QTL analysis of proteome and transcriptome variations for dissecting the genetic architecture of complex traits in maize

In this review, we present some studies on genetic analysis of proteome and transcriptome variations, which exemplify new strategies for a better understanding of the molecular and genetic bases of complex traits. A large genetic variability was revealed at the proteome expression level, which raise...

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Published in:Plant molecular biology Vol. 48; no. 5-6; pp. 575 - 581
Main Authors: Consoli, L, Lefèvre, A, Zivy, M, de Vienne, D, Damerval, C
Format: Journal Article
Language:English
Published: Netherlands Springer Nature B.V 01-03-2002
Springer Verlag (Germany)
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Summary:In this review, we present some studies on genetic analysis of proteome and transcriptome variations, which exemplify new strategies for a better understanding of the molecular and genetic bases of complex traits. A large genetic variability was revealed at the proteome expression level, which raised the possibility to predict phenotypical performance on the basis of gene product variability. This approach yielded limited results, but could be re-newed by extensive identification of proteins now allowed by mass spectrometry. The dissection of the genetic basis of the variation of individual protein amounts proves very powerful to select 'candidate' proteins, physiologically relevant for a given phenotypical trait, as shown by a study on the effect of water stress in maize. In order to investigate factors of grain quality in maize, we selected a regulatory locus known to control the expression of several storage protein genes, Opaque-2, and investigated the relationships between variability in zein amount and composition and the molecular polymorphism at this locus. Moreover, a QTL analysis revealed that the variability in Opaque-2 transcript abundance was controlled by several polymorphic trans-acting regulators unlinked to the Opaque-2 structural gene. Such genetic approaches should represent additional tools for physiological analysis of the huge amounts of data generated by transcritome and proteome projects.
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ISSN:0167-4412
1573-5028
DOI:10.1023/A:1014840810203