¹Department of Mathematics and Statistics, Rolf Nevanlinna Institute, University of Helsinki, FIN-00014 Helsinki, Finland

Correspondence: MJ Sillanpää, Department of Mathematics and Statistics, Rolf Nevanlinna Institute, University of Helsinki, PO Box 68, FIN-00014 Helsinki, Finland. E-mail: mjs@rolf.helsinki.fi

Received 9 September 2005; Accepted 28 February 2006; Published online 3 May 2006.

Abstract

A novel Bayesian gene mapping method, which can simultaneously utilize both molecular marker and gene expression data, is introduced. The approach enables a quantitative or qualitative phenotype to be expressed as a linear combination of the marker genotypes, gene expression levels, and possible genotype gene expression interactions. The interaction data, given as marker–gene pairs, contains possible in cis and in trans effects obtained from earlier allelic expression studies, genetical genomics studies, biological hypotheses, or known pathways. The method is presented for an inbred line cross design and can be easily generalized to handle other types of populations and designs. The model selection is based on the use of effect-specific variance components combined with Jeffreys' noninformative prior – the method operates by adaptively shrinking marker, expression, and interaction effects toward zero so that non-negligible effects are expected to occur only at very few positions. The estimation of the model parameters and the handling of missing genotype or expression data is performed via Markov chain Monte Carlo sampling. The potential of the method including heritability estimation is presented using simulated examples and novel summary statistics. The method is also applied to a real yeast data set with known pathways.