Aus dem Institut für Kulturpflanzenwissenschaften

Genome-wide selection (GS) involves estimating breeding values using molecular markers spanning the entire genome. The success of GS approaches will depend crucially on the availability of efficient and easy-to-use computational tools. Therefore, approaches that can be implemented using mixed models hold particular promise and deserve detailed study. A particular class of mixed models suitable for GS is given by geostatistical mixed models, when genetic distance is treated analogously to spatial distance in geostatistics. We consider various geostatistical mixed models for use in GS. The analyses presented for the QTL-MAS 2009 dataset pay particular attention to the modelling of residual errors as well as of polygenetic effects. It is shown that geostatistical models are viable alternatives to ridge regression best linear unbiased prediction, one of the common approaches to GS. Correlations between genome-wide estimated breeding values and true breeding values were between 0.879 and 0.889. In the example considered, we did not find a large effect of the residual error variance modelling, largely because error variances were very small. A variance components model reflecting the pedigree of the crosses did not provide an improved fit. Therefore geostatistical models deserve further study as a tool to GS that is easily implemented in a mixed model package. 18 3. Schulz-Streeck et al., 2011. BMC Proceedings 5(Suppl 3):S12 3. Pre-selection of markers for genomic selection Torben Schulz-Streeck, Joseph O Ogutu, Hans-Peter Piepho Bioinformatics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany BMC Proceedings 2011, 5(Suppl 3):S12. The original publication is available at http://www.biomedcentral.com/bmcproc/