title: Differentially penalized regression to predict agronomic traits from metabolites and markers in wheat.
abstract: BACKGROUND
  Genomic prediction of agronomic traits as targets for selection in plant breeding programmes is increasingly common. The methods employed can also be applied to predict traits from other sources of covariates, such as metabolomics. However, prediction combining sets of covariates can be less accurate than using the best of the individual sets.
  
  
  RESULTS
  We describe a method, termed Differentially Penalized Regression (DiPR), which uses standard ridge regression software to combine sets of covariates while applying independent penalties to each. In a dataset of wheat varieties, field traits are better predicted, on average, by seed metabolites than by genetic markers, but DiPR using both sets of predictors is best.
  
  
  CONCLUSIONS
  DiPR is a simple and accessible method of using existing software to combine multiple sets of covariates in trait prediction when there are more predictors than observations and the contribution to accuracy from each set differs.
authors: Plant Biology and Crop Science, Rothamsted Research, Harpenden, AL5 2JQ, UK. jane.ward@rothamsted.ac.uk.
authors: Jane
authors: J
authors: Ward
authors: Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, P.O. Box 19. 2462, Martonvásár, Hungary. rakszegi.mariann@agrar.mta.hu.
authors: Mariann
authors: M
authors: Rakszegi
authors: Agricultural Institute, Centre for Agricultural Research, Hungarian Academy of Sciences, P.O. Box 19. 2462, Martonvásár, Hungary. bedo.zoltan@agrar.mta.hu.
authors: Zoltán
authors: Z
authors: Bedő
authors: Plant Biology and Crop Science, Rothamsted Research, Harpenden, AL5 2JQ, UK. peter.shewry@rothamsted.ac.uk.
authors: Peter R
authors: PR
authors: Shewry
authors: John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge, CB3 0LE, UK. ian.mackay@niab.com.
authors: Ian
authors: I
authors: Mackay
mesh_terms: D005819:Genetic Markers; D012984:Software; D014908:Triticum / Q000737:chemistry* / Q000145:classification / Q000235:genetics*
chemical_list: D005819:Genetic Markers