title: Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers.
abstract: A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.
authors.affiliation: Department of Bioinformatics, University of Potsdam, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany. steinfath@mpimp-golm.mpg.de
authors.forename: Matthias
authors.initials: M
authors.lastname: Steinfath
authors.forename: Tanja
authors.initials: T
authors.lastname: Gärtner
authors.forename: Jan
authors.initials: J
authors.lastname: Lisec
authors.forename: Rhonda C
authors.initials: RC
authors.lastname: Meyer
authors.forename: Thomas
authors.initials: T
authors.lastname: Altmann
authors.forename: Lothar
authors.initials: L
authors.lastname: Willmitzer
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D017360:Arabidopsis / Q000235:genetics* / Q000254:growth & development; D015415:Biomarkers / Q000378:metabolism; D018533:Biomass; D005821:Genetic Techniques; D005838:Genotype; D006823:Hybrid Vigor; D006824:Hybridization, Genetic; D007178:Inbreeding; D020641:Polymorphism, Single Nucleotide
chemical_list: D015415:Biomarkers