1786) with complete genotype data for 49 single-nucleotide polymorphisms (SNPs) were selected for analyses.
  
  
  METHODS
  We constructed genetic risk scores (GRSs) by summing the susceptibility alleles of 49 SNP loci for T2D (GRS-49) or 10 SNP loci with genome-wide significant association in previous Japanese studies (GRS-10) and examined the association of the GRSs with the disease by receiver operating characteristic analyses using a logistic regression model.
  
  
  RESULTS
  The GRS-49 was significantly associated with T2D (P = 8.75 × 10(-45)). The area under the curve (AUC) for GRS-49 alone (model 1) and for age, sex, and body mass index (model 2) was 0.624 and 0.743, respectively. Addition of the GRS-49 to model 2 resulted in a small but significant increase in the AUC (ΔAUC = 0.03, P = 7.99 × 10(-15)). Receiver operating characteristic AUC was greater for GRS-49 than for GRS-10 (0.624 vs 0.603, P = .019), whereas the odds ratio per risk allele was smaller for GRS-49 than for GRS-10 (GRS-49, 1.13, 95% confidence interval 1.11-1.15; GRS-10, 1.26, 95% confidence interval = 1.22-1.31, P = 7.31 × 10(-10)). The GRS-49 was significantly associated with age at diagnosis in 1591 cases (β = -0.199, P = .0069) and with fasting plasma glucose in 804 controls (β = 0.009, P = 0.021).
  
  
  CONCLUSIONS
  Updated genetic information slightly improves disease prediction ability but is not sufficiently robust for translation into clinical practice.
authors: Laboratory for Endocrinology, Metabolism, and Kidney Diseases, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. smaeda@src.riken.jp.
authors: Minako
authors: M
authors: Imamura
authors: Daichi
authors: D
authors: Shigemizu
authors: Tatsuhiko
authors: T
authors: Tsunoda
authors: Minoru
authors: M
authors: Iwata
authors: Hiroshi
authors: H
authors: Maegawa
authors: Hirotaka
authors: H
authors: Watada
authors: Hiroshi
authors: H
authors: Hirose
authors: Yasushi
authors: Y
authors: Tanaka
authors: Kazuyuki
authors: K
authors: Tobe
authors: Kohei
authors: K
authors: Kaku
authors: Atsunori
authors: A
authors: Kashiwagi
authors: Ryuzo
authors: R
authors: Kawamori
authors: Shiro
authors: S
authors: Maeda
mesh_terms: D000328:Adult; D000368:Aged; D000483:Alleles; D044466:Asian People / Q000235:genetics; D016022:Case-Control Studies; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D007564:Japan; D008297:Male; D008875:Middle Aged