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Genome-wide selection by mixed model ridge regression and extensions based on geostatistical models
Torben Schulz-Streeck1, Hans-Peter Piepho
1Bioinformatics Unit, Institute for Crop Production and Grassland Research, Universität Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany. torben.schulz-streeck@uni-hohenheim.de.
BMC Proceedings
|April 13, 2010
Summary
Geostatistical mixed models offer a promising computational tool for genome-wide selection (GS). These models provide a viable alternative to traditional methods like ridge regression, achieving high correlations between estimated and true breeding values.
Area of Science:
- Quantitative genetics
- Statistical genomics
- Bioinformatics
Background:
- Genome-wide selection (GS) requires efficient computational tools.
- Mixed models are promising for GS applications.
- Geostatistical mixed models adapt spatial statistics concepts to genetic distance.
Purpose of the Study:
- To evaluate geostatistical mixed models for genome-wide selection.
- To investigate the impact of residual error and polygenic effect modeling in GS.
Main Methods:
- Application of various spatial mixed models to the QTL-MAS 2009 dataset.
- Detailed modeling of residual errors and polygenic effects.
- Comparison with ridge regression and pedigree-based variance component models.
Main Results:
- Geostatistical models demonstrated viability as alternatives to ridge regression for GS.
- Correlations between estimated and true breeding values ranged from 0.879 to 0.889.
- Modeling residual errors and using pedigree information did not significantly improve model fit in this specific dataset.
Conclusions:
- Geostatistical models are effective and practical tools for genome-wide selection.
- Further research into geostatistical models within mixed model frameworks is warranted for GS applications.
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