title: Phenomic prediction of maize hybrids.
abstract: Phenomic experiments are carried out in large-scale plant phenotyping facilities that acquire a large number of pictures of hundreds of plants simultaneously. With the aid of automated image processing, the data are converted into genotype-feature matrices that cover many consecutive days of development. Here, we explore the possibility of predicting the biomass of the fully grown plant from early developmental stage image-derived features. We performed phenomic experiments on 195 inbred and 382 hybrid maizes varieties and followed their progress from 16 days after sowing (DAS) to 48 DAS with 129 image-derived features. By applying sparse regression methods, we show that 73% of the variance in hybrid fresh weight of fully-grown plants is explained by about 20 features at the three-leaf-stage or earlier. Dry weight prediction explained over 90% of the variance. When phenomic features of parental inbred lines were used as predictors of hybrid biomass, the proportion of variance explained was 42 and 45%, for fresh weight and dry weight models consisting of 35 and 36 features, respectively. These models were very robust, showing only a small amount of variation in performance over the time scale of the experiment. We also examined mid-parent heterosis in phenomic features. Feature heterosis displayed a large degree of variance which resulted in prediction performance that was less robust than models of either parental or hybrid predictors. Our results show that phenomic prediction is a viable alternative to genomic and metabolic prediction of hybrid performance. In particular, the utility of early-stage parental lines is very encouraging.
authors.affiliation: Bioinformatics Group, Institute for Biochemistry and Biology, University of Potsdam, 14476, Germany; Max Planck Institute of Molecular Plant Physiology, Potsdam 14476, Germany. Electronic address: edlich@mpimp-golm.mpg.de.
authors.forename: Christian
authors.initials: C
authors.lastname: Edlich-Muth
authors.affiliation: Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Stadt Seeland, Germany.
authors.forename: Moses M
authors.initials: MM
authors.lastname: Muraya
authors.affiliation: Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Stadt Seeland, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Altmann
authors.affiliation: Bioinformatics Group, Institute for Biochemistry and Biology, University of Potsdam, 14476, Germany; Max Planck Institute of Molecular Plant Physiology, Potsdam 14476, Germany.
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D000465:Algorithms; D018533:Biomass; D005838:Genotype; D006823:Hybrid Vigor / Q000235:genetics*; D006824:Hybridization, Genetic; D007178:Inbreeding; D008957:Models, Genetic; D010641:Phenotype; D013997:Time Factors; D003313:Zea mays / Q000235:genetics* / Q000254:growth & development*
keywords: Hybrid prediction; LASSO; Maize; Phenomics; Regression