@article{Bermingham2015, abstract = {AbstractIn this study, we investigated the effect of five feature selection approaches on the performance of a mixed model (G-BLUP) and a Bayesian (Bayes C) prediction method. We predicted height, high density lipoprotein cholesterol (HDL) and body mass index (BMI) within 2,186 Croatian and into 810 UK individuals using genome-wide SNP data. Using all SNP information Bayes C and G-BLUP had similar predictive performance across all traits within the Croatian data and for the highly polygenic traits height and BMI when predicting into the UK data. Bayes C outperformed G-BLUP in the prediction of HDL, which is influenced by loci of moderate size, in the UK data. Supervised feature selection of a SNP subset in the G-BLUP framework provided a flexible, generalisable and computationally efficient alternative to Bayes C; but careful evaluation of predictive performance is required when supervised feature selection has been used.}, author = {Bermingham, M. L. and Pong-wong, R. and Hayward, C. and Spiliopoulou, A. and Rudan, I. and Campbell, H. and Wright, A. F. and Wilson, J. F. and Agakov, F. and Navarro, P. and Haley, C. S.}, doi = {10.1038/srep10312}, journal = {Scientific Reports}, month = {may}, title = {Application of high-dimensional feature selection: evaluation for genomic prediction in man}, url = {http://dx.doi.org/10.1038/srep10312}, volume = {5}, year = {2015} }