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This article is part of the supplement: Proceedings of the 14th European workshop on QTL mapping and marker assisted selection (QTL-MAS)

Open Access Proceedings

Hierarchical likelihood opens a new way of estimating genetic values using genome-wide dense marker maps

Xia Shen12*, Lars Rönnegård23 and Örjan Carlborg13

Author Affiliations

1 The Linnaeus Centre for Bioinformatics, Uppsala University, Uppsala, Sweden

2 Statistics Unit, Dalarna University, Borlänge, Sweden

3 Department of Animal Breeding & Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden

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BMC Proceedings 2011, 5(Suppl 3):S14  doi:10.1186/1753-6561-5-S3-S14

Published: 27 May 2011

Abstract

Background

Genome-wide dense markers have been used to detect genes and estimate relative genetic values. Among many methods, Bayesian techniques have been widely used and shown to be powerful in genome-wide breeding value estimation and association studies. However, computation is known to be intensive under the Bayesian framework, and specifying a prior distribution for each parameter is always required for Bayesian computation. We propose the use of hierarchical likelihood to solve such problems.

Results

Using double hierarchical generalized linear models, we analyzed the simulated dataset provided by the QTLMAS 2010 workshop. Marker-specific variances estimated by double hierarchical generalized linear models identified the QTL with large effects for both the quantitative and binary traits. The QTL positions were detected with very high accuracy. For young individuals without phenotypic records, the true and estimated breeding values had Pearson correlation of 0.60 for the quantitative trait and 0.72 for the binary trait, where the quantitative trait had a more complicated genetic architecture involving imprinting and epistatic QTL.

Conclusions

Hierarchical likelihood enables estimation of marker-specific variances under the likelihoodist framework. Double hierarchical generalized linear models are powerful in localizing major QTL and computationally fast.