Phenotype prediction from genome-wide association studies: application to smoking behaviorsReport as inadecuate

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BMC Systems Biology

, 6:S11

First Online: 12 December 2012


BackgroundA great success of the genome wide association study enabled us to give more attention on the personal genome and clinical application such as diagnosis and disease risk prediction. However, previous prediction studies using known disease associated loci have not been successful Area Under Curve 0.55 ~ 0.68 for type 2 diabetes and coronary heart disease. There are several reasons for poor predictability such as small number of known disease-associated loci, simple analysis not considering complexity in phenotype, and a limited number of features used for prediction.

MethodsIn this research, we investigated the effect of feature selection and prediction algorithm on the performance of prediction method thoroughly. In particular, we considered the following feature selection and prediction methods: regression analysis, regularized regression analysis, linear discriminant analysis, non-linear support vector machine, and random forest. For these methods, we studied the effects of feature selection and the number of features on prediction. Our investigation was based on the analysis of 8,842 Korean individuals genotyped by Affymetrix SNP array 5.0, for predicting smoking behaviors.

ResultsTo observe the effect of feature selection methods on prediction performance, selected features were used for prediction and area under the curve score was measured. For feature selection, the performances of support vector machine SVM and elastic-net EN showed better results than those of linear discriminant analysis LDA, random forest RF and simple logistic regression LR methods. For prediction, SVM showed the best performance based on area under the curve score. With less than 100 SNPs, EN was the best prediction method while SVM was the best if over 400 SNPs were used for the prediction.

ConclusionsBased on combination of feature selection and prediction methods, SVM showed the best performance in feature selection and prediction.

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Author: Dankyu Yoon - Young Jin Kim - Taesung Park


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