CONTEXT: 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. DESIGN AND SETTING: We conducted a cross-sectional case-control study. PARTICIPANTS: T2D cases (n = 2613) and controls (n = 1786) with complete genotype data for 49 single-nucleotide polymorphisms (SNPs) were selected for analyses. OUTCOME MEASURES: 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.
CONTEXT: 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. DESIGN AND SETTING: We conducted a cross-sectional case-control study. PARTICIPANTS: T2D cases (n = 2613) and controls (n = 1786) with complete genotype data for 49 single-nucleotide polymorphisms (SNPs) were selected for analyses. OUTCOME MEASURES: 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: Ping Rao; Yong Zhou; Si-Qi Ge; An-Xin Wang; Xin-Wei Yu; Mohamed Ali Alzain; Andrea Katherine Veronica; Jing Qiu; Man-Shu Song; Jie Zhang; Hao Wang; Hong-Hong Fang; Qing Gao; You-Xin Wang; Wei Wang Journal: Int J Environ Res Public Health Date: 2016-08-30 Impact factor: 3.390
Authors: Geoffrey A Walford; Bianca C Porneala; Marco Dauriz; Jason L Vassy; Susan Cheng; Eugene P Rhee; Thomas J Wang; James B Meigs; Robert E Gerszten; Jose C Florez Journal: Diabetes Care Date: 2014-06-19 Impact factor: 19.112
Authors: Ping Rao; Hao Wang; Honghong Fang; Qing Gao; Jie Zhang; Manshu Song; Yong Zhou; Youxin Wang; Wei Wang Journal: Int J Environ Res Public Health Date: 2016-06-09 Impact factor: 3.390