title: "Noisy beets": impact of phenotyping errors on genomic predictions for binary traits in Beta vulgaris.
abstract: BACKGROUND
  Noise (errors) in scientific data is endemic and may have a detrimental effect on statistical analyses and experimental results. The effects of noisy data have been assessed in genome-wide association studies for case-control experiments in human medicine. Little is known, however, on the impact of noisy data on genomic predictions, a widely used statistical application in plant and animal breeding.
  
  
  RESULTS
  In this study, the sensitivity to noise in the data of five classification methods (K-nearest neighbours-KNN, random forest-RF, ridge logistic regression-LR, and support vector machines with linear or radial basis function kernels) was investigated. A sugar beet population of 123 plants phenotyped for a binary trait and genotyped for 192 SNP (single nucleotide polymorphism) markers was used. Labels (0/1 phenotype) were randomly sampled to generate noise. From the base scenario without errors in the labels, increasing proportions of noisy labels-up to 50 %-were generated and introduced in the data.
  
  
  CONCLUSIONS
  Local classification methods-KNN and RF-showed higher tolerance to noisy labels compared to methods that leverage global data properties-LR and the two SVM models. In particular, KNN outperformed all other classifiers with AUC (area under the ROC curve) higher than 0.95 up to 20 % noisy labels. The runner-up method, RF, had an AUC of 0.941 with 20 % noise.
authors.affiliation: Department of Bioinformatics and Biostatistics, PTP Science Park, Via Einstein - Loc. Cascina Codazza, 26900 Lodi, Italy.
authors.forename: Filippo
authors.identifier: 0000-0002-3901-2354
authors.initials: F
authors.lastname: Biscarini
authors.affiliation: Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, Lodi, Italy.
authors.forename: Nelson
authors.initials: N
authors.lastname: Nazzicari
authors.affiliation: DAFNE, Università di Padova, Padua, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Broccanello
authors.affiliation: DAFNE, Università di Padova, Padua, Italy.
authors.forename: Piergiorgio
authors.initials: P
authors.lastname: Stevanato
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan.
authors.forename: Simone
authors.initials: S
authors.lastname: Marini
keywords: Binomial phenotype; Classification; Genomic predictions; K-nearest neighbours (KNN); Noisy data; Random forest (RF); Ridge logistic regression; Robustness to errors; Sugar beet; Support vector machines (SVM)