Improved heterosis prediction by combining information on DNA- and metabolic markers
Background: Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such...
Main Authors: | , , , , , , , |
---|---|
Format: | Others |
Language: | English |
Published: |
Universität Potsdam
2009
|
Subjects: | |
Online Access: | http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45132 http://opus.kobv.de/ubp/volltexte/2010/4513/ |
id |
ndltd-Potsdam-oai-kobv.de-opus-ubp-4513 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-Potsdam-oai-kobv.de-opus-ubp-45132013-01-08T00:59:09Z Improved heterosis prediction by combining information on DNA- and metabolic markers Gärtner, Tanja Steinfath, Matthias Andorf, Sandra Lisec, Jan Meyer, Rhonda C. Altmann, Thomas Willmitzer, Lothar Selbig, Joachim Life sciences Background: Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress. Dominance, overdominance or epistasis based models are commonly used explanations. Conclusion/Significance: The heterosis level is clearly a function of the combination of the parents used for offspring production. This results in a major challenge for plant breeders, as usually several thousand combinations of parents have to be tested for identifying the best combinations. Thus, any approach to reliably predict heterosis levels based on properties of the parental lines would be highly beneficial for plant breeding. Methodology/Principal Findings: Recently, genetic data have been used to predict heterosis. Here we show that a combination of parental genetic and metabolic markers, identified via feature selection and minimum-description-length based regression methods, significantly improves the prediction of biomass heterosis in resulting offspring. These findings will help furthering our understanding of the molecular basis of heterosis, revealing, for instance, the presence of nonlinear genotype-phenotype relationships. In addition, we describe a possible approach for accelerated selection in plant breeding. Universität Potsdam Mathematisch-Naturwissenschaftliche Fakultät. Institut für Biochemie und Biologie 2009 Postprint application/pdf urn:nbn:de:kobv:517-opus-45132 http://opus.kobv.de/ubp/volltexte/2010/4513/ PLoS one 4 (2009), 4, Art. e5220, DOI: 10.1371/journal.pone.0005220 eng http://creativecommons.org/licenses/by/3.0/ |
collection |
NDLTD |
language |
English |
format |
Others
|
sources |
NDLTD |
topic |
Life sciences |
spellingShingle |
Life sciences Gärtner, Tanja Steinfath, Matthias Andorf, Sandra Lisec, Jan Meyer, Rhonda C. Altmann, Thomas Willmitzer, Lothar Selbig, Joachim Improved heterosis prediction by combining information on DNA- and metabolic markers |
description |
Background:
Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress. Dominance, overdominance or epistasis based models are commonly used explanations.
Conclusion/Significance:
The heterosis level is clearly a function of the combination of the parents used for offspring production. This results in a major challenge for plant breeders, as usually several thousand combinations of parents have to be tested for identifying the best combinations. Thus, any approach to reliably predict heterosis levels based on properties of the parental lines would be highly beneficial for plant breeding.
Methodology/Principal Findings:
Recently, genetic data have been used to predict heterosis. Here we show that a combination of parental genetic and metabolic markers, identified via feature selection and minimum-description-length based regression methods, significantly improves the prediction of biomass heterosis in resulting offspring. These findings will help furthering our understanding of the molecular basis of heterosis, revealing, for instance, the presence of nonlinear genotype-phenotype relationships. In addition, we describe a possible approach for accelerated selection in plant breeding. |
author |
Gärtner, Tanja Steinfath, Matthias Andorf, Sandra Lisec, Jan Meyer, Rhonda C. Altmann, Thomas Willmitzer, Lothar Selbig, Joachim |
author_facet |
Gärtner, Tanja Steinfath, Matthias Andorf, Sandra Lisec, Jan Meyer, Rhonda C. Altmann, Thomas Willmitzer, Lothar Selbig, Joachim |
author_sort |
Gärtner, Tanja |
title |
Improved heterosis prediction by combining information on DNA- and metabolic markers |
title_short |
Improved heterosis prediction by combining information on DNA- and metabolic markers |
title_full |
Improved heterosis prediction by combining information on DNA- and metabolic markers |
title_fullStr |
Improved heterosis prediction by combining information on DNA- and metabolic markers |
title_full_unstemmed |
Improved heterosis prediction by combining information on DNA- and metabolic markers |
title_sort |
improved heterosis prediction by combining information on dna- and metabolic markers |
publisher |
Universität Potsdam |
publishDate |
2009 |
url |
http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45132 http://opus.kobv.de/ubp/volltexte/2010/4513/ |
work_keys_str_mv |
AT gartnertanja improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT steinfathmatthias improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT andorfsandra improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT lisecjan improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT meyerrhondac improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT altmannthomas improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT willmitzerlothar improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers AT selbigjoachim improvedheterosispredictionbycombininginformationondnaandmetabolicmarkers |
_version_ |
1716502344289484800 |