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Nature Research, Scientific Reports, 1(5), 2015

DOI: 10.1038/srep10312

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Application of high-dimensional feature selection: evaluation for genomic prediction in man

Journal article published in 2015 by M. L. Bermingham, R. Pong-wong, C. Hayward, A. Spiliopoulou, I. Rudan, H. Campbell, A. F. Wright, J. F. Wilson, F. Agakov, P. Navarro ORCID, C. S. Haley
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.