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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-09

AUTHORS

M. L. Bermingham, R. Pong-Wong, A. Spiliopoulou, C. Hayward, I. Rudan, H. Campbell, A. F. Wright, J. F. Wilson, F. Agakov, P. Navarro, C. S. Haley

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. More... »

PAGES

10312

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/srep10312

    DOI

    http://dx.doi.org/10.1038/srep10312

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1047984410

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/25988841


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