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


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.

SUBMITTER: Bermingham ML 

PROVIDER: S-EPMC4437376 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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

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

Scientific reports 20150519


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 heig  ...[more]

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