Bayesian mapping of genotype × expression interactions in quantitative and qualitative traits

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 effectspecific 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.

Mapping epistatic quantitative trait loci underlying endosperm traits using all markers on the entire genome in a random hybridization design

“…Third, is the number of markers that can be applied using the PML method limited? It is preferable to gather more samples or reduce the number of effects considered in the model (Zhang and Xu, 2005a;Hoti and Sillanpää, 2006). If the number of markers is large, however, the number of effects in the model is enormous-more than 40 000 in the simulated experiment with a large genome.…”

Hierarchical modeling of clinical and expression quantitative trait loci

“…The prior distribution of the marker data is defined in the same way as in Sillanpää and Arjas (1998) and Hoti and Sillanpää (2006). We assume that the genotype measurements are conditionally independent between individuals (given the parents), because all individuals are equally related:…”

“…Such data was called link data in Hoti and Sillanpää (2006) and is here considered to represent earlier eQTL findings from allele expression studies, genetical genomics experiments or known pathways that are to be validated. Both cis-and trans-acting pairs can be included but the validation data set cannot be the same set where the original findings were made (to avoid selection bias).…”

“…The same kind of data (markers and expression jointly) can be used to explain variation in quantitative traits, which is called clinical quantitative trait locus (cQTL) analysis (Hoti and Sillanpää, 2006;Bhattacharjee and Sillanpää, 2008). It is evident in this setup that the expression measurements of the joint data can provide additional information for explaining and predicting the phenotype (West et al, 2006).…”

“…Thus, we present method to estimate parameters underlying the frequency distribution of gene expression among the prespecified set of marker gene pairs. Information from previous eQTL and allelic expression studies as well as known pathways can be utilized in the forming of such input data (marker gene pairs; Hoti and Sillanpää, 2006). The suggested method provides posterior estimates and predictions for parameters (for example, missing data) including: (1) the proportions and occupancy probabilities of the eQTLs (the markers regulating the expression) and the cQTLs (the marker or transcript variation explaining the phenotype) as well as their eQTL and cQTL effects, (2) the predicted values of gene expression based on the genotypes at a regulatory locus and (3) the genotype predictions based on the expression values and the genotypes at linked (adjacent) loci.…”