Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index.
With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.
Sunghwan
S
Bae
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.
Sungkyoung
S
Choi
Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.
Sung Min
SM
Kim
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.; Bioinformatics and Biostatistics Lab, Seoul National University, Seoul 08826, Korea.; Department of Statistics, Seoul National University, Seoul 08826, Korea.
Taesung
T
Park
body mass index; clinical prediction rule; genome-wide association study; penalized regression models; variable selection