In:
Seed Science Research, Cambridge University Press (CUP), Vol. 26, No. 1 ( 2016-03), p. 57-66
Kurzfassung:
New trends in crop breeding include analytical approaches to identify metabolic fingerprints that can be used for associations to the genetic background. The biochemical phenotype, as a result of plant endogenous factors and interaction with the environment, has the potential to increase the accuracy of forecasting regarding agronomical quality factors. In this study a metabolite profile analysis by gas chromatography–mass spectrometry (GC–MS) was conducted on sets of seed material from sugar beet. One set represented high-performing varieties with a close genetic background and with a similar quality in terms of germination capacity. The second set contained seed lots from different genotypes comprising different germination capacities. By multivariate statistical analyses high variance in both sample sets was revealed. These data were further allocated to corresponding metabolite classes. It could be shown that an untargeted GC–MS approach has the power to resolve differences in the molecular phenotypes of related offspring lines. Metabolic profiles were found to correlate more to genotypic differences than to differences in the germination capacity.
Materialart:
Online-Ressource
ISSN:
0960-2585
,
1475-2735
DOI:
10.1017/S0960258515000379
Sprache:
Englisch
Verlag:
Cambridge University Press (CUP)
Publikationsdatum:
2016
ZDB Id:
2016354-X
SSG:
12
