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2. Mixed model approaches for the identification of QTLs within a maize hybrid breeding program.

Author

van Eeuwijk, F.A., Boer, M., Totir, L., Bink, M.C.A.M., Wright, D., Winkler, C., Podlich, D., Boldman, K., Baumgarten, R., Smalley, M., Arbelbide, M., ter Braak, C.J.F., Cooper, M., van Eeuwijk, F.A., Boer, M., Totir, L., Bink, M.C.A.M., Wright, D., Winkler, C., Podlich, D., Boldman, K., Baumgarten, R., Smalley, M., Arbelbide, M., ter Braak, C.J.F., and Cooper, M.

Abstract

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Published
2010

3. Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Author

Andy Baumgarten, Martin P. Boer, Mark E. Cooper, L. Radu Totir, Christopher R. Winkler, Deanne Wright, Matthew David Smalley, Keith Boldman, Dean Podlich, Fred A. van Eeuwijk, Martin Arbelbide, Cajo J. F. ter Braak, and Marco C. A. M. Bink

Subjects
Genetic Markers, Mixed model, Breeding program, Genetic Linkage, Restricted maximum likelihood, Computational biology, Biology, Quantitative trait locus, Zea mays, Identity by descent, Wiskundige en Statistische Methoden - Biometris, environmental covariables, symbols.namesake, Family-based QTL mapping, heterosis, Genetics, Leerstoel Maatschappelijke aspecten van biologische landbouw, Mathematical and Statistical Methods - Biometris, Selection (genetic algorithm), Original Paper, Models, Genetic, single-cross performance, Chromosome Mapping, pedigree, Bayes Theorem, Markov chain Monte Carlo, General Medicine, variance-components, PE&RC, Markov Chains, genomewide selection, PRI Biometris, plant-populations, marker data, quantitative trait loci, symbols, Hybridization, Genetic, Agronomy and Crop Science, linear unbiased prediction, Biotechnology
Abstract

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.

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