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Interspecific differentiation values (G ST ) between two closely related oak species (Quercus petraea and Q. robur) were compiled across different studies with the aim to explore the distribution of differentiation at the genome level. The study was based on a total set of 389 markers (isozymes, AFLPs, SCARs, microsatellites, and SNPs) for which allelic frequencies were estimated in pairs of populations sampled throughout the sympatric distribution of the two species. The overall distribution of G ST values followed an L-shaped curve with most markers exhibiting low species differentiation (G ST Ͻ 0.01) and only a few loci reaching Ͼ10% levels. Twelve percent of the loci exhibited significant G ST deviations to neutral expectations, suggesting that selection contributed to species divergence. Coding regions expressed higher differentiation than noncoding regions. Among the 389 markers, 158 could be mapped on the 12 linkage groups of the existing Q. robur genetic map. Outlier loci with large G ST values were distributed over 9 linkage groups. One cluster of three outlier loci was found within 0.51 cM; but significant autocorrelation of G ST was observed at distances Ͻ2 cM. The size and distribution of genomic regions involved in species divergence are discussed in reference to hitchhiking effects and disruptive selection.

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PCR methods for the detection of genetically modified organisms (GMOs) were developed that can be used for screening purposes and for specific detection of glyphosate-tolerant soybean and insect-resistant maize in food. Primers were designed to amplify parts of the 35S promoter derived from Cauliflower Mosaic Virus, the NOS terminator derived from Agrobacterium tumefaciens and the antibiotic marker gene NPTII (neomycin-phosphotransferase II), to allow for general screening of foods. PCR/hybridization protocols were established for the detection of glyphosate-tolerant RoundUp Ready soybean and insect-resistant Bt-maize. Besides hybridization, confirmation of the results using restriction analysis was also possible. The described methods enabled a highly sensitive and specific detection of GMOs and thus provide a useful tool for routine analysis of raw and processed food products.