Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population

Abstract

Aims/hypothesis

We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score.

Methods

Participants ( n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls ( n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test.

Results

Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 ( p < 1.00 × 10 ⁻²⁰ vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 × 10 ⁻⁶ vs null model), but lower discriminative power than model 1 ( p = 5.92 × 10 ⁻⁵). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 × 10 ⁻⁹) and was not statistically different from model 1 ( p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 ( p = 2.30 × 10 ⁻⁷ vs null model) and smaller than model 1 ( p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model ( p < 1.0 × 10 ⁻²⁰) and model 1 ( p = 1.32 × 10 ⁻⁵); its ROC AUC was 0.626.

Conclusions/interpretation

Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabetes loci.