TY  - JOUR
A1  - Edlich-Muth, Christian
A1  - Muraya, Moses M.
A1  - Altmann, Thomas
A1  - Selbig, Joachim
T1  - Phenomic prediction of maize hybrids
JF  - Biosystems : journal of biological and information processing sciences
N2  - 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. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
KW  - Hybrid prediction
KW  - LASSO
KW  - Regression
KW  - Maize
KW  - Phenomics
Y1  - 2016
U6  - https://doi.org/10.1016/j.biosystems.2016.05.008
SN  - 0303-2647
SN  - 1872-8324
VL  - 146
SP  - 102
EP  - 109
PB  - Elsevier
CY  - Oxford
ER  -