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Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons97424

Steinfath,  M.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97274

Lisec,  J.
Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97302

Meyer,  R. C.
Developmental Physiology and Genomics, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97050

Altmann,  T.
Developmental Physiology and Genomics, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97481

Willmitzer,  L.
Small Molecules, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97409

Selbig,  J.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Steinfath-2010-Prediction of hybrid.pdf
(beliebiger Volltext), 314KB

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Zitation

Steinfath, M., Gaertner, T., Lisec, J., Meyer, R. C., Altmann, T., Willmitzer, L., et al. (2010). Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers. Theoretical and Applied Genetics, 120(2), 239-247. doi:10.1007/s00122-009-1191-2.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-230E-A
Zusammenfassung
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.