Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers.
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.
Steinfath
Matthias
M
Department of Bioinformatics, University of Potsdam, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany. steinfath@mpimp-golm.mpg.de
Gärtner
Tanja
T
Lisec
Jan
J
Meyer
Rhonda C
RC
Altmann
Thomas
T
Willmitzer
Lothar
L
Selbig
Joachim
J
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
D015415:Biomarkers