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This article is part of the supplement: Proceedings of the 12th European workshop on QTL mapping and marker assisted selection

Open Access Proceedings

A Bayesian QTL linkage analysis of the common dataset from the 12th QTLMAS workshop

Marco CAM Bink1* and Fred A van Eeuwijk12

Author Affiliations

1 Biometris, Wageningen University & Research centre, Bornsesteeg 47, 6708 PD, Wageningen, Netherlands

2 Centre for BioSystems Genomics, PO Box 98, 6700 AB Wageningen, Netherlands

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BMC Proceedings 2009, 3(Suppl 1):S4  doi:10.1186/1753-6561-3-S1-S4

Published: 23 February 2009

Abstract

Background

To compare the power of various QTL mapping methodologies, a dataset was simulated within the framework of 12th QTLMAS workshop. A total of 5865 diploid individuals was simulated, spanning seven generations, with known pedigree. Individuals were genotyped for 6000 SNPs across six chromosomes. We present an illustration of a Bayesian QTL linkage analysis, as implemented in the special purpose software FlexQTL. Most importantly, we treated the number of bi-allelic QTL as a random variable and used Bayes Factors to infer plausible QTL models. We investigated the power of our analysis in relation to the number of phenotyped individuals and SNPs.

Results

We report clear posterior evidence for 12 QTL that jointly explained 30% of the phenotypic variance, which was very close to the total of included simulation effects, when using all phenotypes and a set of 600 SNPs. Decreasing the number of phenotyped individuals from 4665 to 1665 and/or the number of SNPs in the analysis from 600 to 120 dramatically reduced the power to identify and locate QTL. Posterior estimates of genome-wide breeding values for a small set of individuals were given.

Conclusion

We presented a successful Bayesian linkage analysis of a simulated dataset with a pedigree spanning several generations. Our analysis identified all regions that contained QTL with effects explaining more than one percent of the phenotypic variance. We showed how the results of a Bayesian QTL mapping can be used in genomic prediction.