Next Generation forward genetics for crop plants
BMBF funded project in cooperation with KWS Saat AG, Einbeck; Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Holtsee; Syngenta Seeds GmbH, Bad Salzuflen; Strube Research GmbH & Co. KG, Söllingen; MPI-DB, Tübingen; CA University Kiel; University Bielefeld; dsv-saaten Lippstadt
The identification of agronomically important genes and / or allelic variants in crops is a prerequisite for the future rapid development of new genetic material to increase yield and to ensure yield stability, especially in view of the expected global climate change. Of particular interest are the tolerance to frost, drought or salt, the oil, protein or starch content in seeds, the time of flowering or the resistance to insects. However, the fact that many of these agriculturally useful plant traits are controlled by multiple gene functions and interactions, highlights the need for a method to directly link each (commercially) important trait to the causative genetic background with molecular markers.
An important step towards that goal was the identification of gene mutations by means of Next-Generation Sequencing (NGS) technology, and the development of the "SHOREmap" method (Schneeberger et al., 2009). SHOREmap is an advanced version of the bulked segregant analysis, which is commonly used for the serial mapping of thousands of markers in many single lines. Instead, in SHOREmap, marker recognition, typing and mapping of genes are performed in a single simultaneous process of comparative sequencing of DNA pools.
In the NuGGET-Verbund project, a combination of several German plant breeders and leading academic research institutions, the SHOREmap method will be further developed for use in crops with complex genomes. The aim of the Nugget project is the implementation, improvement and use of the novel method for the rapid discovery of modified genes in populations of natural or engineered mutants using NGS technology. The availability of such a process should directly feed into the development of new varieties and products as well as enhance basic research on and with crops. To achieve this, the SHOREmap method will be adapted for use in a highly complex but agriculturally important crop such as maize (10 chromosomes, 2.3 GB, ~ 80% repetitive sequences).
First, in so-called "proof-of-concept" (POF) experiments, simple traits controlled by single genes variants such as flowering time will be analysed to adapt and to optimize the methodology. Later, the SHOREmap analysis will be used to identify and to characterise potentially useful mutations in a new maize EMS population.