Phenomic Selection: predicting quantitative traits in canola with NIRSprofiles

Lennard Ehrig and Sven Weber are postdoctorate students at the Department of Plant Breeding at Justus Liebig University in Germany

ABSTRACT
Phenomic selection (PS) is a recent and promising approach in plant breeding that can compete to genomic selection (GS) while costs are reduced by using nondestructive,
high-throughput phenotyping methods like near-infrared reflectance spectroscopy to obtain predictors for numerous traits. Here, we applied this concept to canola test-hybrids to evaluate the performance of predicting different quantitative traits with linear and machine learning models. Various cross-validation scenarios showed that PS can be an efficient alternative to GS for selecting potential hybrid parents that is less influenced by population structure. 

PROFILE
Lenard Ehrig is a  student at the Department of Plant Breeding at Justus Liebig University in Giessen, Germany. From there he also graduated in 2021 with a master`s degree in crop science. His work focuses on quantitative genetics and genomic prediction in canola using various Machine Learning methods. Recently he is interested in connecting genotypic and multispectral data to build multi-omics approaches for the prediction of complex traits.

As a PhD student at the Department of Plant Breeding in Giessen, Germany under the supervision of Professor Rod Snowdon, Sven Weber focuses on predictive breeding in general. His
research covers quantitative genetics tasks like haplotype block and structural variation-based genomic selection as well as Machine Learning based high throughput phenotyping with 3D
plant scans and hyperspectral data from UAVs. He is interested in the relationship between genotype, phenotype, and architectural interaction between traits.

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