Genomic prediction of health traits in humans: demonstrating the value of marker selection.

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

Research output: Contribution to conference › Paper › peer-review

Abstract

In this study, we explored prediction of human height, high-density lipoproteins (HDL) and body mass index (BMI) using SNPs within a Croatian (N=2,186) and into a UK population (N=810) using two methods from livestock breeding: Bayes-C (using Gibbs sampling) and G-BLUP. Correlation between predicted and observed trait values in 10-fold cross-validation was used to assess prediction accuracy. Using all available 263,357 SNPs, Bayes-C and G-BLUP had similar prediction accuracy across traits within the Croatian data, and for height and BMI when predicting into the UK population. However, Bayes-C outperformed G-BLUP in the prediction of less polygenic HDL into the UK population. Feature selection allowed G-BLUP to achieve equivalent predictive performance to Bayes-C across all traits with greatly reduced computational effort. Feature selection in the G-BLUP framework therefore provides an efficient alternative to computationally expensive Bayes-C for traits considered in this study.
Original languageEnglish
Publication statusPublished - 17 Aug 2014
Event10th World Congress of Genetics Applied to Livestock Production - British Columbia, Vancouver, Canada
Duration: 17 Aug 2014 → 22 Aug 2014

Conference

Conference10th World Congress of Genetics Applied to Livestock Production
Country/TerritoryCanada
City Vancouver
Period17/08/14 → 22/08/14

Keywords / Materials (for Non-textual outputs)

  • Phenotype prediction
  • Feature section
  • Bayes-C
  • G-BLUP

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