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High-throughput identification of genetic markers using representational oligonucleotide microarray analysis.

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons50404

Lange,  C.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

Mittermayr,  L.
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50135

Dohm,  J. C.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50203

Himmelbauer,  H.
Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Lange, C., Mittermayr, L., Dohm, J. C., Holtgräwe, D., Weisshaar, B., & Himmelbauer, H. (2010). High-throughput identification of genetic markers using representational oligonucleotide microarray analysis. Theoretical and Applied Genetics, 121(3), 549-565. doi:10.1007/s00122-010-1329-2.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-7B62-9
Abstract
We describe a novel approach for high-throughput development of genetic markers using representational oligonucleotide microarray analysis. We test the performance of the method in sugar beet (Beta vulgaris L.) as a model for crop plants with little sequence information available. Genomic representations of both parents of a mapping population were hybridized on microarrays containing in total 146,554 custom made oligonucleotides based on sugar beet bacterial artificial chromosome (BAC) end sequences and expressed sequence tags (ESTs). Oligonucleotides showing a signal with one parental line only, were selected as potential marker candidates and placed onto an array, designed for genotyping of 184 F(2) individuals from the mapping population. Utilizing known co-dominant anchor markers we obtained 511 new dominant markers (392 derived from BAC end sequences, and 119 from ESTs) distributed over all nine sugar beet linkage groups and calculated genetic maps. Further improvements for large-scale application of the approach are discussed and its feasibility for the cost-effective and flexible generation of genetic markers is presented.