Pure additive contribution of genetic variants to a risk prediction model using propensity score matching: application to type 2 diabetes
- Author:
Chanwoo PARK
1
;
Nan JIANG
;
Taesung PARK
Author Information
- Publication Type:Original Article
- Keywords: genome-wide association study; penalized regression model; propensity score; type 2 diabetes
- MeSH: Area Under Curve; Body Mass Index; Case-Control Studies; Genome-Wide Association Study; Korea; Logistic Models; Methods; Polymorphism, Single Nucleotide; Propensity Score; ROC Curve
- From:Genomics & Informatics 2019;17(4):47-
- CountryRepublic of Korea
- Language:English
- Abstract: The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.
