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This article is part of the supplement: Proceedings of the 13th European workshop on QTL mapping and marker assisted selection

Open Access Proceedings

Genome-wide selection by mixed model ridge regression and extensions based on geostatistical models

Torben Schulz-Streeck and Hans-Peter Piepho*

Author Affiliations

Bioinformatics Unit, Institute for Crop Production and Grassland Research, Universität Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany

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BMC Proceedings 2010, 4(Suppl 1):S8  doi:10.1186/1753-6561-4-S1-S8

Published: 31 March 2010

Abstract

Background

The success of genome-wide selection (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.

Methods

We consider various spatial 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.

Results

It is shown that geostatistical models are viable alternatives to ridge regression, 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.

Conclusions

We conclude that geostatistical models deserve further study as a tool to GS that is easily implemented in a mixed model package.