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

作者：Bermingham M L^*; Pong Wong R; Spiliopoulou A; Hayward C; Rudan I; Campbell H; Wright A F; Wilson J F; Agakov F; Navarro P; Haley C S

来源：Scientific Reports, 2015, 5(1): 10312.

DOI：10.1038/srep10312

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.

• 出版日期2015-5-19