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

Mairead Lesley Bermingham; Ricardo Pong-Wong; Athina Spiliopoulou; Caroline Hayward; Igor Rudan; Harry Campbell; Alan F. Wright; Jim Wilson; Felix Agakov; Pau Navarro; Chris Haley

doi:10.1038/srep10312

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

Mairead Lesley Bermingham, Ricardo Pong-Wong, Athina Spiliopoulou, Caroline Hayward, Igor Rudan, Harry Campbell, Alan F. Wright, Jim Wilson, Felix Agakov, Pau Navarro, Chris Haley

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	10312
Pages (from-to)	1-12
Number of pages	12
Journal	Scientific Reports
Volume	5
DOIs	https://doi.org/10.1038/srep10312
Publication status	Published - 19 May 2015

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http://www.nature.com/srep/2015/150519/srep10312/full/srep10312.html

A toolbox for the promotion of healthy ageing: Phenotypic prediction from genes and
Haley, C. (Principal Investigator), Agakov, F. (Co-investigator), Tenesa, A. (Co-investigator), Woolliams, J. (Co-investigator), Bermingham, M. (Researcher), Navarro, P. (Researcher), Pong-Wong, R. (Researcher) & Spiliopoulou, A. (Researcher)
Biotechnology and Biological Sciences Research Council
1/04/12 → 31/03/15
Project: Research
Orkney Complex Disease Study ORCADES Cardiovascular Study
Wilson, J. (Principal Investigator), Campbell, H. (Co-investigator) & Wild, S. (Co-investigator)
UK central government bodies/local authorities, health and hospital authorities
1/04/09 → 31/12/12
Project: Research
Orkney Cardiovascular Disease Study
Wilson, J. (Principal Investigator), Campbell, H. (Co-investigator) & Webb, D. (Co-investigator)
UK central government bodies/local authorities, health and hospital authorities
1/10/04 → 30/09/07
Project: Research

Ricardo Pong-Wong
- ricardo.pong-wongroslin.ed.acuk
- Royal (Dick) School of Veterinary Studies - Core Scientist in quantitative predictive biology
Person: Academic: Research Active

Cite this

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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.",

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AU - Bermingham, Mairead Lesley

AU - Pong-Wong, Ricardo

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AU - Rudan, Igor

AU - Campbell, Harry

AU - Wright, Alan F.

AU - Wilson, Jim

AU - Agakov, Felix

AU - Navarro, Pau

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AB - 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.

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

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Projects

A toolbox for the promotion of healthy ageing: Phenotypic prediction from genes and

Orkney Complex Disease Study ORCADES Cardiovascular Study

Orkney Cardiovascular Disease Study

Profiles

Ricardo Pong-Wong

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