Application of high-dimensional feature selection: evaluation for genomic prediction in man (original) (raw)
ADS
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- Pong-Wong, R. ;
- Spiliopoulou, A. ;
- Hayward, C. ;
- Rudan, I. ;
- Campbell, H. ;
- Wright, A. F. ;
- Wilson, J. F. ;
- Agakov, F. ;
- Navarro, P. ;
- Haley, C. S.
Abstract
In 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.
Publication:
Scientific Reports
Pub Date:
May 2015
DOI:
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