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Relnsc N.,Institute For Genetik Und Biometrie | Duda J.,Landeskuratorium der Erzeugerringe fur Tierische Veredelung in Bayern e.V. LKV
Zuchtungskunde | Year: 2015

Besides well-known parameters like the contents of fat, protein and lactose the milk laboratories take measurements of the full infrared-absorption spectra of all milk samples. Those remain, however, unused for the measurement of further milk constituents or other purposes. The article first reviews some basic principles of infrared-spectrometry in order to better assess the future prospects of extracting valuable information from those spectra for the purposes of herd management. Then the research on the possibilities to measure various milk constituents in much more detail is discussed. In the last section the difficulties are highlighted which have yet to be overcome for complex traits like energy balance or pregnancy status, which are the current targets of research aiming at information for improved herd management. © Verlag Eugen Ulmer, Stuttgart. Source

Rudolf H.,Institute For Genetik Und Biometrie | Nuernberg G.,Institute For Genetik Und Biometrie | Koczan D.,University of Rostock | Vanselow J.,Institute For Genetik Und Biometrie | And 5 more authors.
BMC Genomics | Year: 2015

Background: Pooled samples are frequently used in experiments measuring gene expression. In this method, RNA from different individuals sharing the same experimental conditions and explanatory variables is blended and their concentrations are jointly measured. As a matter of principle, individuals are represented in equal shares in each pool. However, some degree of disproportionality may arise from the limits of technical precision. As a consequence a special kind of technical error occurs, which can be modelled by a respective variance component. Previously published theory - allowing for variable pool sizes - has been applied to four microarray gene expression data sets from different species in order to assess the practical relevance of this type of technical error in terms of significance and size of this variance component. Results: The number of transcripts with a significant variance component due to imperfect blending was found to be 4329 (23 %) in mouse data and 7093 (49 %) in honey bees, but only 6 in rats and none whatsoever in human data. These results correspond to a false discovery rate of 5 % in each data set. The number of transcripts found to be differentially expressed between treatments was always higher when the blending error variance was neglected. Simulations clearly indicated overly-optimistic (anti-conservative) test results in terms of false discovery rates whenever this source of variability was not represented in the model. Conclusions: Imperfect equality of shares when blending RNA from different individuals into joint pools of variable size is a source of technical variation with relevance for experimental design, practice at the laboratory bench and data analysis. Its potentially adverse effects, incorrect identification of differentially expressed transcripts and overly-optimistic significance tests, can be fully avoided, however, by the sound application of recently established theory and models for data analysis. © 2015 Rudolf et al. Source

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