Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

Abstract

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

Keywords: clinical prediction rule; genome-wide association study; penalized regression models; type 2 diabetes mellitus.

Fig. 1. Outline of the risk prediction model construction and validation. T2D, type 2 diabetes; CV, cross-validation; ALL, single-nucleotide polymorphisms (SNPs) only reported in the genome-wide association study (GWAS) catalog; KARE, Korean Association Resource; ASIAN, SNPs only reported in the GWAS catalog with an Asian population; ALL + KARE, combined SNPs in the GWAS catalog and KARE cohort; ASIAN + KARE, combined SNPs in the GWAS catalog with an Asian population and the KARE cohort; SLR, stepwise logistic regression; LASSO, least absolute shrinkage and selection operator; EN, Elastic-Net.

Fig. 2. Venn diagrams summarizing the number of variables shared among 5-fold CV by variables selection methods. CV, cross-validation; ALL, single-nucleotide polymorphisms (SNPs) only reported in the genome-wide association study (GWAS) catalog; ASIAN, SNPs only reported in the GWAS catalog with an Asian population; KARE, Korean Association Resource; ALL + KARE, combined SNPs in the GWAS catalog and KARE cohort; ASIAN + KARE, combined SNPs in the GWAS catalog with an Asian population and the KARE cohort; SLR, stepwise logistic regression; LASSO, least absolute shrinkage and selection operator; EN, Elastic-Net.

Fig. 3. Internal validation shows the AUC values for each combination of variable selection and prediction methods. Each bar represents one of five SNP data sets. AUC, area under the receiver operating characteristic curve; SNP, single-nucleotide polymorphism; ALL, SNPs only reported in the genome-wide association study (GWAS) catalog; ASIAN, SNPs only reported in the GWAS catalog with an Asian population; KARE, Korean Association Resource; ALL + KARE, combined SNPs in the GWAS catalog and KARE cohort; ASIAN + KARE, combined SNPs in the GWAS catalog with an Asian population and the KARE cohort; SLR, stepwise logistic regression; LASSO, least absolute shrinkage and selection operator; EN, Elastic-Net.

Fig. 4. External validation shows the AUC values for each combination of variable selection and prediction methods. Each bar represents one of five SNP data sets. AUC, area under the receiver operating characteristic curve; SNP, single-nucleotide polymorphism; ALL, SNPs only reported in the genome-wide association study (GWAS) catalog; ASIAN, SNPs only reported in the GWAS catalog with an Asian population; KARE, Korean Association Resource; ALL + KARE, combined SNPs in the GWAS catalog and KARE cohort; ASIAN + KARE, combined SNPs in the GWAS catalog with an Asian population and the KARE cohort; SLR, stepwise logistic regression; LASSO, least absolute shrinkage and selection operator; EN, Elastic-Net.