Heteroscedastic Ridge Regression Approaches for Genome-Wide Prediction With a Focus on Computational Efficiency and Accurate Effect Estimation

Supporting Information for Hofheinz and Frisch, 2014

Files in this Data Supplement:

• File S1 - Genotypic and phenotypic data of Pérez-Rodríguez et al. (2012), recoded; R code for marker effect estimation; Figure 1 (.zip, 170 KB)
• File S2 - R code for data simulation and effect estimation; R code for plotting estimated marker effects of the simulated data; Figure 2; Estimated marker effects used for plotting in fig-2-plot.R (.zip, 7 MB)
• File S3 - R code for converting the data of Crossa et al. (2010) for SelectionTools; R code for converting the data of Pérez-Rodríguez et al. (2012) for SelectionTools; R code for computing times of marker effect estimation in the data set of Crossa et al. (2010); R code for computing times of marker effect estimation in the sugar beet data set; R code for computing times of marker effect estimation in the data set of Pérez-Rodríguez et al. (2012); R code for computing times of marker effect estimation in the simulated data set; and genotypic and phenotypic data of sugar beet dataset (.zip, 234 KB)
• File S4 - R code for converting the data of Crossa et al. (2010) for SelectionTools; R code for converting the data of Pérez-Rodríguez et al. (2012) for SelectionTools; R code for cross validation in the data set of Crossa et al. (2010); R code for cross validation in the data set of the sugar beet data set; R code for cross validation in the data set of Pérez-Rodríguez et al. (2012); genotypic and phenotypic data of sugar beet dataset, trait SC; and genotypic and phenotypic data of sugar beet dataset, trait ML (.zip, 459 KB)
• Figure S5 - Marker effects (blue circles) estimated with different GWP approaches in the simulated data set plotted against marker locations [M] for the first chromosome. (.zip, 72 KB)