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The identification of QTL involved in heterosis formation is one approach to unravel the not yet fully understood genetic basis of heterosis - the improved agronomic performance of hybrid F1 plants compared to their inbred parents. The identification of candidate genes underlying a QTL is important both for developing markers and determining the molecular genetic basis of a trait, but remains difficult owing to the large number of genes often contained within individual QTL. To address this problem in heterosis analysis, we applied a meta-analysis strategy for grain yield (GY) of Zea mays L. as example, incorporating QTL-, hybrid field-, and parental gene expression data.
The microarray technology has become an established approach for large-scale gene expression analysis with mature protocols for sample, microarray, and data processing. The maize oligonucleotide array (maizearray) is one of the few microarray platforms designed for genome-wide gene expression analysis in Zea mays L. Many datas-ets addressing various genetic, physiological and developmental topics generated with this platform are available. The original 57,452 microarray probes were compiled based on expressed sequence tags (ESTs). Meanwhile the maize genome sequence became available providing the possibility for an improved annotation of the microar-ray probe set. In this study we determined the genome positions of all maizearray probes to obtain current gene annotations and generated current Gene Ontology (GO) annotations. These new data allow tracing redundancy of the probe set and interfering cross-hybridizations, and doubled the number of genes with functional GO data. Our re-annotation will largely improve the functional analysis of available and future datasets generated on this microarray platform.
Die Microarray-Technologie hat sich zu einem etablierten Ansatz der Hochdurchsatz-Genexpressionsanalyse entwickelt. Das „maize oligonucleotide array“ (maizearray) ist eine der wenigen Microarray-Plattformen, welche für die genomweite Genexpressionsanalyse von Mais (Zea mays L.) erzeugt wurden. Die Sonden wurden basierend auf ESTs (expressed sequence tags) generiert. Mittlerweile ist die Genomsequenz von Mais verfügbar und ermöglicht eine genauere Annotation dieser Sonden. In dieser Arbeit wurden die Genompositionen aller Sonden und basierend darauf die zugrunde liegenden Gene sowie deren funktionelle Annotation bestimmt. Durch die Analyse konnten Redundanzen und nicht eindeutig bindende Sonden aufgedeckt und gleichzeitig die Zahl der Gene mit funktioneller Annotation verdoppelt werden. Unsere Reannotation wird funktionelle Analysen bereits existierender und zukünftiger Datensätze stark verbessern.